Author: "Yates P" - Searchworks@Jio Institute Digital Library Search Results

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17,168 results on '"Yates P"'

1. Data-driven model validation for neutrino-nucleus cross section measurements

Author: MicroBooNE collaboration, Abratenko, P., Alterkait, O., Aldana, D. Andrade, Arellano, L., Asaadi, J., Ashkenazi, A., Balasubramanian, S., Baller, B., Barnard, A., Barr, G., Barrow, D., Barrow, J., Basque, V., Bateman, J., Rodrigues, O. Benevides, Berkman, S., Bhanderi, A., Bhat, A., Bhattacharya, M., Bishai, M., Blake, A., Bogart, B., Bolton, T., Brunetti, M. B., Camilleri, L., Cao, Y., Caratelli, D., Cavanna, F., Cerati, G., Chappell, A., Chen, Y., Conrad, J. M., Convery, M., Cooper-Troendle, L., Crespo-Anadon, J. I., Cross, R., Del Tutto, M., Dennis, S. R., Detje, P., Diurba, R., Djurcic, Z., Duffy, K., Dytman, S., Eberly, B., Englezos, P., Ereditato, A., Evans, J. J., Fang, C., Fleming, B. T., Foreman, W., Franco, D., Furmanski, A. P., Gao, F., Garcia-Gamez, D., Gardiner, S., Ge, G., Gollapinni, S., Gramellini, E., Green, P., Greenlee, H., Gu, L., Gu, W., Guenette, R., Guzowski, P., Hagaman, L., Handley, M. D., Hen, O., Hilgenberg, C., Horton-Smith, G. A., Imani, Z., Irwin, B., Ismail, M. S., James, C., Ji, X., Jo, J. H., Johnson, R. A., Jwa, Y. J., Kalra, D., Karagiorgi, G., Ketchum, W., Kirby, M., Kobilarcik, T., Lane, N., Li, J. -Y., Li, Y., Lin, K., Littlejohn, B. R., Liu, L., Louis, W. C., Luo, X., Mahmud, T., Mariani, C., Marsden, D., Marshall, J., Martinez, N., Caicedo, D. A. Martinez, Martynenko, S., Mastbaum, A., Mawby, I., McConkey, N., Meddage, V., Mellet, L., Mendez, J., Micallef, J., Miller, K., Mistry, K., Mohayai, T., Mogan, A., Mooney, M., Moor, A. F., Moore, C. D., Lepin, L. Mora, Moudgalya, M. M., Babu, S. Mulleria, Naples, D., Navrer-Agasson, A., Nayak, N., Nebot-Guinot, M., Nguyen, C., Nowak, J., Oza, N., Palamara, O., Pallat, N., Paolone, V., Papadopoulou, A., Papavassiliou, V., Parkinson, H., Pate, S. F., Patel, N., Pavlovic, Z., Piasetzky, E., Pletcher, K., Pophale, I., Qian, X., Raaf, J. L., Radeka, V., Rafique, A., Reggiani-Guzzo, M., Ren, L., Rochester, L., Rondon, J. Rodriguez, Rosenberg, M., Ross-Lonergan, M., Safa, I., Schmitz, D. W., Schukraft, A., Seligman, W., Shaevitz, M. H., Sharankova, R., Shi, J., Snider, E. L., Soderberg, M., Soldner-Rembold, S., Spitz, J., Stancari, M., John, J. St., Strauss, T., Szelc, A. M., Taniuchi, N., Terao, K., Thorpe, C., Torbunov, D., Totani, D., Toups, M., Trettin, A., Tsai, Y. -T., Tyler, J., Uchida, M. A., Usher, T., Viren, B., Wang, J., Weber, M., Wei, H., White, A. J., Wolbers, S., Wongjirad, T., Wospakrik, M., Wresilo, K., Wu, W., Yandel, E., Yang, T., Yates, L. E., Yu, H. W., Zeller, G. P., Zennamo, J., and Zhang, C.
Subjects: High Energy Physics - Experiment
Abstract: Neutrino-nucleus cross section measurements are needed to improve interaction modeling to meet the precision needs of neutrino experiments in efforts to measure oscillation parameters and search for physics beyond the Standard Model. We review the difficulties associated with modeling neutrino-nucleus interactions that lead to a dependence on event generators in oscillation analyses and cross section measurements alike. We then describe data-driven model validation techniques intended to address this model dependence. The method relies on utilizing various goodness-of-fit tests and the correlations between different observables and channels to probe the model for defects in the phase space relevant for the desired analysis. These techniques shed light on relevant mis-modeling, allowing it to be detected before it begins to bias the cross section results. We compare more commonly used model validation methods which directly validate the model against alternative ones to these data-driven techniques and show their efficacy with fake data studies. These studies demonstrate that employing data-driven model validation in cross section measurements represents a reliable strategy to produce robust results that will stimulate the desired improvements to interaction modeling.
Published: 2024

2. On the Hardness of the $L_1-L_2$ Regularization Problem

Author: Ouyang, Yuyuan and Yates, Kyle
Subjects: Mathematics - Optimization and Control, 68Q25, 94A12, 90C30
Abstract: The sparse linear reconstruction problem is a core problem in signal processing which aims to recover sparse solutions to linear systems. The original problem regularized by the total number of nonzero components (also know as $L_0$ regularization) is well-known to be NP-hard. The relaxation of the $L_0$ regularization by using the $L_1$ norm offers a convex reformulation, but is only exact under contain conditions (e.g., restricted isometry property) which might be NP-hard to verify. To overcome the computational hardness of the $L_0$ regularization problem while providing tighter results than the $L_1$ relaxation, several alternate optimization problems have been proposed to find sparse solutions. One such problem is the $L_1-L_2$ minimization problem, which is to minimize the difference of the $L_1$ and $L_2$ norms subject to linear constraints. This paper proves that solving the $L_1-L_2$ minimization problem is NP-hard. Specifically, we prove that it is NP-hard to minimize the $L_1-L_2$ regularization function subject to linear constraints. Moreover, it is also NP-hard to solve the unconstrained formulation that minimizes the sum of a least squares term and the $L_1-L_2$ regularization function. Furthermore, restricting the feasible set to a smaller one by adding nonnegative constraints does not change the NP-hardness nature of the problems., Comment: 15 pages, 0 figures
Published: 2024

3. Magritte, a modern software library for spectral line radiative transfer

Author: Ceulemans, Thomas, De Ceuster, Frederik, Decin, Leen, and Yates, Jeremy
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: Spectral line observations are an indispensable tool to remotely probe the physical and chemical conditions throughout the universe. Modelling their behaviour is a computational challenge that requires dedicated software. In this paper, we present the first long-term stable release of Magritte, an open-source software library for line radiative transfer. First, we establish its necessity with two applications. Then, we introduce the overall design strategy and the application/programmer interface (API). Finally, we present three key improvements over previous versions: (1) an improved re-meshing algorithm to efficiently coarsen the spatial discretisation of a model; (2) a variation on Ng-acceleration, a popular acceleration-of-convergence method for non-LTE line transfer; and, (3) a semi-analytic approximation for line optical depths in the presence of large velocity gradients., Comment: 12 pages, 9 figures
Published: 2024

4. A prescriptive theory for brain-like inference

Author: Vafaii, Hadi, Galor, Dekel, and Yates, Jacob L.
Subjects: Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition
Abstract: The Evidence Lower Bound (ELBO) is a widely used objective for training deep generative models, such as Variational Autoencoders (VAEs). In the neuroscience literature, an identical objective is known as the variational free energy, hinting at a potential unified framework for brain function and machine learning. Despite its utility in interpreting generative models, including diffusion models, ELBO maximization is often seen as too broad to offer prescriptive guidance for specific architectures in neuroscience or machine learning. In this work, we show that maximizing ELBO under Poisson assumptions for general sequence data leads to a spiking neural network that performs Bayesian posterior inference through its membrane potential dynamics. The resulting model, the iterative Poisson VAE (iP-VAE), has a closer connection to biological neurons than previous brain-inspired predictive coding models based on Gaussian assumptions. Compared to amortized and iterative VAEs, iP-VAElearns sparser representations and exhibits superior generalization to out-of-distribution samples. These findings suggest that optimizing ELBO, combined with Poisson assumptions, provides a solid foundation for developing prescriptive theories in NeuroAI.
Published: 2024

5. Demonstration of new MeV-scale capabilities in large neutrino LArTPCs using ambient radiogenic and cosmogenic activity in MicroBooNE

Author: MicroBooNE collaboration, Abratenko, P., Alterkait, O., Aldana, D. Andrade, Arellano, L., Asaadi, J., Ashkenazi, A., Balasubramanian, S., Baller, B., Barnard, A., Barr, G., Barrow, D., Barrow, J., Basque, V., Bateman, J., Rodrigues, O. Benevides, Berkman, S., Bhanderi, A., Bhat, A., Bhattacharya, M., Bishai, M., Blake, A., Bogart, B., Bolton, T., Brunetti, M. B., Camilleri, L., Cao, Y., Caratelli, D., Cavanna, F., Cerati, G., Chappell, A., Chen, Y., Conrad, J. M., Convery, M., Cooper-Troendle, L., Crespo-Anadon, J. I., Cross, R., Del Tutto, M., Dennis, S. R., Detje, P., Diurba, R., Djurcic, Z., Duffy, K., Dytman, S., Eberly, B., Englezos, P., Ereditato, A., Evans, J. J., Fang, C., Fleming, B. T., Foreman, W., Franco, D., Furmanski, A. P., Gao, F., Garcia-Gamez, D., Gardiner, S., Ge, G., Gollapinni, S., Gramellini, E., Green, P., Greenlee, H., Gu, L., Gu, W., Guenette, R., Guzowski, P., Hagaman, L., Handley, M. D., Hen, O., Hilgenberg, C., Horton-Smith, G. A., Imani, Z., Irwin, B., Ismail, M. S., James, C., Ji, X., Jo, J. H., Johnson, R. A., Jwa, Y. J., Kalra, D., Karagiorgi, G., Ketchum, W., Kirby, M., Kobilarcik, T., Lane, N., Li, J. -Y., Li, Y., Lin, K., Littlejohn, B. R., Liu, L., Louis, W. C., Luo, X., Mahmud, T., Mariani, C., Marsden, D., Marshall, J., Martinez, N., Caicedo, D. A. Martinez, Martynenko, S., Mastbaum, A., Mawby, I., McConkey, N., Meddage, V., Mellet, L., Mendez, J., Micallef, J., Miller, K., Mistry, K., Mohayai, T., Mogan, A., Mooney, M., Moor, A. F., Moore, C. D., Lepin, L. Mora, Moudgalya, M. M., Babu, S. Mulleria, Naples, D., Navrer-Agasson, A., Nayak, N., Nebot-Guinot, M., Nguyen, C., Nowak, J., Oza, N., Palamara, O., Pallat, N., Paolone, V., Papadopoulou, A., Papavassiliou, V., Parkinson, H., Pate, S. F., Patel, N., Pavlovic, Z., Piasetzky, E., Pletcher, K., Pophale, I., Qian, X., Raaf, J. L., Radeka, V., Rafique, A., Reggiani-Guzzo, M., Ren, L., Rochester, L., Rondon, J. Rodriguez, Rosenberg, M., Ross-Lonergan, M., Safa, I., Schmitz, D. W., Schukraft, A., Seligman, W., Shaevitz, M. H., Sharankova, R., Shi, J., Snider, E. L., Soderberg, M., Soldner-Rembold, S., Spitz, J., Stancari, M., John, J. St., Strauss, T., Szelc, A. M., Taniuchi, N., Terao, K., Thorpe, C., Torbunov, D., Totani, D., Toups, M., Trettin, A., Tsai, Y. -T., Tyler, J., Uchida, M. A., Usher, T., Viren, B., Wang, J., Weber, M., Wei, H., White, A. J., Wolbers, S., Wongjirad, T., Wospakrik, M., Wresilo, K., Wu, W., Yandel, E., Yang, T., Yates, L. E., Yu, H. W., Zeller, G. P., Zennamo, J., and Zhang, C.
Subjects: High Energy Physics - Experiment
Abstract: Large neutrino liquid argon time projection chamber (LArTPC) experiments can broaden their physics reach by reconstructing and interpreting MeV-scale energy depositions, or blips, present in their data. We demonstrate new calorimetric and particle discrimination capabilities at the MeV energy scale using reconstructed blips in data from the MicroBooNE LArTPC at Fermilab. We observe a concentration of low energy ($<$3 MeV) blips around fiberglass mechanical support struts along the TPC edges with energy spectrum features consistent with the Compton edge of 2.614 MeV $^{208}$Tl decay $\gamma$ rays. These features are used to verify proper calibration of electron energy scales in MicroBooNE's data to few percent precision and to measure the specific activity of $^{208}$Tl in the fiberglass composing these struts, $(11.7 \pm 0.2 ~\text{(stat)} \pm 2.8~\text{(syst)})~\text{Bq/kg}$. Cosmogenically-produced blips above 3 MeV in reconstructed energy are used to showcase the ability of large LArTPCs to distinguish between low-energy proton and electron energy depositions. An enriched sample of low-energy protons selected using this new particle discrimination technique is found to be smaller in data than in dedicated CORSIKA cosmic ray simulations, suggesting either incorrect CORSIKA modeling of incident cosmic fluxes or particle transport modeling issues in Geant4., Comment: 19 pages, 15 figures total including the supplementary material section, 1 table. CC BY license
Published: 2024

6. Contrasting Attitudes Towards Current and Future AI Applications for Computerised Interpretation of ECG: A Clinical Stakeholder Interview Study

Author: Hughes-Noehrer, Lukas, Channer, Leda, Strain, Gabriel, Yates, Gregory, Body, Richard, and Jay, Caroline
Subjects: Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract: Objectives: To investigate clinicians' attitudes towards current automated interpretation of ECG and novel AI technologies and their perception of computer-assisted interpretation. Materials and Methods: We conducted a series of interviews with clinicians in the UK. Our study: (i) explores the potential for AI, specifically future 'human-like' computing approaches, to facilitate ECG interpretation and support clinical decision making, and (ii) elicits their opinions about the importance of explainability and trustworthiness of AI algorithms. Results: We performed inductive thematic analysis on interview transcriptions from 23 clinicians and identified the following themes: (i) a lack of trust in current systems, (ii) positive attitudes towards future AI applications and requirements for these, (iii) the relationship between the accuracy and explainability of algorithms, and (iv) opinions on education, possible deskilling, and the impact of AI on clinical competencies. Discussion: Clinicians do not trust current computerised methods, but welcome future 'AI' technologies. Where clinicians trust future AI interpretation to be accurate, they are less concerned that it is explainable. They also preferred ECG interpretation that demonstrated the results of the algorithm visually. Whilst clinicians do not fear job losses, they are concerned about deskilling and the need to educate the workforce to use AI responsibly. Conclusion: Clinicians are positive about the future application of AI in clinical decision-making. Accuracy is a key factor of uptake and visualisations are preferred over current computerised methods. This is viewed as a potential means of training and upskilling, in contrast to the deskilling that automation might be perceived to bring.
Published: 2024

7. DyVo: Dynamic Vocabularies for Learned Sparse Retrieval with Entities

Author: Nguyen, Thong, Chatterjee, Shubham, MacAvaney, Sean, Mackie, Iain, Dalton, Jeff, and Yates, Andrew
Subjects: Computer Science - Information Retrieval
Abstract: Learned Sparse Retrieval (LSR) models use vocabularies from pre-trained transformers, which often split entities into nonsensical fragments. Splitting entities can reduce retrieval accuracy and limits the model's ability to incorporate up-to-date world knowledge not included in the training data. In this work, we enhance the LSR vocabulary with Wikipedia concepts and entities, enabling the model to resolve ambiguities more effectively and stay current with evolving knowledge. Central to our approach is a Dynamic Vocabulary (DyVo) head, which leverages existing entity embeddings and an entity retrieval component that identifies entities relevant to a query or document. We use the DyVo head to generate entity weights, which are then merged with word piece weights to create joint representations for efficient indexing and retrieval using an inverted index. In experiments across three entity-rich document ranking datasets, the resulting DyVo model substantially outperforms state-of-the-art baselines., Comment: https://github.com/thongnt99/DyVo
Published: 2024

8. Table Question Answering for Low-resourced Indic Languages

Author: Pal, Vaishali, Kanoulas, Evangelos, Yates, Andrew, and de Rijke, Maarten
Subjects: Computer Science - Computation and Language
Abstract: TableQA is the task of answering questions over tables of structured information, returning individual cells or tables as output. TableQA research has focused primarily on high-resource languages, leaving medium- and low-resource languages with little progress due to scarcity of annotated data and neural models. We address this gap by introducing a fully automatic large-scale tableQA data generation process for low-resource languages with limited budget. We incorporate our data generation method on two Indic languages, Bengali and Hindi, which have no tableQA datasets or models. TableQA models trained on our large-scale datasets outperform state-of-the-art LLMs. We further study the trained models on different aspects, including mathematical reasoning capabilities and zero-shot cross-lingual transfer. Our work is the first on low-resource tableQA focusing on scalable data generation and evaluation procedures. Our proposed data generation method can be applied to any low-resource language with a web presence. We release datasets, models, and code (https://github.com/kolk/Low-Resource-TableQA-Indic-languages)., Comment: Accepted at EMNLP,2024
Published: 2024

9. Corpus-informed Retrieval Augmented Generation of Clarifying Questions

Author: Krasakis, Antonios Minas, Yates, Andrew, and Kanoulas, Evangelos
Subjects: Computer Science - Information Retrieval
Abstract: This study aims to develop models that generate corpus informed clarifying questions for web search, in a way that ensures the questions align with the available information in the retrieval corpus. We demonstrate the effectiveness of Retrieval Augmented Language Models (RAG) in this process, emphasising their ability to (i) jointly model the user query and retrieval corpus to pinpoint the uncertainty and ask for clarifications end-to-end and (ii) model more evidence documents, which can be used towards increasing the breadth of the questions asked. However, we observe that in current datasets search intents are largely unsupported by the corpus, which is problematic both for training and evaluation. This causes question generation models to ``hallucinate'', ie. suggest intents that are not in the corpus, which can have detrimental effects in performance. To address this, we propose dataset augmentation methods that align the ground truth clarifications with the retrieval corpus. Additionally, we explore techniques to enhance the relevance of the evidence pool during inference, but find that identifying ground truth intents within the corpus remains challenging. Our analysis suggests that this challenge is partly due to the bias of current datasets towards clarification taxonomies and calls for data that can support generating corpus-informed clarifications.
Published: 2024

10. The hypothetical track-length fitting algorithm for energy measurement in liquid argon TPCs

Author: DUNE Collaboration, Abud, A. Abed, Abi, B., Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adamowski, M., Adams, D., Adinolfi, M., Adriano, C., Aduszkiewicz, A., Aguilar, J., Akbar, F., Alex, N. S., Allison, K., Monsalve, S. Alonso, Alrashed, M., Alton, A., Alvarez, R., Alves, T., Amar, H., Amedo, P., Anderson, J., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Ankowski, A., Antic, D., Antoniassi, M., Antonova, M., Antoshkin, A., Aranda-Fernandez, A., Arellano, L., Diaz, E. Arrieta, Arroyave, M. A., Asaadi, J., Ashkenazi, A., Asner, D., Asquith, L., Atkin, E., Auguste, D., Aurisano, A., Aushev, V., Autiero, D., Azam, M. B., Azfar, F., Back, A., Back, H., Back, J. J., Bagaturia, I., Bagby, L., Balashov, N., Balasubramanian, S., Baldi, P., Baldini, W., Baldonedo, J., Baller, B., Bambah, B., Banerjee, R., Barao, F., Barbu, D., Barenboim, G., Alzás, P. Barham, Barker, G. J., Barkhouse, W., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, D., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Batchelor, C., Bathe-Peters, L., Battat, J. B. R., Battisti, F., Bay, F., Bazetto, M. C. Q., Alba, J. L. L. Bazo, Beacom, J. F., Bechetoille, E., Behera, B., Belchior, E., Bell, G., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Benekos, N., Montiel, C. Benitez, Benjamin, D., Neves, F. Bento, Berger, J., Berkman, S., Bernal, J., Bernardini, P., Bersani, A., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bevan, A., Bezawada, Y., Bezerra, A. T., Bezerra, T. J., Bhat, A., Bhatnagar, V., Bhatt, J., Bhattacharjee, M., Bhattacharya, M., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biery, K., Bilki, B., Bishai, M., Bitadze, A., Blake, A., Blaszczyk, F. D., Blazey, G. C., Blucher, E., Bodek, A., Bogenschuetz, J., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bonilla-Diaz, C., Bonini, F., Booth, A., Boran, F., Bordoni, S., Merlo, R. Borges, Borkum, A., Bostan, N., Bouet, R., Boza, J., Bracinik, J., Brahma, B., Brailsford, D., Bramati, F., Branca, A., Brandt, A., Bremer, J., Brew, C., Brice, S. J., Brio, V., Brizzolari, C., Bromberg, C., Brooke, J., Bross, A., Brunetti, G., Brunetti, M., Buchanan, N., Budd, H., Buergi, J., Bundock, A., Burgardt, D., Butchart, S., V., G. Caceres, Cagnoli, I., Cai, T., Calabrese, R., Calcutt, J., Calivers, L., Calvo, E., Caminata, A., Camino, A. F., Campanelli, W., Campani, A., Benitez, A. Campos, Canci, N., Capó, J., Caracas, I., Caratelli, D., Carber, D., Carceller, J. M., Carini, G., Carlus, B., Carneiro, M. F., Carniti, P., Terrazas, I. Caro, Carranza, H., Carrara, N., Carroll, L., Carroll, T., Carter, A., Casarejos, E., Casazza, D., Forero, J. F. Castaño, Castaño, F. A., Castillo, A., Castromonte, C., Catano-Mur, E., Cattadori, C., Cavalier, F., Cavanna, F., Centro, S., Cerati, G., Cerna, C., Cervelli, A., Villanueva, A. Cervera, Chakraborty, K., Chalifour, M., Chappell, A., Charitonidis, N., Chatterjee, A., Chen, H., Chen, M., Chen, W. C., Chen, Y., Chen-Wishart, Z., Cherdack, D., Chi, C., Chiapponi, F., Chirco, R., Chitirasreemadam, N., Cho, K., Choate, S., Choi, G., Chokheli, D., Chong, P. S., Chowdhury, B., Christian, D., Chukanov, A., Chung, M., Church, E., Cicala, M. F., Cicerchia, M., Cicero, V., Ciolini, R., Clarke, P., Cline, G., Coan, T. E., Cocco, A. G., Coelho, J. A. B., Cohen, A., Collazo, J., Collot, J., Conley, E., Conrad, J. M., Convery, M., Copello, S., Cova, P., Cox, C., Cremaldi, L., Cremonesi, L., Crespo-Anadón, J. I., Crisler, M., Cristaldo, E., Crnkovic, J., Crone, G., Cross, R., Cudd, A., Cuesta, C., Cui, Y., Curciarello, F., Cussans, D., Dai, J., Dalager, O., Dallavalle, R., Dallaway, W., D'Amico, R., da Motta, H., Dar, Z. A., Darby, R., Peres, L. Da Silva, David, Q., Davies, G. S., Davini, S., Dawson, J., De Aguiar, R., De Almeida, P., Debbins, P., De Bonis, I., Decowski, M. P., de Gouvêa, A., De Holanda, P. C., Astiz, I. L. De Icaza, De Jong, P., Sanchez, P. Del Amo, De la Torre, A., De Lauretis, G., Delbart, A., Delepine, D., Delgado, M., Dell'Acqua, A., Monache, G. Delle, Delmonte, N., De Lurgio, P., Demario, R., De Matteis, G., Neto, J. R. T. de Mello, DeMuth, D. M., Dennis, S., Densham, C., Denton, P., Deptuch, G. W., De Roeck, A., De Romeri, V., Detje, J. P., Devine, J., Dharmapalan, R., Dias, M., Diaz, A., Díaz, J. S., Díaz, F., Di Capua, F., Di Domenico, A., Di Domizio, S., Di Falco, S., Di Giulio, L., Ding, P., Di Noto, L., Diociaiuti, E., Distefano, C., Diurba, R., Diwan, M., Djurcic, Z., Doering, D., Dolan, S., Dolek, F., Dolinski, M. J., Domenici, D., Domine, L., Donati, S., Donon, Y., Doran, S., Douglas, D., Doyle, T. A., Dragone, A., Drielsma, F., Duarte, L., Duchesneau, D., Duffy, K., Dugas, K., Dunne, P., Dutta, B., Duyang, H., Dwyer, D. A., Dyshkant, A. S., Dytman, S., Eads, M., Earle, A., Edayath, S., Edmunds, D., Eisch, J., Englezos, P., Ereditato, A., Erjavec, T., Escobar, C. O., Evans, J. J., Ewart, E., Ezeribe, A. C., Fahey, K., Fajt, L., Falcone, A., Fani', M., Farnese, C., Farrell, S., Farzan, Y., Fedoseev, D., Felix, J., Feng, Y., Fernandez-Martinez, E., Ferry, G., Fialova, E., Fields, L., Filip, P., Filkins, A., Filthaut, F., Fine, R., Fiorillo, G., Fiorini, M., Fogarty, S., Foreman, W., Fowler, J., Franc, J., Francis, K., Franco, D., Franklin, J., Freeman, J., Fried, J., Friedland, A., Fuess, S., Furic, I. K., Furman, K., Furmanski, A. P., Gaba, R., Gabrielli, A., Gago, A. M., Galizzi, F., Gallagher, H., Gallice, N., Galymov, V., Gamberini, E., Gamble, T., Ganacim, F., Gandhi, R., Ganguly, S., Gao, F., Gao, S., Garcia-Gamez, D., García-Peris, M. Á., Gardim, F., Gardiner, S., Gastler, D., Gauch, A., Gauvreau, J., Gauzzi, P., Gazzana, S., Ge, G., Geffroy, N., Gelli, B., Gent, S., Gerlach, L., Ghorbani-Moghaddam, Z., Giammaria, T., Gibin, D., Gil-Botella, I., Gilligan, S., Gioiosa, A., Giovannella, S., Girerd, C., Giri, A. K., Giugliano, C., Giusti, V., Gnani, D., Gogota, O., Gollapinni, S., Gollwitzer, K., Gomes, R. A., Bermeo, L. V. Gomez, Fajardo, L. S. Gomez, Gonnella, F., Gonzalez-Diaz, D., Gonzalez-Lopez, M., Goodman, M. C., Goswami, S., Gotti, C., Goudeau, J., Goudzovski, E., Grace, C., Gramellini, E., Gran, R., Granados, E., Granger, P., Grant, C., Gratieri, D. R., Grauso, G., Green, P., Greenberg, S., Greer, J., Griffith, W. C., Groetschla, F. T., Grzelak, K., Gu, L., Gu, W., Guarino, V., Guarise, M., Guenette, R., Guerzoni, M., Guffanti, D., Guglielmi, A., Guo, B., Guo, F. Y., Gupta, A., Gupta, V., Gurung, G., Gutierrez, D., Guzowski, P., Guzzo, M. 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Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract: This paper introduces the hypothetical track-length fitting algorithm, a novel method for measuring the kinetic energies of ionizing particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy loss as a function of the energy, including models of electron recombination and detector response. The algorithm can be used to measure the energies of particles that interact before they stop, such as charged pions that are absorbed by argon nuclei. The algorithm's energy measurement resolutions and fractional biases are presented as functions of particle kinetic energy and number of track hits using samples of stopping secondary charged pions in data collected by the ProtoDUNE-SP detector, and also in a detailed simulation. Additional studies describe impact of the dE/dx model on energy measurement performance. The method described in this paper to characterize the energy measurement performance can be repeated in any LArTPC experiment using stopping secondary charged pions.
Published: 2024

11. Using $^{26}$Al to detect ongoing self-enrichment in young massive star clusters

Author: Nowak, Katarzyna, Krause, Martin G. H., Siegert, Thomas, Forbrich, Jan, Yates, Robert M., Ramírez-Galeano, Laura, Charbonnel, Corinne, and Gieles, Mark
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: Self-enrichment is one of the leading explanations for chemical anomalies in globular clusters. In this scenario, various candidate polluter stars have been proposed to eject gas with altered chemical composition during the self-enrichment process. Most of the proposed polluters will also eject radioactive $^{26}$Al into the surroundings. Hence, any detection of $^{26}$Al in young massive star clusters (YMCs) would support the self-enrichment scenario if YMCs were indeed the progenitors of globular clusters. Observations of gamma-ray data from COMPTEL and INTEGRAL, as well as detections of $^{26}$AlF molecules by the Atacama Large Millimeter-submillimeter Array (ALMA), indicate the maturing of $^{26}$Al detection methods. Detection possibilities will be enhanced in the short- to mid-term by the upcoming launch of the Compton Spectrometer and Imager (COSI). The Square Kilometer Array (SKA) could in principle also detect radio recombination lines of the positronium formed from the decay products of $^{26}$Al. Here, we show for a sample of YMCs in the nearby Universe, where self-enrichment could plausibly take place. For some nearby galaxies, this could enhance $^{26}$Al by an order of one magnitude. Detecting $^{26}$AlF with ALMA appears feasible for many candidate self-enrichment clusters, although significant challenges remain with other detection methods. The Large Magellanic Cloud, with its YMC R136, stands out as the most promising candidate. Detecting a 1.8~MeV radioactive decay line of $^{26}$Al here would require at least 15 months of targeted observation with COSI, assuming ongoing self-enrichment in R136., Comment: 13 pages, 5 figures
Published: 2024

12. Radio Signatures of Star-Planet Interactions, Exoplanets, and Space Weather

Author: Callingham, J. R., Pope, B. J. S., Kavanagh, R. D., Bellotti, S., Daley-Yates, S., Damasso, M., Grießmeier, J. -M., Güdel, M., Günther, M., Kao, M. M., Klein, B., Mahadevan, S., Morin, J., Nichols, J. D., Osten, R. A., Pérez-Torres, M., Pineda, J. S., Rigney, J., Saur, J., Stefánsson, G., Turner, J. D., Vedantham, H., Vidotto, A. A., Villadsen, J., and Zarka, P.
Subjects: Astrophysics - Earth and Planetary Astrophysics, Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics
Abstract: Radio detections of stellar systems provide a window onto stellar magnetic activity and the space weather conditions of extrasolar planets, information that is difficult to attain at other wavelengths. There have been recent advances observing auroral emissions from radio-bright low-mass stars and exoplanets largely due to the maturation of low-frequency radio instruments and the plethora of wide-field radio surveys. To guide us in placing these recent results in context, we introduce the foremost local analogues for the field: Solar bursts and the aurorae found on Jupiter. We detail how radio bursts associated with stellar flares are foundational to the study of stellar coronae, and time-resolved radio dynamic spectra offers one of the best prospects of detecting and characterising coronal mass ejections from other stars. We highlight the prospects of directly detecting coherent radio emission from exoplanetary magnetospheres, and early tentative results. We bridge this discussion to the field of brown dwarf radio emission, in which their larger and stronger magnetospheres are amenable to detailed study with current instruments. Bright, coherent radio emission is also predicted from magnetic interactions between stars and close-in planets. We discuss the underlying physics of these interactions and implications of recent provisional detections for exoplanet characterisation. We conclude with an overview of outstanding questions in theory of stellar, star-planet interaction, and exoplanet radio emission, and the prospects of future facilities in answering them., Comment: Accepted to Nature Astronomy. The manuscript is designed to be a primer for new doctoral students and scholars to the field of radio stars and exoplanets. 36 pages, 3 figures
Published: 2024

13. Simulating stellar coronal rain and slingshot prominences

Author: Daley-Yates, Simon and Jardine, Moira
Subjects: Astrophysics - Solar and Stellar Astrophysics
Abstract: We have numerically demonstrated that simulated cool star coronae naturally form condensations. If the star rotates slowly, with a co-rotation radius greater than the Alfv\'{e}n radius (i.e. $R_{\mathrm{K}} > R_{\mathrm{A}}$), these condensations will form below the co-rotation radius $R_{\mathrm{K}}$ and simply fall back to the stellar surface as coronal rain. If, however, the star is more rapidly rotating, ($R_{\mathrm{K}} < R_{\mathrm{A}}$), not only rain will form but also ``slingshot prominences''. In this case, condensations collect into a large mass reservoir around the co-rotation radius, from which periodic centrifugal ejections occur. In this case, some $51\%$ of the coronal mass is cold gas, either in rain or prominences. We find that 21\% of the mass lost by our simulated fast rotating star is cold gas. Studies of stellar mass-loss from the hot wind do not consider this component of the wind and therefore systematically underestimate mass-loss rates of these stars. Centrifugal ejections happen periodically, between every 7.5 - 17.5 hours with masses clustering around $10^{16}$ g, These results agree well with observational statistics. Contrasting the fast and slow rotating magnetospheres, we find that there are two distinct types of solutions, high lying and low lying loops. Low lying loops only produce coronal rain whereas high lying loops produce both rain and slingshots.
Published: 2024
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14. Three- to Six-Year-Olds Demonstration of Connection to Nature at a Montessori School in the Upper Midwest

Author: Natasha Yates
Abstract: Children are born into a world today with a drastically changing environmental climate. When young people develop an emotional attachment and sense of identity with nature, they may be more likely to behave in less destructive ways toward the planet and possibly live with a sense of responsibility and respect for nature. This mixed method study aimed to measure to what extent 3- to 6-year-olds demonstrated environmental sensitivity, awareness, and preferences at a nature-based Montessori school in the upper Midwest of the United States and asked if age was an influencing variable. Young children's connection to nature in these categories was determined using a modified age-appropriate psychological games testing tool, field observations of the types of nature features and activities the children experienced indoors and outdoors, and interviews with the two lead teachers. Results indicated that this cohort of children demonstrated a moderate to strong connection to nature in all three categories. Age was an influencing variable measuring environmental sensitivity and some aspects of environmental awareness, but not environmental preferences regarding where to play. This study adds to the body of work conducted in early childhood education, environmental education, early childhood environmental education for sustainability, and Montessori education.
Published: 2024

15. Exploring Business Students' Perceptions of Authentic Project-Based and Work-Integrated Assessments

Author: Maryam Mirzaei, Denisa Hebblethwaite, and Anne Yates
Abstract: This study draws on the perceptions of students who have completed authentic project-based and work-integrated assessments and explores their perception of authenticity of the assessments in terms of real work projects. In response to industry and government expectations for work-ready graduates three authentic assessments, were developed in collaboration with industry partners, covering different business program subjects. Drawing on the literature, a three-dimensional framework for authentic assessment in workplace learning was developed. These dimensions -- realism, producing an authentic product, and workplace-based judgment criteria -- formed the basis for thematic analysis of students' perceptions. A case study approach was used with three authentic assessment cases. Qualitative data was gathered via focus groups and surveys guided by the three-dimensional framework. Findings suggest that factors such as social environments created by engaging with industry, realistic tasks that replicated industry activities, and working in teams contributed to the perception of an authentic project-based and work-integrated assessment.
Published: 2024

16. Special Education and Psychology Majors Designing and Implementing Interventions for Algebra 1 Students with Learning Disabilities and Math Anxiety

Author: Casey Hord, Anna Fricano DeJarnette, Shelby Yates, Kilee Wolfe, Lizzie Bernys, Ian Byard, Ashley Bruner, Allison Patterson, Hannah E. Libby, and Meghan A. O’Connor
Abstract: The researchers conducted a qualitative case study on a learning community comprising special education and psychology majors. This community was strategically organized to foster interdisciplinary learning among its members and to design interventions for students struggling with mathematics anxiety, specifically in classes like Algebra 1. Working alongside a university faculty member and a graduate student, the undergraduates applied mental health principles from psychology research to real-world situations. Their efforts included teaching Algebra 1 to a student grappling with both a learning disability in mathematics and mathematics anxiety. In their approach, the group utilized mindfulness principles to tailor conversations to the high school student's interests, effectively incorporating mindfulness techniques into the mathematics tutoring sessions. This approach proved successful, particularly when the student was experiencing high levels of anxiety, enabling significant progress in his Algebra 1 work. The collaborative efforts also provided substantial benefits to the college students, who learned about each other's fields of study. They effectively combined this knowledge in practical ways and applied it in real-world settings. The findings of this study offer important insights for the training of pre-service special education teachers and college students in related fields.
Published: 2024

17. Using in vivo intact structure for system-wide quantitative analysis of changes in proteins.

Author: Son, Ahrum, Kim, Hyunsoo, Diedrich, Jolene, Bamberger, Casimir, McClatchy, Daniel, Lipton, Stuart, and Yates, John
Subjects: Animals, Alzheimer Disease, Mice, Proteomics, Disease Models, Animal, Protein Conformation, Humans, Protein Footprinting, Proteostasis, Proteins, Mass Spectrometry, Protein Folding, Lysine, Mice, Transgenic, Male
Abstract: Mass spectrometry-based methods can provide a global expression profile and structural readout of proteins in complex systems. Preserving the in vivo conformation of proteins in their innate state is challenging during proteomic experiments. Here, we introduce a whole animal in vivo protein footprinting method using perfusion of reagents to add dimethyl labels to exposed lysine residues on intact proteins which provides information about protein conformation. When this approach is used to measure dynamic structural changes during Alzheimers disease (AD) progression in a mouse model, we detect 433 proteins that undergo structural changes attributed to AD, independent of aging, across 7 tissues. We identify structural changes of co-expressed proteins and link the communities of these proteins to their biological functions. Our findings show that structural alterations of proteins precede changes in expression, thereby demonstrating the value of in vivo protein conformation measurement. Our method represents a strategy for untangling mechanisms of proteostasis dysfunction caused by protein misfolding. In vivo whole-animal footprinting should have broad applicability for discovering conformational changes in systemic diseases and for the design of therapeutic interventions.
Published: 2024

18. Nitrogen accountancy in space agriculture.

Author: Yates, Kevin, Berliner, Aaron, Makrygiorgos, Georgios, Kaiyom, Farrah, McNulty, Matthew, Khan, Imran, Kusuma, Paul, Kinlaw, Claire, Miron, Diogo, Legg, Charles, Wilson, James, Bugbee, Bruce, Mesbah, Ali, Arkin, Adam, Nandi, Somen, and Mcdonald, Karen
Abstract: Food production and pharmaceutical synthesis are posited as essential biotechnologies for facilitating human exploration beyond Earth. These technologies not only offer critical green space and food agency to astronauts but also promise to minimize mass and volume requirements through scalable, modular agriculture within closed-loop systems, offering an advantage over traditional bring-along strategies. Despite these benefits, the prevalent model for evaluating such systems exhibits significant limitations. It lacks comprehensive inventory and mass balance analyses for crop cultivation and life support, and fails to consider the complexities introduced by cultivating multiple crop varieties, which is crucial for enhancing food diversity and nutritional value. Here we expand space agriculture modeling to account for nitrogen dependence across an array of crops and demonstrate our model with experimental fitting of parameters. By adding nitrogen limitations, an extended model can account for potential interruptions in feedstock supply. Furthermore, sensitivity analysis was used to distill key consequential parameters that may be the focus of future experimental efforts.
Published: 2024

19. Anticipating how rain-on-snow events will change through the 21st century: lessons from the 1997 new year’s flood event

Author: Rhoades, Alan M, Zarzycki, Colin M, Hatchett, Benjamin J, Inda-Diaz, Héctor, Rudisill, William, Bass, Benjamin, Dennis, Eli, Heggli, Anne, McCrary, Rachel, McGinnis, Seth, Ombadi, Mohammed, Rahimi-Esfarjani, Stefan, Slinskey, Emily, Srivastava, Abhishekh, Szinai, Julia, Ullrich, Paul A, Wehner, Michael, Yates, David, and Jones, Andrew D
Subjects: Hydrology, Physical Geography and Environmental Geoscience, Atmospheric Sciences, Earth Sciences, Climate Action, Climate change, Mountain hydrometeorology, Compound extremes, Rain-on-snow, Floods, Regionally refined earth system modeling, Oceanography, Meteorology & Atmospheric Sciences, Atmospheric sciences, Climate change science
Abstract: The California-Nevada 1997 New Year’s flood was an atmospheric river (AR)-driven rain-on-snow (RoS) event and remains the costliest in their history. The joint occurrence of saturated soils, rainfall, and snowmelt generated inundation throughout northern California-Nevada. Although AR RoS events are projected to occur more frequently with climate change, the warming sensitivity of their flood drivers across scales remains understudied. We leverage the regionally refined mesh capabilities of the Energy Exascale Earth System Model (RRM-E3SM) to recreate the 1997 New Year’s flood with horizontal grid spacings of 3.5 km across California, with forecast lead times of up to 4 days, and across six warming levels ranging from pre-industrial conditions to +3.5∘C. We describe the sensitivity of the flood drivers to warming including AR duration and intensity, precipitation phase, intensity and efficiency, snowpack mass and energy changes, and runoff efficiency. Our findings indicate current levels of climate change negligibly influence the flood drivers. At warming levels ≥1.7∘C, AR hazard potential increases, snowpack nonlinearly decreases, antecedent soil moisture decreases (except where the snowline retreats), and runoff decreases (except in the southern Sierra Nevada where antecedent snowpack persists). Storm total precipitation increases, but at rates below warming-induced increases in saturation-specific humidity. Warming intensifies short-duration, high-intensity rainfall, particularly where snowfall-to-rainfall transitions occur. This study highlights the nonlinear tradeoffs in 21st-century RoS flood hazards with warming and provides water management and infrastructure investment adaptation considerations.
Published: 2024

20. Age and Value of Information Optimization for Systems with Multi-Class Updates

Author: Arafa, Ahmed and Yates, Roy D.
Subjects: Computer Science - Information Theory, Computer Science - Networking and Internet Architecture, Electrical Engineering and Systems Science - Signal Processing
Abstract: Received samples of a stochastic process are processed by a server for delivery as updates to a monitor. Each sample belongs to a class that specifies a distribution for its processing time and a function that describes how the value of the processed update decays with age at the monitor. The class of a sample is identified when the processed update is delivered. The server implements a form of M/G/1/1 blocking queue; samples arriving at a busy server are discarded and samples arriving at an idle server are subject to an admission policy that depends on the age and class of the prior delivered update. For the delivered updates, we characterize the average age of information (AoI) and average value of information (VoI). We derive the optimal stationary policy that minimizes the convex combination of the AoI and (negative) VoI. It is shown that the policy has a threshold structure, in which a new sample is allowed to arrive to the server only if the previous update's age and value difference surpasses a certain threshold that depends on the specifics of the value function and system statistics., Comment: Appeared in IEEE ICC 2024
Published: 2024

21. DUNE Phase II: Scientific Opportunities, Detector Concepts, Technological Solutions

Author: DUNE Collaboration, Abud, A. Abed, Abi, B., Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adamowski, M., Adams, D., Adinolfi, M., Adriano, C., Aduszkiewicz, A., Aguilar, J., Akbar, F., Allison, K., Monsalve, S. Alonso, Alrashed, M., Alton, A., Alvarez, R., Alves, T., Amar, H., Amedo, P., Anderson, J., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Ankowski, A., Antic, D., Antoniassi, M., Antonova, M., Antoshkin, A., Aranda-Fernandez, A., Arellano, L., Diaz, E. Arrieta, Arroyave, M. A., Asaadi, J., Ashkenazi, A., Asner, D. M., Asquith, L., Atkin, E., Auguste, D., Aurisano, A., Aushev, V., Autiero, D., Azam, M. B., Azfar, F., Back, A., Back, H., Back, J. J., Bagaturia, I., Bagby, L., Balashov, N., Balasubramanian, S., Baldi, P., Baldini, W., Baldonedo, J., Baller, B., Bambah, B., Banerjee, R., Barao, F., Barbu, D., Barenboim, G., Barham~Alzás, P., Barker, G. J., Barkhouse, W., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, D., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Batchelor, C., Bathe-Peters, L., Battat, J. B. R., Battisti, F., Bay, F., Bazetto, M. C. Q., Alba, J. L. L. Bazo, Beacom, J. F., Bechetoille, E., Behera, B., Belchior, E., Bell, G., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Benekos, N., Montiel, C. Benitez, Benjamin, D., Neves, F. Bento, Berger, J., Berkman, S., Bernal, J., Bernardini, P., Bersani, A., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bevan, A., Bezawada, Y., Bezerra, A. T., Bezerra, T. J., Bhat, A., Bhatnagar, V., Bhatt, J., Bhattacharjee, M., Bhattacharya, M., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biery, K., Bilki, B., Bishai, M., Bitadze, A., Blake, A., Blaszczyk, F. D., Blazey, G. C., Blucher, E., Bodek, A., Bogenschuetz, J., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bonilla-Diaz, C., Bonini, F., Booth, A., Boran, F., Bordoni, S., Merlo, R. Borges, Borkum, A., Bostan, N., Bouet, R., Boza, J., Bracinik, J., Brahma, B., Brailsford, D., Bramati, F., Branca, A., Brandt, A., Bremer, J., Brew, C., Brice, S. J., Brio, V., Brizzolari, C., Bromberg, C., Brooke, J., Bross, A., Brunetti, G., Brunetti, M., Buchanan, N., Budd, H., Buergi, J., Bundock, A., Burgardt, D., Butchart, S., V., G. Caceres, Cagnoli, I., Cai, T., Calabrese, R., Calcutt, J., Calivers, L., Calvo, E., Caminata, A., Camino, A. F., Campanelli, W., Campani, A., Benitez, A. Campos, Canci, N., Capó, J., Caracas, I., Caratelli, D., Carber, D., Carceller, J. M., Carini, G., Carlus, B., Carneiro, M. F., Carniti, P., Terrazas, I. Caro, Carranza, H., Carrara, N., Carroll, L., Carroll, T., Carter, A., Casarejos, E., Casazza, D., Forero, J. F. Castaño, Castaño, F. A., Castillo, A., Castromonte, C., Catano-Mur, E., Cattadori, C., Cavalier, F., Cavanna, F., Centro, S., Cerati, G., Cerna, C., Cervelli, A., Villanueva, A. Cervera, Chakraborty, K., Chakraborty, S., Chalifour, M., Chappell, A., Charitonidis, N., Chatterjee, A., Chen, H., Chen, M., Chen, W. C., Chen, Y., Chen-Wishart, Z., Cherdack, D., Chi, C., Chiapponi, F., Chirco, R., Chitirasreemadam, N., Cho, K., Choate, S., Chokheli, D., Chong, P. S., Chowdhury, B., Christian, D., Chukanov, A., Chung, M., Church, E., Cicala, M. F., Cicerchia, M., Cicero, V., Ciolini, R., Clarke, P., Cline, G., Coan, T. E., Cocco, A. G., Coelho, J. A. B., Cohen, A., Collazo, J., Collot, J., Conley, E., Conrad, J. M., Convery, M., Copello, S., Cortez, A. F. V., Cova, P., Cox, C., Cremaldi, L., Cremonesi, L., Crespo-Anadón, J. I., Crisler, M., Cristaldo, E., Crnkovic, J., Crone, G., Cross, R., Cudd, A., Cuesta, C., Cui, Y., Curciarello, F., Cussans, D., Dai, J., Dalager, O., Dallavalle, R., Dallaway, W., D'Amico, R., da Motta, H., Dar, Z. A., Darby, R., Peres, L. Da Silva, David, Q., Davies, G. S., Davini, S., Dawson, J., De Aguiar, R., De Almeida, P., Debbins, P., De Bonis, I., Decowski, M. P., de Gouvêa, A., De Holanda, P. C., Astiz, I. L. De Icaza, De Jong, P., Sanchez, P. Del Amo, De la Torre, A., De Lauretis, G., Delbart, A., Delepine, D., Delgado, M., Dell'Acqua, A., Monache, G. Delle, Delmonte, N., De Lurgio, P., Demario, R., De Matteis, G., Neto, J. R. T. de Mello, DeMuth, D. M., Dennis, S., Densham, C., Denton, P., Deptuch, G. W., De Roeck, A., De Romeri, V., Detje, J. P., Devine, J., Dharmapalan, R., Dias, M., Diaz, A., Díaz, J. S., Díaz, F., Di Capua, F., Di Domenico, A., Di Domizio, S., Di Falco, S., Di Giulio, L., Ding, P., Di Noto, L., Diociaiuti, E., Distefano, C., Diurba, R., Diwan, M., Djurcic, Z., Doering, D., Dolan, S., Dolek, F., Dolinski, M. J., Domenici, D., Domine, L., Donati, S., Donon, Y., Doran, S., Douglas, D., Doyle, T. A., Dragone, A., Drielsma, F., Duarte, L., Duchesneau, D., Duffy, K., Dugas, K., Dunne, P., Dutta, B., Duyang, H., Dwyer, D. A., Dyshkant, A. S., Dytman, S., Eads, M., Earle, A., Edayath, S., Edmunds, D., Eisch, J., Englezos, P., Ereditato, A., Erjavec, T., Escobar, C. O., Evans, J. J., Ewart, E., Ezeribe, A. C., Fahey, K., Fajt, L., Falcone, A., Fani', M., Farnese, C., Farrell, S., Farzan, Y., Fedoseev, D., Felix, J., Feng, Y., Fernandez-Martinez, E., Fernández-Posada, D., Ferry, G., Fialova, E., Fields, L., Filip, P., Filkins, A., Filthaut, F., Fine, R., Fiorillo, G., Fiorini, M., Fogarty, S., Foreman, W., Fowler, J., Franc, J., Francis, K., Franco, D., Franklin, J., Freeman, J., Fried, J., Friedland, A., Fuess, S., Furic, I. K., Furman, K., Furmanski, A. P., Gaba, R., Gabrielli, A., M~Gago, A., Galizzi, F., Gallagher, H., Gallice, N., Galymov, V., Gamberini, E., Gamble, T., Ganacim, F., Gandhi, R., Ganguly, S., Gao, F., Gao, S., Garcia-Gamez, D., García-Peris, M. Á., Gardim, F., Gardiner, S., Gastler, D., Gauch, A., Gauvreau, J., Gauzzi, P., Gazzana, S., Ge, G., Geffroy, N., Gelli, B., Gent, S., Gerlach, L., Ghorbani-Moghaddam, Z., Giammaria, T., Gibin, D., Gil-Botella, I., Gilligan, S., Gioiosa, A., Giovannella, S., Girerd, C., Giri, A. K., Giugliano, C., Giusti, V., Gnani, D., Gogota, O., Gollapinni, S., Gollwitzer, K., Gomes, R. A., Bermeo, L. V. Gomez, Fajardo, L. S. Gomez, Gonnella, F., Gonzalez-Diaz, D., Gonzalez-Lopez, M., Goodman, M. C., Goswami, S., Gotti, C., Goudeau, J., Goudzovski, E., Grace, C., Gramellini, E., Gran, R., Granados, E., Granger, P., Grant, C., Gratieri, D. R., Grauso, G., Green, P., Greenberg, S., Greer, J., Griffith, W. C., Groetschla, F. T., Grzelak, K., Gu, L., Gu, W., Guarino, V., Guarise, M., Guenette, R., Guerzoni, M., Guffanti, D., Guglielmi, A., Guo, B., Guo, F. Y., Gupta, A., Gupta, V., Gurung, G., Gutierrez, D., Guzowski, P., Guzzo, M. M., Gwon, S., Habig, A., Hadavand, H., Haegel, L., Haenni, R., Hagaman, L., Hahn, A., Haiston, J., Hakenmüller, J., Hamernik, T., Hamilton, P., Hancock, J., Happacher, F., Harris, D. A., Hart, A., Hartnell, J., Hartnett, T., Harton, J., Hasegawa, T., Hasnip, C. M., Hatcher, R., Hayrapetyan, K., Hays, J., Hazen, E., He, M., Heavey, A., Heeger, K. M., Heise, J., Hellmuth, P., Henry, S., Hernández-García, J., Herner, K., Hewes, V., Higuera, A., Hilgenberg, C., Hillier, S. J., Himmel, A., Hinkle, E., Hirsch, L. R., Ho, J., Hoff, J., Holin, A., Holvey, T., Hoppe, E., Horiuchi, S., Horton-Smith, G. A., Houdy, T., Howard, B., Howell, R., Hristova, I., Hronek, M. S., Huang, J., Huang, R. G., Hulcher, Z., Ibrahim, M., Iles, G., Ilic, N., Iliescu, A. M., Illingworth, R., Ingratta, G., Ioannisian, A., Irwin, B., Isenhower, L., Oliveira, M. Ismerio, Itay, R., Jackson, C. M., Jain, V., James, E., Jang, W., Jargowsky, B., Jena, D., Jentz, I., Ji, X., Jiang, C., Jiang, J., Jiang, L., Jipa, A., Jo, J. H., Joaquim, F. R., Johnson, W., Jollet, C., Jones, B., Jones, R., Jovancevic, N., Judah, M., Jung, C. K., Junk, T., Jwa, Y., Kabirnezhad, M., Kaboth, A. C., Kadenko, I., Kakorin, I., Kalitkina, A., Kalra, D., Kandemir, M., Kaplan, D. M., Karagiorgi, G., Karaman, G., Karcher, A., Karyotakis, Y., Kasai, S., Kasetti, S. P., Kashur, L., Katsioulas, I., Kauther, A., Kazaryan, N., Ke, L., Kearns, E., Keener, P. T., Kelly, K. J., Kemp, E., Kemularia, O., Kermaidic, Y., Ketchum, W., Kettell, S. H., Khabibullin, M., Khan, N., Khvedelidze, A., Kim, D., Kim, J., Kim, M. J., King, B., Kirby, B., Kirby, M., Kish, A., Klein, J., Kleykamp, J., Klustova, A., Kobilarcik, T., Koch, L., Koehler, K., Koerner, L. W., Koh, D. H., Kolupaeva, L., Korablev, D., Kordosky, M., Kosc, T., Kose, U., Kostelecký, V. A., Kothekar, K., Kotler, I., Kovalcuk, M., Kozhukalov, V., Krah, W., Kralik, R., Kramer, M., Kreczko, L., Krennrich, F., Kreslo, I., Kroupova, T., Kubota, S., Kubu, M., Kudenko, Y., Kudryavtsev, V. A., Kufatty, G., Kuhlmann, S., Kulagin, S., Kumar, J., Kumar, P., Kumaran, S., Kunzmann, J., Kuravi, R., Kurita, N., Kuruppu, C., Kus, V., Kutter, T., Kuźniak, M., Kvasnicka, J., Labree, T., Lackey, T., Lalău, I., Lambert, A., Land, B. J., Lane, C. E., Lane, N., Lang, K., Langford, T., Langstaff, M., Lanni, F., Lantwin, O., Larkin, J., Lasorak, P., Last, D., Laudrain, A., Laundrie, A., Laurenti, G., Lavaut, E., Laycock, P., Lazanu, I., LaZur, R., Lazzaroni, M., Le, T., Leardini, S., Learned, J., LeCompte, T., Legin, V., Miotto, G. Lehmann, Lehnert, R., de Oliveira, M. A. Leigui, Leitner, M., Silverio, D. Leon, Lepin, L. M., -Y~Li, J., Li, S. W., Li, Y., Liao, H., Lin, C. S., Lindebaum, D., Linden, S., Lineros, R. A., Lister, A., Littlejohn, B. R., Liu, H., Liu, J., Liu, Y., Lockwitz, S., Lokajicek, M., Lomidze, I., Long, K., Lopes, T. V., Lopez, J., de Rego, I. López, López-March, N., Lord, T., LoSecco, J. M., Louis, W. C., Sanchez, A. Lozano, Lu, X. -G., Luk, K. B., Lunday, B., Luo, X., Luppi, E., MacFarlane, D., Machado, A. A., Machado, P., Macias, C. T., Macier, J. R., MacMahon, M., Maddalena, A., Madera, A., Madigan, P., Magill, S., Magueur, C., Mahn, K., Maio, A., Major, A., Majumdar, K., Mameli, S., Man, M., Mandujano, R. C., Maneira, J., Manly, S., Mann, A., Manolopoulos, K., Plata, M. Manrique, Corchado, S. Manthey, Manyam, V. N., Marchan, M., Marchionni, A., Marciano, W., Marfatia, D., Mariani, C., Maricic, J., Marinho, F., Marino, A. D., Markiewicz, T., Marques, F. Das Chagas, Marquet, C., Marshak, M., Marshall, C. M., Marshall, J., Martina, L., Martín-Albo, J., Martinez, N., Caicedo, D. A. Martinez, López, F. Martínez, Miravé, P. Martínez, Martynenko, S., Mascagna, V., Massari, C., Mastbaum, A., Matichard, F., Matsuno, S., Matteucci, G., Matthews, J., Mauger, C., Mauri, N., Mavrokoridis, K., Mawby, I., Mazza, R., McAskill, T., McConkey, N., McFarland, K. S., McGrew, C., McNab, A., Meazza, L., Meddage, V. C. N., Mefodiev, A., Mehta, B., Mehta, P., Melas, P., Mena, O., Mendez, H., Mendez, P., Méndez, D. P., Menegolli, A., Meng, G., Mercuri, A. C. E. A., Meregaglia, A., Messier, M. D., Metallo, S., Metcalf, W., Mewes, M., Meyer, H., Miao, T., Micallef, J., Miccoli, A., Michna, G., Milincic, R., Miller, F., Miller, G., Miller, W., Mineev, O., Minotti, A., Miralles, L., Miranda, O. G., Mironov, C., Miryala, S., Miscetti, S., Mishra, C. S., Mishra, P., Mishra, S. R., Mislivec, A., Mitchell, M., Mladenov, D., Mocioiu, I., Mogan, A., Moggi, N., Mohanta, R., Mohayai, T. A., Mokhov, N., Molina, J., Bueno, L. Molina, Montagna, E., Montanari, A., Montanari, C., Montanari, D., Montanino, D., Zetina, L. M. Montaño, Mooney, M., Moor, A. F., Moore, Z., Moreno, D., Moreno-Palacios, O., Morescalchi, L., Moretti, D., Moretti, R., Morris, C., Mossey, C., Moura, C. A., Mouster, G., Mu, W., Mualem, L., Mueller, J., Muether, M., Muheim, F., Muir, A., Mulhearn, M., Munford, D., Munteanu, L. J., Muramatsu, H., Muraz, J., Murphy, M., Murphy, T., Muse, J., Mytilinaki, A., Nachtman, J., Nagai, Y., Nagu, S., Nandakumar, R., Naples, D., Narita, S., Navrer-Agasson, A., Nayak, N., Nebot-Guinot, M., Nehm, A., Nelson, J. K., Neogi, O., Nesbit, J., Nessi, M., Newbold, D., Newcomer, M., Nichol, R., Nicolas-Arnaldos, F., Nikolica, A., Nikolov, J., Niner, E., Nishimura, K., Norman, A., Norrick, A., Novella, P., Nowak, A., Nowak, J. A., Oberling, M., Ochoa-Ricoux, J. P., Oh, S., Oh, S. B., Olivier, A., Olshevskiy, A., Olson, T., Onel, Y., Onishchuk, Y., Oranday, A., Gann, G. D. Orebi, Osbiston, M., Vélez, J. A. Osorio, O'Sullivan, L., Ormachea, L. Otiniano, Ott, J., Pagani, L., Palacio, G., Palamara, O., Palestini, S., Paley, J. M., Pallavicini, M., Palomares, C., Pan, S., Panda, P., Vazquez, W. Panduro, Pantic, E., Paolone, V., Papaleo, R., Papanestis, A., Papoulias, D., Paramesvaran, S., Paris, A., Parke, S., Parozzi, E., Parsa, S., Parsa, Z., Parveen, S., Parvu, M., Pasciuto, D., Pascoli, S., Pasqualini, L., Pasternak, J., Patrick, C., Patrizii, L., Patterson, R. B., Patzak, T., Paudel, A., Paulucci, L., Pavlovic, Z., Pawloski, G., Payne, D., Pec, V., Pedreschi, E., Peeters, S. J. M., Pellico, W., Perez, A. Pena, Pennacchio, E., Penzo, A., Peres, O. L. G., Gonzalez, Y. F. Perez, Pérez-Molina, L., Pernas, C., Perry, J., Pershey, D., Pessina, G., Petrillo, G., Petta, C., Petti, R., Pfaff, M., Pia, V., Pickering, L., Pietropaolo, F., Pimentel, V. L., Pinaroli, G., Pincha, S., Pinchault, J., Pitts, K., Plows, K., Pollack, C., Pollman, T., Pompa, F., Pons, X., Poonthottathil, N., Popov, V., Poppi, F., Porter, J., Paix{ã}o, L. G. 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Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract: The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy toward the implementation of this leading-edge, large-scale science project. The 2023 report of the US Particle Physics Project Prioritization Panel (P5) reaffirmed this vision and strongly endorsed DUNE Phase I and Phase II, as did the European Strategy for Particle Physics. While the construction of the DUNE Phase I is well underway, this White Paper focuses on DUNE Phase II planning. DUNE Phase-II consists of a third and fourth far detector (FD) module, an upgraded near detector complex, and an enhanced 2.1 MW beam. The fourth FD module is conceived as a "Module of Opportunity", aimed at expanding the physics opportunities, in addition to supporting the core DUNE science program, with more advanced technologies. This document highlights the increased science opportunities offered by the DUNE Phase II near and far detectors, including long-baseline neutrino oscillation physics, neutrino astrophysics, and physics beyond the standard model. It describes the DUNE Phase II near and far detector technologies and detector design concepts that are currently under consideration. A summary of key R&D goals and prototyping phases needed to realize the Phase II detector technical designs is also provided. DUNE's Phase II detectors, along with the increased beam power, will complete the full scope of DUNE, enabling a multi-decadal program of groundbreaking science with neutrinos.
Published: 2024

22. Coulomb Excitation of $^{80}$Sr and the limits of the $N = Z = 40$ island of deformation

Author: Russell, R., Heery, J., Henderson, J., Wadsworth, R., Kaneko, K., Shimizu, N., Mizusaki, T., Sun, Y., Andreoiu, C., Annen, D. W., Avaa, A. A., Ball, G. C., Bildstein, V., Buck, S., Cousins, C., Garnsworthy, A. B., Gillespie, S. A., Greaves, B., Grimes, A., Hackman, G., Hughes, R. O., Jenkins, D. G., Kowalewski, T. M., Martin, M. S., Müller-Gatermann, C., Murias, J. R., Murillo-Morales, S., Pascu, S., Rhodes, D. M., Smallcombe, J., Spagnoletti, P., Svensson, C. E., Wallis, B., Williams, J., Wu, C. Y., and Yates, D.
Subjects: Nuclear Experiment, Nuclear Theory
Abstract: The region of $N\approx Z\approx 40$ has long been associated with strongly deformed nuclear configurations. The presence of this strong deformation was recently confirmed through lifetime measurements in $N\approx Z$ Sr and Zr nuclei. Theoretically, however, these nuclei present a challenge due to the vast valence space required to incorporate all deformation driving interactions. Recent state-of-the-art predictions indicate a near axial prolate deformation for $N=Z$ and $N=Z+2$ nuclei between $N=Z=36$ and $N=Z=40$. In this work we investigate the shores of this island of deformation through a sub-barrier Coulomb excitation study of the $N=Z+4$ nucleus, \textsuperscript{80}Sr. Extracting a spectroscopic quadrupole moment of $Q_s(2^+_1) = 0.45^{+0.83}_{-0.88}$~eb, we find that \textsuperscript{80}Sr is inconsistent with significant axial prolate deformation. This indicates that the predicted region of strong prolate deformation around $N=Z=40$ is tightly constrained to the quartet of nuclei: \textsuperscript{76,78}Sr and \textsuperscript{78,80}Zr., Comment: Submitted to Physics Letters B
Published: 2024

23. First Measurement of the Total Inelastic Cross-Section of Positively-Charged Kaons on Argon at Energies Between 5.0 and 7.5 GeV

Author: DUNE Collaboration, Abud, A. Abed, Abi, B., Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adamowski, M., Adams, D., Adinolfi, M., Adriano, C., Aduszkiewicz, A., Aguilar, J., Akbar, F., Allison, K., Monsalve, S. Alonso, Alrashed, M., Alton, A., Alvarez, R., Alves, T., Amar, H., Amedo, P., Anderson, J., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Ankowski, A., Antic, D., Antoniassi, M., Antonova, M., Antoshkin, A., Aranda-Fernandez, A., Arellano, L., Diaz, E. Arrieta, Arroyave, M. A., Asaadi, J., Ashkenazi, A., Asner, D., Asquith, L., Atkin, E., Auguste, D., Aurisano, A., Aushev, V., Autiero, D., Azam, M. B., Azfar, F., Back, A., Back, H., Back, J. J., Bagaturia, I., Bagby, L., Balashov, N., Balasubramanian, S., Baldi, P., Baldini, W., Baldonedo, J., Baller, B., Bambah, B., Banerjee, R., Barao, F., Barbu, D., Barenboim, G., Barham~Alzás, P., Barker, G. J., Barkhouse, W., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, D., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Batchelor, C., Bathe-Peters, L., Battat, J. B. R., Battisti, F., Bay, F., Bazetto, M. C. Q., Alba, J. L. L. Bazo, Beacom, J. F., Bechetoille, E., Behera, B., Belchior, E., Bell, G., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Benekos, N., Montiel, C. Benitez, Benjamin, D., Neves, F. Bento, Berger, J., Berkman, S., Bernal, J., Bernardini, P., Bersani, A., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bevan, A., Bezawada, Y., Bezerra, A. T., Bezerra, T. J., Bhat, A., Bhatnagar, V., Bhatt, J., Bhattacharjee, M., Bhattacharya, M., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biery, K., Bilki, B., Bishai, M., Bitadze, A., Blake, A., Blaszczyk, F. D., Blazey, G. C., Blucher, E., Bodek, A., Bogenschuetz, J., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bonilla-Diaz, C., Bonini, F., Booth, A., Boran, F., Bordoni, S., Merlo, R. Borges, Borkum, A., Bostan, N., Bouet, R., Boza, J., Bracinik, J., Brahma, B., Brailsford, D., Bramati, F., Branca, A., Brandt, A., Bremer, J., Brew, C., Brice, S. J., Brio, V., Brizzolari, C., Bromberg, C., Brooke, J., Bross, A., Brunetti, G., Brunetti, M., Buchanan, N., Budd, H., Buergi, J., Bundock, A., Burgardt, D., Butchart, S., V., G. Caceres, Cagnoli, I., Cai, T., Calabrese, R., Calcutt, J., Calivers, L., Calvo, E., Caminata, A., Camino, A. F., Campanelli, W., Campani, A., Benitez, A. Campos, Canci, N., Capó, J., Caracas, I., Caratelli, D., Carber, D., Carceller, J. M., Carini, G., Carlus, B., Carneiro, M. F., Carniti, P., Terrazas, I. Caro, Carranza, H., Carrara, N., Carroll, L., Carroll, T., Carter, A., Casarejos, E., Casazza, D., Forero, J. F. Castaño, Castaño, F. A., Castillo, A., Castromonte, C., Catano-Mur, E., Cattadori, C., Cavalier, F., Cavanna, F., Centro, S., Cerati, G., Cerna, C., Cervelli, A., Villanueva, A. Cervera, Chakraborty, K., Chakraborty, S., Chalifour, M., Chappell, A., Charitonidis, N., Chatterjee, A., Chen, H., Chen, M., Chen, W. C., Chen, Y., Chen-Wishart, Z., Cherdack, D., Chi, C., Chiapponi, F., Chirco, R., Chitirasreemadam, N., Cho, K., Choate, S., Chokheli, D., Chong, P. S., Chowdhury, B., Christian, D., Chukanov, A., Chung, M., Church, E., Cicala, M. F., Cicerchia, M., Cicero, V., Ciolini, R., Clarke, P., Cline, G., Coan, T. E., Cocco, A. G., Coelho, J. A. B., Cohen, A., Collazo, J., Collot, J., Conley, E., Conrad, J. M., Convery, M., Copello, S., Cova, P., Cox, C., Cremaldi, L., Cremonesi, L., Crespo-Anadón, J. I., Crisler, M., Cristaldo, E., Crnkovic, J., Crone, G., Cross, R., Cudd, A., Cuesta, C., Cui, Y., Curciarello, F., Cussans, D., Dai, J., Dalager, O., Dallavalle, R., Dallaway, W., D'Amico, R., da Motta, H., Dar, Z. A., Darby, R., Peres, L. Da Silva, David, Q., Davies, G. S., Davini, S., Dawson, J., De Aguiar, R., De Almeida, P., Debbins, P., De Bonis, I., Decowski, M. P., de Gouvêa, A., De Holanda, P. C., Astiz, I. L. De Icaza, De Jong, P., Sanchez, P. Del Amo, De la Torre, A., De Lauretis, G., Delbart, A., Delepine, D., Delgado, M., Dell'Acqua, A., Monache, G. Delle, Delmonte, N., De Lurgio, P., Demario, R., De Matteis, G., Neto, J. R. T. de Mello, DeMuth, D. M., Dennis, S., Densham, C., Denton, P., Deptuch, G. W., De Roeck, A., De Romeri, V., Detje, J. P., Devine, J., Dharmapalan, R., Dias, M., Diaz, A., Díaz, J. S., Díaz, F., Di Capua, F., Di Domenico, A., Di Domizio, S., Di Falco, S., Di Giulio, L., Ding, P., Di Noto, L., Diociaiuti, E., Distefano, C., Diurba, R., Diwan, M., Djurcic, Z., Doering, D., Dolan, S., Dolek, F., Dolinski, M. J., Domenici, D., Domine, L., Donati, S., Donon, Y., Doran, S., Douglas, D., Doyle, T. A., Dragone, A., Drielsma, F., Duarte, L., Duchesneau, D., Duffy, K., Dugas, K., Dunne, P., Dutta, B., Duyang, H., Dwyer, D. A., Dyshkant, A. S., Dytman, S., Eads, M., Earle, A., Edayath, S., Edmunds, D., Eisch, J., Englezos, P., Ereditato, A., Erjavec, T., Escobar, C. O., Evans, J. J., Ewart, E., Ezeribe, A. C., Fahey, K., Fajt, L., Falcone, A., Fani', M., Farnese, C., Farrell, S., Farzan, Y., Fedoseev, D., Felix, J., Feng, Y., Fernandez-Martinez, E., Ferry, G., Fialova, E., Fields, L., Filip, P., Filkins, A., Filthaut, F., Fine, R., Fiorillo, G., Fiorini, M., Fogarty, S., Foreman, W., Fowler, J., Franc, J., Francis, K., Franco, D., Franklin, J., Freeman, J., Fried, J., Friedland, A., Fuess, S., Furic, I. K., Furman, K., Furmanski, A. P., Gaba, R., Gabrielli, A., M~Gago, A., Galizzi, F., Gallagher, H., Gallice, N., Galymov, V., Gamberini, E., Gamble, T., Ganacim, F., Gandhi, R., Ganguly, S., Gao, F., Gao, S., Garcia-Gamez, D., García-Peris, M. 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V., Vannozzi, A., Van Nuland-Troost, M., Varanini, F., Oliva, D. Vargas, Vasina, S., Vaughan, N., Vaziri, K., Vázquez-Ramos, A., Vega, J., Ventura, S., Verdugo, A., Vergani, S., Verzocchi, M., Vetter, K., Vicenzi, M., de Souza, H. Vieira, Vignoli, C., Vilela, C., Villa, E., Viola, S., Viren, B., Hernandez, A. P. Vizcaya, Vuong, Q., Waldron, A. V., Wallbank, M., Walsh, J., Walton, T., Wang, H., Wang, J., Wang, L., Wang, M. H. L. S., Wang, X., Wang, Y., Warburton, K., Warner, D., Warsame, L., Wascko, M. O., Waters, D., Watson, A., Wawrowska, K., Weber, A., Weber, C. M., Weber, M., Wei, H., Weinstein, A., Westerdale, S., Wetstein, M., Whalen, K., White, A., Whitehead, L. H., Whittington, D., Wilhlemi, J., Wilking, M. J., Wilkinson, A., Wilkinson, C., Wilson, F., Wilson, R. J., Winter, P., Wisniewski, W., Wolcott, J., Wolfs, J., Wongjirad, T., Wood, A., Wood, K., Worcester, E., Worcester, M., Wospakrik, M., Wresilo, K., Wret, C., Wu, S., Wu, W., Wurm, M., Wyenberg, J., Xiao, Y., Xiotidis, I., Yaeggy, B., Yahlali, N., Yandel, E., Yang, J., Yang, K., Yang, T., Yankelevich, A., Yershov, N., Yonehara, K., Young, T., Yu, B., Yu, H., Yu, J., Yu, Y., Yuan, W., Zaki, R., Zalesak, J., Zambelli, L., Zamorano, B., Zani, A., Zapata, O., Zazueta, L., Zeller, G. P., Zennamo, J., Zeug, K., Zhang, C., Zhang, S., Zhao, M., Zhivun, E., Zimmerman, E. D., Zucchelli, S., Zuklin, J., Zutshi, V., and Zwaska, R.
Subjects: High Energy Physics - Experiment, Physics - Instrumentation and Detectors
Abstract: ProtoDUNE Single-Phase (ProtoDUNE-SP) is a 770-ton liquid argon time projection chamber that operated in a hadron test beam at the CERN Neutrino Platform in 2018. We present a measurement of the total inelastic cross section of charged kaons on argon as a function of kaon energy using 6 and 7 GeV/$c$ beam momentum settings. The flux-weighted average of the extracted inelastic cross section at each beam momentum setting was measured to be 380$\pm$26 mbarns for the 6 GeV/$c$ setting and 379$\pm$35 mbarns for the 7 GeV/$c$ setting.
Published: 2024
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24. Modelling the multi-wavelength detection of protoclusters. I: An excess of submillimetre galaxies in protocluster cores

Author: Araya-Araya, Pablo, Cochrane, Rachel K., Hayward, Christopher C., Yates, Robert M., Sodré Jr., Laerte, Vicentin, Marcelo C., Rennehan, Douglas, Overzier, Roderik, and van Daalen, Marcel
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: Studies of galaxy protoclusters yield insights into galaxy cluster formation complementary to those obtained via `archaeological' studies of present-day galaxy clusters. Submillimetre-selected galaxies (SMGs) are one class of sources used to find high-redshift protoclusters. However, due to the rarity of protoclusters (and thus the large simulation volume required) and the complexity of modeling dust emission from galaxies, the relationship between SMGs and protoclusters has not been adequately addressed in the theoretical literature. In this work, we apply the L-GALAXIES semi-analytic model (SAM) to the Millennium N-body simulation. We assign submillimetre (submm) flux densities to the model galaxies using a scaling relation from previous work, in which dust radiative transfer was performed on high-resolution galaxy zoom simulations. We find that the fraction of model galaxies that are submm-bright is higher in protocluster cores than in both protocluster `outskirts' and the field; the fractions for the latter two are similar. This excess is not driven by an enhanced starburst frequency. Instead, the primary reason is that overdense environments have a relative overdensity of high-mass halos and thus `oversample' the high-mass end of the star formation main sequence relative to less-dense environments. The fraction of SMGs that are optically bright is dependent on stellar mass and redshift but independent of environment. The fraction of galaxies for which the majority of star formation is dust-obscured is higher in protocluster cores, primarily due to the dust-obscured fraction being correlated with stellar mass. Our results can be used to guide and interpret multi-wavelength studies of galaxy populations in protoclusters., Comment: 25 pages, 16 figures (including Appendix), Accepted for publication in ApJ
Published: 2024

25. The Chemical Diversity of the Metal-Poor Milky Way

Author: Buckley, Nicole, Das, Payel, Jofré, Paula, Yates, Robert M., and Hawkins, Keith
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: We present a detailed study of the chemical diversity of the metal-poor Milky Way (MW) using data from the GALAH DR3 survey. Considering 17 chemical abundances relative to iron ([X/Fe]) for 9,923 stars, we employ Principal Component Analysis (PCA) and Extreme Deconvolution (XD) to identify 10 distinct stellar groups. This approach, free from chemical or dynamical cuts, reveals known populations, including the accreted halo, thick disc, thin disc, and in-situ halo. The thick disc is characterised by multiple substructures, suggesting it comprises stars formed in diverse environments. Our findings highlight the limited discriminatory power of magnesium in separating accreted and disc stars. Elements such as Ba, Al, Cu, and Sc are critical in distinguishing disc from accreted stars, while Ba, Y, Eu and Zn differentiate disc and accreted stars from the in-situ halo. This study demonstrates the potential power of combining a latent space representation of the data (PCA) with a clustering algorithm (XD) in Galactic archaeology, in providing new insights into the galaxy's assembly and evolutionary history.
Published: 2024

26. Chemical tracers of a highly eccentric AGB-main sequence star binary

Author: Danilovich, T., Malfait, J., Van de Sande, M., Montargès, M., Kervella, P., De Ceuster, F., Coenegrachts, A., Millar, T. J., Richards, A. M. S., Decin, L., Gottlieb, C. A., Pinte, C., De Beck, E., Price, D. J., Wong, K. T., Bolte, J., Menten, K. M., Baudry, A., de Koter, A., Etoka, S., Gobrecht, D., Gray, M., Herpin, F., Jeste, M., Lagadec, E., Maes, S., McDonald, I., Marinho, L., Müller, H. S. P., Pimpanuwat, B., Plane, J. M. C., Sahai, R., Wallström, S. H. J., Yates, J., and Zijlstra, A.
Subjects: Astrophysics - Solar and Stellar Astrophysics
Abstract: Binary interactions have been proposed to explain a variety of circumstellar structures seen around evolved stars, including asymptotic giant branch (AGB) stars and planetary nebulae. Studies resolving the circumstellar envelopes of AGB stars have revealed spirals, discs and bipolar outflows, with shaping attributed to interactions with a companion. For the first time, we have used a combined chemical and dynamical analysis to reveal a highly eccentric and long-period orbit for W Aquilae, a binary system containing an AGB star and a main sequence companion. Our results are based on anisotropic SiN emission, the first detections of NS and SiC towards an S-type star, and density structures observed in the CO emission. These features are all interpreted as having formed during periastron interactions. Our astrochemistry-based method can yield stringent constraints on the orbital parameters of long-period binaries containing AGB stars, and will be applicable to other systems., Comment: This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature's AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1038/s41550-023-02154-y
Published: 2024
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27. Supernova Pointing Capabilities of DUNE

Author: DUNE Collaboration, Abud, A. Abed, Abi, B., Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adamowski, M., Adams, D., Adinolfi, M., Adriano, C., Aduszkiewicz, A., Aguilar, J., Aimard, B., Akbar, F., Allison, K., Monsalve, S. Alonso, Alrashed, M., Alton, A., Alvarez, R., Alves, T., Amar, H., Amedo, P., Anderson, J., Andrade, D. A., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Ankowski, A., Antoniassi, M., Antonova, M., Antoshkin, A., Aranda-Fernandez, A., Arellano, L., Diaz, E. Arrieta, Arroyave, M. A., Asaadi, J., Ashkenazi, A., Asner, D., Asquith, L., Atkin, E., Auguste, D., Aurisano, A., Aushev, V., Autiero, D., Azfar, F., Back, A., Back, H., Back, J. J., Bagaturia, I., Bagby, L., Balashov, N., Balasubramanian, S., Baldi, P., Baldini, W., Baldonedo, J., Baller, B., Bambah, B., Banerjee, R., Barao, F., Barenboim, G., Alzás, P. Barham, Barker, G. J., Barkhouse, W., Barr, G., Monarca, J. 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Subjects: High Energy Physics - Experiment, Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Solar and Stellar Astrophysics, Nuclear Experiment, Physics - Instrumentation and Detectors
Abstract: The determination of the direction of a stellar core collapse via its neutrino emission is crucial for the identification of the progenitor for a multimessenger follow-up. A highly effective method of reconstructing supernova directions within the Deep Underground Neutrino Experiment (DUNE) is introduced. The supernova neutrino pointing resolution is studied by simulating and reconstructing electron-neutrino charged-current absorption on $^{40}$Ar and elastic scattering of neutrinos on electrons. Procedures to reconstruct individual interactions, including a newly developed technique called ``brems flipping'', as well as the burst direction from an ensemble of interactions are described. Performance of the burst direction reconstruction is evaluated for supernovae happening at a distance of 10 kpc for a specific supernova burst flux model. The pointing resolution is found to be 3.4 degrees at 68% coverage for a perfect interaction-channel classification and a fiducial mass of 40 kton, and 6.6 degrees for a 10 kton fiducial mass respectively. Assuming a 4% rate of charged-current interactions being misidentified as elastic scattering, DUNE's burst pointing resolution is found to be 4.3 degrees (8.7 degrees) at 68% coverage., Comment: 25 pages, 16 figures
Published: 2024

28. Histopathological Image Classification with Cell Morphology Aware Deep Neural Networks

Author: Ignatov, Andrey, Yates, Josephine, and Boeva, Valentina
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods
Abstract: Histopathological images are widely used for the analysis of diseased (tumor) tissues and patient treatment selection. While the majority of microscopy image processing was previously done manually by pathologists, recent advances in computer vision allow for accurate recognition of lesion regions with deep learning-based solutions. Such models, however, usually require extensive annotated datasets for training, which is often not the case in the considered task, where the number of available patient data samples is very limited. To deal with this problem, we propose a novel DeepCMorph model pre-trained to learn cell morphology and identify a large number of different cancer types. The model consists of two modules: the first one performs cell nuclei segmentation and annotates each cell type, and is trained on a combination of 8 publicly available datasets to ensure its high generalizability and robustness. The second module combines the obtained segmentation map with the original microscopy image and is trained for the downstream task. We pre-trained this module on the Pan-Cancer TCGA dataset consisting of over 270K tissue patches extracted from 8736 diagnostic slides from 7175 patients. The proposed solution achieved a new state-of-the-art performance on the dataset under consideration, detecting 32 cancer types with over 82% accuracy and outperforming all previously proposed solutions by more than 4%. We demonstrate that the resulting pre-trained model can be easily fine-tuned on smaller microscopy datasets, yielding superior results compared to the current top solutions and models initialized with ImageNet weights. The codes and pre-trained models presented in this paper are available at: https://github.com/aiff22/DeepCMorph
Published: 2024

29. Demonstration of neutron identification in neutrino interactions in the MicroBooNE liquid argon time projection chamber

Author: MicroBooNE collaboration, Abratenko, P., Alterkait, O., Aldana, D. Andrade, Arellano, L., Asaadi, J., Ashkenazi, A., Balasubramanian, S., Baller, B., Barnard, A., Barr, G., Barrow, D., Barrow, J., Basque, V., Bateman, J., Rodrigues, O. Benevides, Berkman, S., Bhanderi, A., Bhat, A., Bhattacharya, M., Bishai, M., Blake, A., Bogart, B., Bolton, T., Book, J. Y., Brunetti, M. B., Camilleri, L., Cao, Y., Caratelli, D., Cavanna, F., Cerati, G., Chappell, A., Chen, Y., Conrad, J. M., Convery, M., Cooper-Troendle, L., Crespo-Anadon, J. I., Cross, R., Del Tutto, M., Dennis, S. R., Detje, P., Diurba, R., Djurcic, Z., Dorrill, R., Duffy, K., Dytman, S., Eberly, B., Englezos, P., Ereditato, A., Evans, J. J., Fine, R., Fleming, B. T., Foreman, W., Franco, D., Furmanski, A. P., Gao, F., Garcia-Gamez, D., Gardiner, S., Ge, G., Gollapinni, S., Gramellini, E., Green, P., Greenlee, H., Gu, L., Gu, W., Guenette, R., Guzowski, P., Hagaman, L., Handley, M. D., Hen, O., Hilgenberg, C., Horton-Smith, G. A., Imani, Z., Irwin, B., Ismail, M. S., James, C., Ji, X., Jo, J. H., Johnson, R. A., Jwa, Y. J., Kalra, D., Kamp, N., Karagiorgi, G., Ketchum, W., Kirby, M., Kobilarcik, T., Kreslo, I., Lane, N., Li, J. -Y., Li, Y., Lin, K., Littlejohn, B. R., Liu, H., Louis, W. C., Luo, X., Mariani, C., Marsden, D., Marshall, J., Martinez, N., Caicedo, D. A. Martinez, Martynenko, S., Mastbaum, A., Mawby, I., McConkey, N., Meddage, V., Mendez, J., Micallef, J., Miller, K., Mistry, K., Mohayai, T., Mogan, A., Mooney, M., Moor, A. F., Moore, C. D., Lepin, L. Mora, Moudgalya, M. M., Babu, S. Mulleria, Naples, D., Navrer-Agasson, A., Nayak, N., Nebot-Guinot, M., Nguyen, C., Nowak, J., Oza, N., Palamara, O., Pallat, N., Paolone, V., Papadopoulou, A., Papavassiliou, V., Parkinson, H., Pate, S. F., Patel, N., Pavlovic, Z., Piasetzky, E., Pletcher, K., Pophale, I., Qian, X., Raaf, J. L., Radeka, V., Rafique, A., Reggiani-Guzzo, M., Ren, L., Rochester, L., Rondon, J. Rodriguez, Rosenberg, M., Ross-Lonergan, M., Safa, I., Schmitz, D. W., Schukraft, A., Seligman, W., Shaevitz, M. H., Sharankova, R., Shi, J., Snider, E. L., Soderberg, M., Soldner-Rembold, S., Spitz, J., Stancari, M., John, J. St., Strauss, T., Szelc, A. M., Tang, W., Taniuchi, N., Terao, K., Thorpe, C., Torbunov, D., Totani, D., Toups, M., Trettin, A., Tsai, Y. -T., Tyler, J., Uchida, M. A., Usher, T., Viren, B., Wang, J., Weber, M., Wei, H., White, A. J., Wolbers, S., Wongjirad, T., Wospakrik, M., Wresilo, K., Wu, W., Yandel, E., Yang, T., Yates, L. E., Yu, H. W., Zeller, G. P., Zennamo, J., and Zhang, C.
Subjects: High Energy Physics - Experiment
Abstract: A significant challenge in measurements of neutrino oscillations is reconstructing the incoming neutrino energies. While modern fully-active tracking calorimeters such as liquid argon time projection chambers in principle allow the measurement of all final state particles above some detection threshold, undetected neutrons remain a considerable source of missing energy with little to no data constraining their production rates and kinematics. We present the first demonstration of tagging neutrino-induced neutrons in liquid argon time projection chambers using secondary protons emitted from neutron-argon interactions in the MicroBooNE detector. We describe the method developed to identify neutrino-induced neutrons and demonstrate its performance using neutrons produced in muon-neutrino charged current interactions. The method is validated using a small subset of MicroBooNE's total dataset. The selection yields a sample with $60\%$ of selected tracks corresponding to neutron-induced secondary protons.
Published: 2024

30. Improving neutrino energy estimation of charged-current interaction events with recurrent neural networks in MicroBooNE

Author: MicroBooNE collaboration, Abratenko, P., Alterkait, O., Aldana, D. Andrade, Arellano, L., Asaadi, J., Ashkenazi, A., Balasubramanian, S., Baller, B., Barnard, A., Barr, G., Barrow, D., Barrow, J., Basque, V., Bateman, J., Rodrigues, O. Benevides, Berkman, S., Bhanderi, A., Bhat, A., Bhattacharya, M., Bishai, M., Blake, A., Bogart, B., Bolton, T., Book, J. Y., Brunetti, M. B., Camilleri, L., Cao, Y., Caratelli, D., Cavanna, F., Cerati, G., Chappell, A., Chen, Y., Conrad, J. M., Convery, M., Cooper-Troendle, L., Crespo-Anadon, J. I., Cross, R., Del Tutto, M., Dennis, S. R., Detje, P., Diurba, R., Djurcic, Z., Dorrill, R., Duffy, K., Dytman, S., Eberly, B., Englezos, P., Ereditato, A., Evans, J. J., Fine, R., Fleming, B. T., Foreman, W., Franco, D., Furmanski, A. P., Gao, F., Garcia-Gamez, D., Gardiner, S., Ge, G., Gollapinni, S., Gramellini, E., Green, P., Greenlee, H., Gu, L., Gu, W., Guenette, R., Guzowski, P., Hagaman, L., Hen, O., Hilgenberg, C., Horton-Smith, G. A., Imani, Z., Irwin, B., Ismail, M. S., James, C., Ji, X., Jo, J. H., Johnson, R. A., Jwa, Y. J., Kalra, D., Kamp, N., Karagiorgi, G., Ketchum, W., Kirby, M., Kobilarcik, T., Kreslo, I., Lane, N., Lepetic, I., Li, J. -Y., Li, Y., Lin, K., Littlejohn, B. R., Liu, H., Louis, W. C., Luo, X., Mariani, C., Marsden, D., Marshall, J., Martinez, N., Caicedo, D. A. Martinez, Martynenko, S., Mastbaum, A., Mawby, I., McConkey, N., Meddage, V., Mendez, J., Micallef, J., Miller, K., Mistry, K., Mohayai, T., Mogan, A., Mooney, M., Moor, A. F., Moore, C. D., Lepin, L. Mora, Moudgalya, M. M., Babu, S. Mulleria, Naples, D., Navrer-Agasson, A., Nayak, N., Nebot-Guinot, M., Nowak, J., Oza, N., Palamara, O., Pallat, N., Paolone, V., Papadopoulou, A., Papavassiliou, V., Parkinson, H., Pate, S. F., Patel, N., Pavlovic, Z., Piasetzky, E., Pletcher, K., Pophale, I., Qian, X., Raaf, J. L., Radeka, V., Rafique, A., Reggiani-Guzzo, M., Ren, L., Rochester, L., Rondon, J. Rodriguez, Rosenberg, M., Ross-Lonergan, M., Safa, I., Scanavini, G., Schmitz, D. W., Schukraft, A., Seligman, W., Shaevitz, M. H., Sharankova, R., Shi, J., Snider, E. L., Soderberg, M., Soldner-Rembold, S., Spitz, J., Stancari, M., John, J. St., Strauss, T., Szelc, A. M., Tang, W., Taniuchi, N., Terao, K., Thorpe, C., Torbunov, D., Totani, D., Toups, M., Trettin, A., Tsai, Y. -T., Tyler, J., Uchida, M. A., Usher, T., Viren, B., Weber, M., Wei, H., White, A. J., Wolbers, S., Wongjirad, T., Wospakrik, M., Wresilo, K., Wu, W., Yandel, E., Yang, T., Yates, L. E., Yu, H. W., Zeller, G. P., Zennamo, J., and Zhang, C.
Subjects: High Energy Physics - Experiment, Physics - Instrumentation and Detectors
Abstract: We present a deep learning-based method for estimating the neutrino energy of charged-current neutrino-argon interactions. We employ a recurrent neural network (RNN) architecture for neutrino energy estimation in the MicroBooNE experiment, utilizing liquid argon time projection chamber (LArTPC) detector technology. Traditional energy estimation approaches in LArTPCs, which largely rely on reconstructing and summing visible energies, often experience sizable biases and resolution smearing because of the complex nature of neutrino interactions and the detector response. The estimation of neutrino energy can be improved after considering the kinematics information of reconstructed final-state particles. Utilizing kinematic information of reconstructed particles, the deep learning-based approach shows improved resolution and reduced bias for the muon neutrino Monte Carlo simulation sample compared to the traditional approach. In order to address the common concern about the effectiveness of this method on experimental data, the RNN-based energy estimator is further examined and validated with dedicated data-simulation consistency tests using MicroBooNE data. We also assess its potential impact on a neutrino oscillation study after accounting for all statistical and systematic uncertainties and show that it enhances physics sensitivity. This method has good potential to improve the performance of other physics analyses.
Published: 2024

31. Scintillation Light in SBND: Simulation, Reconstruction, and Expected Performance of the Photon Detection System

Author: SBND Collaboration, Abratenko, P., Acciarri, R., Adams, C., Aliaga-Soplin, L., Alterkait, O., Alvarez-Garrote, R., Andreopoulos, C., Antonakis, A., Arellano, L., Asaadi, J., Badgett, W., Balasubramanian, S., Basque, V., Beever, A., Behera, B., Belchior, E., Betancourt, M., Bhat, A., Bishai, M., Blake, A., Bogart, B., Bogenschuetz, J., Brailsford, D., Brandt, A., Brickner, S., Bueno, A., Camilleri, L., Caratelli, D., Carber, D., Carlson, B., Carneiro, M., Castillo, R., Cavanna, F., Chen, H., Chung, S., Cicala, M. F., Coackley, R., Crespo-Anadón, J. I., Cuesta, C., Dalager, O., Darby, R., Del Tutto, M., Di Benedetto, V., Djurcic, Z., Duffy, K., Dytman, S., Ereditato, A., Evans, J. J., Ezeribe, A., Fan, C., Filkins, A., Fleming, B., Foreman, W., Franco, D., Furic, I., Furmanski, A., Gao, S., Garcia-Gamez, D., Gardiner, S., Ge, G., Gil-Botella, I., Gollapinni, S., Green, P., Griffith, W. C., Guenette, R., Guzowski, P., Hagaman, L., Hamer, A., Hamilton, P., Hernandez-Morquecho, M., Hilgenberg, C., Howard, B., Imani, Z., James, C., Jones, R. S., Jung, M., Junk, T., Kalra, D., Karagiorgi, G., Kelly, K., Ketchum, W., King, M., Klein, J., Kotsiopoulou, L., Kroupová, T., Kudryavtsev, V. A., Larkin, J., Lay, H., LaZur, R., Li, J. -Y., Lin, K., Littlejohn, B., Louis, W. C., Luo, X., Machado, A., Machado, P., Mariani, C., Marinho, F., Mastbaum, A., Mavrokoridis, K., McConkey, N., McCusker, B., Meddage, V., Mendez, D., Mooney, M., Moor, A. F., Moura, C. A., Mulleriababu, S., Navrer-Agasson, A., Nebot-Guinot, M., Nguyen, V. C. L., Nicolas-Arnaldos, F., Nowak, J., Oh, S., Oza, N., Palamara, O., Pallat, N., Pandey, V., Papadopoulou, A., Parkinson, H. B., Paton, J., Paulucci, L., Pavlovic, Z., Payne, D., Pelegrina-Gutiérrez, L., Pimentel, V. L., Plows, J., Psihas, F., Putnam, G., Qian, X., Rajagopalan, R., Ratoff, P., Ray, H., Reggiani-Guzzo, M., Roda, M., Ross-Lonergan, M., Safa, I., Sanchez-Castillo, A., Sanchez-Lucas, P., Schmitz, D. W., Schneider, A., Schukraft, A., Scott, H., Segreto, E., Sensenig, J., Shaevitz, M., Slater, B., Soares-Nunes, M., Soderberg, M., Söldner-Rembold, S., Spitz, J., Spooner, N. J. C., Stancari, M., Stenico, G. V., Strauss, T., Szelc, A. M., Totani, D., Toups, M., Touramanis, C., Tung, L., Valdiviesso, G. A., Van de Water, R. G., Vázquez-Ramos, A., Wan, L., Weber, M., Wei, H., Wester, T., White, A., Wilkinson, A., Wilson, P., Wongjirad, T., Worcester, E., Worcester, M., Yadav, S., Yandel, E., Yang, T., Yates, L., Yu, B., Yu, J., Zamorano, B., Zennamo, J., and Zhang, C.
Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract: SBND is the near detector of the Short-Baseline Neutrino program at Fermilab. Its location near to the Booster Neutrino Beam source and relatively large mass will allow the study of neutrino interactions on argon with unprecedented statistics. This paper describes the expected performance of the SBND photon detection system, using a simulated sample of beam neutrinos and cosmogenic particles. Its design is a dual readout concept combining a system of 120 photomultiplier tubes, used for triggering, with a system of 192 X-ARAPUCA devices, located behind the anode wire planes. Furthermore, covering the cathode plane with highly-reflective panels coated with a wavelength-shifting compound recovers part of the light emitted towards the cathode, where no optical detectors exist. We show how this new design provides a high light yield and a more uniform detection efficiency, an excellent timing resolution and an independent 3D-position reconstruction using only the scintillation light. Finally, the whole reconstruction chain is applied to recover the temporal structure of the beam spill, which is resolved with a resolution on the order of nanoseconds., Comment: 21 pages, 17 figures
Published: 2024
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32. Single‐Cell Patch‐Clamp/Proteomics of Human Alzheimer's Disease iPSC‐Derived Excitatory Neurons Versus Isogenic Wild‐Type Controls Suggests Novel Causation and Therapeutic Targets

Author: Ghatak, Swagata, Diedrich, Jolene K, Talantova, Maria, Bhadra, Nivedita, Scott, Henry, Sharma, Meetal, Albertolle, Matthew, Schork, Nicholas J, Yates, John R, and Lipton, Stuart A
Subjects: Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Neurosciences, Biological Sciences, Stem Cell Research - Induced Pluripotent Stem Cell, Acquired Cognitive Impairment, Alzheimer's Disease, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Neurodegenerative, Stem Cell Research, Aging, Brain Disorders, Dementia, Biotechnology, Stem Cell Research - Induced Pluripotent Stem Cell - Human, 2.1 Biological and endogenous factors, Neurological, Alzheimer Disease, Humans, Induced Pluripotent Stem Cells, Proteomics, Neurons, Patch-Clamp Techniques, Single-Cell Analysis, Alzheimer's disease, hiPSC-derived neurons, patch clamp electrophysiology, single-cell proteomics, hiPSC‐derived neurons, patch clamp electrophysiology, single‐cell proteomics
Abstract: Standard single-cell (sc) proteomics of disease states inferred from multicellular organs or organoids cannot currently be related to single-cell physiology. Here, a scPatch-Clamp/Proteomics platform is developed on single neurons generated from hiPSCs bearing an Alzheimer's disease (AD) genetic mutation and compares them to isogenic wild-type controls. This approach provides both current and voltage electrophysiological data plus detailed proteomics information on single-cells. With this new method, the authors are able to observe hyperelectrical activity in the AD hiPSC-neurons, similar to that observed in the human AD brain, and correlate it to ≈1400 proteins detected at the single neuron level. Using linear regression and mediation analyses to explore the relationship between the abundance of individual proteins and the neuron's mutational and electrophysiological status, this approach yields new information on therapeutic targets in excitatory neurons not attainable by traditional methods. This combined patch-proteomics technique creates a new proteogenetic-therapeutic strategy to correlate genotypic alterations to physiology with protein expression in single-cells.
Published: 2024

33. EDGE: A new model for Nuclear Star Cluster formation in dwarf galaxies

Author: Gray, Emily I., Read, Justin I., Taylor, Ethan, Orkney, Matthew D. A., Rey, Martin P., Yates, Robert M., Kim, Stacy Y., Noël, Noelia E. D., Agertz, Oscar, Andersson, Eric, and Pontzen, Andrew
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: Nuclear Star Clusters (NSCs) are amongst the densest stellar systems in the Universe and are found at the centres of many bright spiral and elliptical galaxies, and up to ${\sim}$40% of dwarf galaxies. However, their formation mechanisms, and possible links to globular clusters (GCs), remain debated. This paper uses the EDGE simulations - a collection of zoom-in, cosmological simulations of isolated dwarf galaxies -- to present a new formation mechanism for NSCs. We find that, at a gas spatial and mass resolution of ${\sim}3\,$pc and ${\sim}161$ M$_\odot$, respectively, NSCs naturally emerge in a subset of our EDGE dwarfs with redshift-zero halo masses of $\rm{M}_{\rm{r}200\rm{c}} \sim 5 \times 10^9$ M$_\odot$. These dwarfs are quenched by reionisation, but retain a significant reservoir of gas that is unable to cool and form stars. Sometime after reionisation, the dwarfs then undergo a major (${\sim}$1:1) merger that excites rapid gas cooling, leading to a significant starburst. An NSC forms in this starburst that then quenches star formation thereafter. The result is a nucleated dwarf that has two stellar populations with distinct age: one pre-reionisation and one post-reionisation. Our mechanism is unique for two key reasons. Firstly, the low mass of the host dwarf means that NSCs, formed in this way, can accrete onto galaxies of almost all masses, potentially seeding the formation of NSCs everywhere. Secondly, our model predicts that NSCs should have at least two stellar populations with a large ($\gtrsim$1 billion year) age separation. This yields a predicted colour magnitude diagram for our nucleated dwarfs that has two distinct main sequence turnoffs. Several GCs orbiting the Milky Way, including Omega Centauri and M54, show exactly this behaviour, suggesting that they may, in fact, be accreted NSCs., Comment: Main text 12 pages, 8 figures. Submitted to MNRAS
Published: 2024

34. Poisson Variational Autoencoder

Author: Vafaii, Hadi, Galor, Dekel, and Yates, Jacob L.
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Quantitative Biology - Neurons and Cognition
Abstract: Variational autoencoders (VAE) employ Bayesian inference to interpret sensory inputs, mirroring processes that occur in primate vision across both ventral (Higgins et al., 2021) and dorsal (Vafaii et al., 2023) pathways. Despite their success, traditional VAEs rely on continuous latent variables, which deviates sharply from the discrete nature of biological neurons. Here, we developed the Poisson VAE (P-VAE), a novel architecture that combines principles of predictive coding with a VAE that encodes inputs into discrete spike counts. Combining Poisson-distributed latent variables with predictive coding introduces a metabolic cost term in the model loss function, suggesting a relationship with sparse coding which we verify empirically. Additionally, we analyze the geometry of learned representations, contrasting the P-VAE to alternative VAE models. We find that the P-VAEencodes its inputs in relatively higher dimensions, facilitating linear separability of categories in a downstream classification task with a much better (5x) sample efficiency. Our work provides an interpretable computational framework to study brain-like sensory processing and paves the way for a deeper understanding of perception as an inferential process.
Published: 2024

35. Single Aperture Large Telescope for Universe Studies (SALTUS): Science Overview

Author: Chin, Gordon, Anderson, Carrie M., Bergner, Jennifer, Biver, Nicolas, Bjoraker, Gordon L., Cavalie, Thibault, DiSanti, Michael, Gao, Jian-Rong, Hartogh, Paul, Harding, Leon K., Hu, Qing, Kim, Daewook, Kulesa, Craig, de Lange, Gert, Leisawitz, David T., Levy, Rebecca C., Lichtenberger, Arthur, Marronh, Daniel P., Najita, Joan, Newswander, Trent, Rieke, George H., Rigopoulou, Dimitra, Roefsema, Peter, Roth, Nathan X., Schwarz, Kamber, Shirley, Yancy, Spilker, Justin, Stark, Antony A., van der Tak, Floris, Takashima, Yuzuru, Tielens, Alexander, Willner, David J., Wollack, Edward J., Yates, Stephen, Young, Erick, and Walker, Christopher K.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: The SALTUS Probe mission will provide a powerful far-infrared (far-IR) pointed space observatory to explore our cosmic origins and the possibility of life elsewhere. The observatory employs an innovative deployable 14-m aperture, with a sunshield that will radiatively cool the off-axis primary to <45K. This cooled primary reflector works in tandem with cryogenic coherent and incoherent instruments that span the 34 to 660 micron far-IR range at both high and moderate spectral resolutions., Comment: 49 pages, 10 figures, 3 tables, submitted to SPIE JATIS
Published: 2024

36. A Principled Approach for a New Bias Measure

Author: Scarone, Bruno, Viola, Alfredo, Miller, Renée J., and Baeza-Yates, Ricardo
Subjects: Computer Science - Machine Learning, Computer Science - Computers and Society
Abstract: The widespread use of machine learning and data-driven algorithms for decision making has been steadily increasing over many years. The areas in which this is happening are diverse: healthcare, employment, finance, education, the legal system to name a few; and the associated negative side effects are being increasingly harmful for society. Negative data \emph{bias} is one of those, which tends to result in harmful consequences for specific groups of people. Any mitigation strategy or effective policy that addresses the negative consequences of bias must start with awareness that bias exists, together with a way to understand and quantify it. However, there is a lack of consensus on how to measure data bias and oftentimes the intended meaning is context dependent and not uniform within the research community. The main contributions of our work are: (1) The definition of Uniform Bias (UB), the first bias measure with a clear and simple interpretation in the full range of bias values. (2) A systematic study to characterize the flaws of existing measures in the context of anti employment discrimination rules used by the Office of Federal Contract Compliance Programs, additionally showing how UB solves open problems in this domain. (3) A framework that provides an efficient way to derive a mathematical formula for a bias measure based on an algorithmic specification of bias addition. Our results are experimentally validated using nine publicly available datasets and theoretically analyzed, which provide novel insights about the problem. Based on our approach, we also design a bias mitigation model that might be useful to policymakers.
Published: 2024

37. Timely Offloading in Mobile Edge Cloud Systems

Author: Sathyavageeswaran, Nitya, Yates, Roy D., Sarwate, Anand D., and Mandayam, Narayan
Subjects: Electrical Engineering and Systems Science - Systems and Control
Abstract: Future real-time applications like smart cities will use complex Machine Learning (ML) models for a variety of tasks. Timely status information is required for these applications to be reliable. Offloading computation to a mobile edge cloud (MEC) can reduce the completion time of these tasks. However, using the MEC may come at a cost such as related to use of a cloud service or privacy. In this paper, we consider a source that generates time-stamped status updates for delivery to a monitor after processing by the mobile device or MEC. We study how a scheduler must forward these updates to achieve timely updates at the monitor but also limit MEC usage. We measure timeliness at the monitor using the age of information (AoI) metric. We formulate this problem as an infinite horizon Markov decision process (MDP) with an average cost criterion. We prove that an optimal scheduling policy has an age-threshold structure that depends on how long an update has been in service.
Published: 2024

38. Correcting for the overabundance of low-mass quiescent galaxies in semi-analytic models

Author: Harrold, Jimi E., Almaini, Omar, Pearce, Frazer R., and Yates, Robert M.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: We compare the L-Galaxies semi-analytic model to deep observational data from the UKIDSS Ultra Deep Survey (UDS) across the redshift range $0.5 < z < 3$. We find that the over-abundance of low-mass, passive galaxies at high redshifts in the model can be attributed solely to the properties of `orphan' galaxies, i.e. satellite galaxies where the simulation has lost track of the host dark matter subhalo. We implement a simple model that boosts the star-formation rates in orphan galaxies by matching them to non-orphaned satellite galaxies at a similar evolutionary stage. This straightforward change largely addresses the discrepancy in the low-mass passive fraction across all redshifts. We find that the orphan problem is somewhat alleviated by higher resolution simulations, but the preservation of a larger gas reservoir in orphans is still required to produce a better fit to the observed space density of low-mass passive galaxies. Our findings are also robust to the precise definition of the passive galaxy population. In general, considering the vastly different prescriptions used for orphans in semi-analytic models, we recommend that they are analysed separately from the resolved satellite galaxy population, particularly with JWST observations reigniting interest in the low-mass regime in which they dominate., Comment: 7 pages, 2 figures. Accepted for publication in MNRAS
Published: 2024

39. Studying Self-Care with Generative AI Tools: Lessons for Design

Author: Capel, Tara, Ploderer, Bernd, Bircanin, Filip, Hanmer, Simon, Yates, Jamie, Wang, Jiaxuan, Khor, Kai Ling, Leong, Tuck Wah, Wadley, Greg, and Newcomb, Michelle
Subjects: Computer Science - Human-Computer Interaction
Abstract: The rise of generative AI presents new opportunities for the understanding and practice of self-care through its capability to generate varied content, including self-care suggestions via text and images, and engage in dialogue with users over time. However, there are also concerns about accuracy and trustworthiness of self-care advice provided via AI. This paper reports our findings from workshops, diaries, and interviews with five researchers and 24 participants to explore their experiences and use of generative AI for self-care. We analyze our findings to present a framework for the use of generative AI to support five types of self-care, - advice seeking, mentorship, resource creation, social simulation, and therapeutic self-expression - mapped across two dimensions - expertise and modality. We discuss how these practices shift the role of technologies for self-care from merely offering information to offering personalized advice and supporting creativity for reflection, and we offer suggestions for using the framework to investigate new self-care designs., Comment: 28 pages, 4 figures, to be published in the proceedings of the ACM Designing Interactive Systems Conference (DIS '24)
Published: 2024
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40. Are We Really Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation?

Author: Li, Ming, Liu, Yuanna, Jullien, Sami, Ariannezhad, Mozhdeh, Aliannejadi, Mohammad, Yates, Andrew, and de Rijke, Maarten
Subjects: Computer Science - Information Retrieval
Abstract: Next basket recommendation (NBR) is a special type of sequential recommendation that is increasingly receiving attention. So far, most NBR studies have focused on optimizing the accuracy of the recommendation, whereas optimizing for beyond-accuracy metrics, e.g., item fairness and diversity remains largely unexplored. Recent studies into NBR have found a substantial performance difference between recommending repeat items and explore items. Repeat items contribute most of the users' perceived accuracy compared with explore items. Informed by these findings, we identify a potential "short-cut" to optimize for beyond-accuracy metrics while maintaining high accuracy. To leverage and verify the existence of such short-cuts, we propose a plug-and-play two-step repetition-exploration (TREx) framework that treats repeat items and explores items separately, where we design a simple yet highly effective repetition module to ensure high accuracy, while two exploration modules target optimizing only beyond-accuracy metrics. Experiments are performed on two widely-used datasets w.r.t. a range of beyond-accuracy metrics, viz. five fairness metrics and three diversity metrics. Our experimental results verify the effectiveness of TREx. Prima facie, this appears to be good news: we can achieve high accuracy and improved beyond-accuracy metrics at the same time. However, we argue that the real-world value of our algorithmic solution, TREx, is likely to be limited and reflect on the reasonableness of the evaluation setup. We end up challenging existing evaluation paradigms, particularly in the context of beyond-accuracy metrics, and provide insights for researchers to navigate potential pitfalls and determine reasonable metrics to consider when optimizing for accuracy and beyond-accuracy metrics., Comment: To appear at SIGIR'24
Published: 2024
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41. U Can't Gen This? A Survey of Intellectual Property Protection Methods for Data in Generative AI

Author: Šarčević, Tanja, Karlowicz, Alicja, Mayer, Rudolf, Baeza-Yates, Ricardo, and Rauber, Andreas
Subjects: Computer Science - Computers and Society, Computer Science - Artificial Intelligence
Abstract: Large Generative AI (GAI) models have the unparalleled ability to generate text, images, audio, and other forms of media that are increasingly indistinguishable from human-generated content. As these models often train on publicly available data, including copyrighted materials, art and other creative works, they inadvertently risk violating copyright and misappropriation of intellectual property (IP). Due to the rapid development of generative AI technology and pressing ethical considerations from stakeholders, protective mechanisms and techniques are emerging at a high pace but lack systematisation. In this paper, we study the concerns regarding the intellectual property rights of training data and specifically focus on the properties of generative models that enable misuse leading to potential IP violations. Then we propose a taxonomy that leads to a systematic review of technical solutions for safeguarding the data from intellectual property violations in GAI.
Published: 2024

42. Efficient and Timely Memory Access

Author: Ramani, Vishakha, Seskar, Ivan, and Yates, Roy D.
Subjects: Electrical Engineering and Systems Science - Systems and Control, Computer Science - Information Theory
Abstract: This paper investigates the optimization of memory sampling in status updating systems, where source updates are published in shared memory, and reader process samples the memory for source updates by paying a sampling cost. We formulate a discrete-time decision problem to find a sampling policy that minimizes average cost comprising age at the client and the cost incurred due to sampling. We establish that an optimal policy is a stationary and deterministic threshold-type policy, and subsequently derive optimal threshold and the corresponding optimal average cost., Comment: To be presented at ISIT 2024
Published: 2024

43. First double-differential cross section measurement of neutral-current $\pi^0$ production in neutrino-argon scattering in the MicroBooNE detector

Author: MicroBooNE collaboration, Abratenko, P., Alterkait, O., Aldana, D. Andrade, Arellano, L., Asaadi, J., Ashkenazi, A., Balasubramanian, S., Baller, B., Barnard, A., Barr, G., Barrow, D., Barrow, J., Basque, V., Bateman, J., Rodrigues, O. Benevides, Berkman, S., Bhanderi, A., Bhat, A., Bhattacharya, M., Bishai, M., Blake, A., Bogart, B., Bolton, T., Book, J. Y., Brunetti, M. B., Camilleri, L., Cao, Y., Caratelli, D., Cavanna, F., Cerati, G., Chappell, A., Chen, Y., Conrad, J. M., Convery, M., Cooper-Troendle, L., Crespo-Anadon, J. I., Cross, R., Del Tutto, M., Dennis, S. R., Detje, P., Diurba, R., Djurcic, Z., Dorrill, R., Duffy, K., Dytman, S., Eberly, B., Englezos, P., Ereditato, A., Evans, J. J., Fine, R., Fleming, B. T., Foreman, W., Franco, D., Furmanski, A. P., Gao, F., Garcia-Gamez, D., Gardiner, S., Ge, G., Gollapinni, S., Gramellini, E., Green, P., Greenlee, H., Gu, L., Gu, W., Guenette, R., Guzowski, P., Hagaman, L., Handley, M., Hen, O., Hilgenberg, C., Horton-Smith, G. A., Imani, Z., Irwin, B., Ismail, M. S., James, C., Ji, X., Jo, J. H., Johnson, R. A., Jwa, Y. J., Kalra, D., Kamp, N., Karagiorgi, G., Ketchum, W., Kirby, M., Kobilarcik, T., Kreslo, I., Lane, N., Lepetic, I., Li, J. -Y., Li, Y., Lin, K., Littlejohn, B. R., Liu, H., Louis, W. C., Luo, X., Mariani, C., Marsden, D., Marshall, J., Martinez, N., Caicedo, D. A. Martinez, Martynenko, S., Mastbaum, A., Mawby, I., McConkey, N., Meddage, V., Mendez, J., Micallef, J., Miller, K., Mistry, K., Mohayai, T., Mogan, A., Mooney, M., Moor, A. F., Moore, C. D., Lepin, L. Mora, Moudgalya, M. M., Babu, S. Mulleria, Naples, D., Navrer-Agasson, A., Nayak, N., Nebot-Guinot, M., Nowak, J., Oza, N., Palamara, O., Pallat, N., Paolone, V., Papadopoulou, A., Papavassiliou, V., Parkinson, H., Pate, S. F., Patel, N., Pavlovic, Z., Piasetzky, E., Pletcher, K., Pophale, I., Qian, X., Raaf, J. L., Radeka, V., Rafique, A., Reggiani-Guzzo, M., Ren, L., Rochester, L., Rondon, J. Rodriguez, Rosenberg, M., Ross-Lonergan, M., Safa, I., Scanavini, G., Schmitz, D. W., Schukraft, A., Seligman, W., Shaevitz, M. H., Sharankova, R., Shi, J., Snider, E. L., Soderberg, M., Soldner-Rembold, S., Spitz, J., Stancari, M., John, J. St., Strauss, T., Szelc, A. M., Tang, W., Taniuchi, N., Terao, K., Thorpe, C., Torbunov, D., Totani, D., Toups, M., Trettin, A., Tsai, Y. -T., Tyler, J., Uchida, M. A., Usher, T., Viren, B., Wang, J., Weber, M., Wei, H., White, A. J., Wolbers, S., Wongjirad, T., Wospakrik, M., Wresilo, K., Wu, W., Yandel, E., Yang, T., Yates, L. E., Yu, H. W., Zeller, G. P., Zennamo, J., and Zhang, C.
Subjects: High Energy Physics - Experiment
Abstract: We report the first double-differential cross section measurement of neutral-current neutral pion (NC$\pi^0$) production in neutrino-argon scattering, as well as single-differential measurements of the same channel in terms of final states with and without protons. The kinematic variables of interest for these measurements are the $\pi^0$ momentum and the $\pi^0$ scattering angle with respect to the neutrino beam. A total of 4971 candidate NC$\pi^0$ events fully-contained within the MicroBooNE detector are selected using data collected at a mean neutrino energy of $\sim 0.8$~GeV from $6.4\times10^{20}$ protons on target from the Booster Neutrino Beam at the Fermi National Accelerator Laboratory. After extensive data-driven model validation to ensure unbiased unfolding, the Wiener-SVD method is used to extract nominal flux-averaged cross sections. The results are compared to predictions from commonly used neutrino event generators, which tend to overpredict the measured NC$\pi^0$ cross section, especially in the 0.2-0.5~GeV/c $\pi^0$ momentum range and at forward scattering angles. Events with at least one proton present in the final state are also underestimated. This data will help improve the modeling of NC$\pi^0$ production, which represents a major background in measurements of charge-parity violation in the neutrino sector and in searches for new physics beyond the Standard Model.
Published: 2024

44. Modelling adhesion in stochastic and mean-field models of cell migration

Author: Noureen, Shahzeb Raja, Mort, Richard L., and Yates, Christian A.
Subjects: Quantitative Biology - Cell Behavior
Abstract: Adhesion between cells plays an important role in many biological processes such as tissue morphogenesis and homeostasis, wound healing and cancer cell metastasis. From a mathematical perspective, adhesion between multiple cell types has been previously analysed using discrete and continuum models including the Cellular Potts models and partial differential equations (PDEs). While these models can represent certain biological situations well, Cellular Potts models can be computationally expensive and continuum models only capture the macroscopic behaviour of a population of cells, ignoring stochasticity and the discrete nature of cell dynamics. Cellular automaton models allow us to address these problems and can be used for a wide variety of biological systems. In this paper, we consider a cellular automaton approach and develop an on-lattice agent-based model (ABM) for cell migration and adhesion in a population composed of two cell types. By deriving and comparing the corresponding PDEs to the ABM, we demonstrate that cell aggregation and cell sorting are not possible in the PDE model. Therefore, we propose a set of stochastic mean equations (SMEs) which better capture the behaviour of the ABM in one and two dimensions.
Published: 2024

45. Measurement of the differential cross section for neutral pion production in charged-current muon neutrino interactions on argon with the MicroBooNE detector

Author: MicroBooNE collaboration, Abratenko, P., Alterkait, O., Aldana, D. Andrade, Arellano, L., Asaadi, J., Ashkenazi, A., Balasubramanian, S., Baller, B., Barr, G., Barrow, D., Barrow, J., Basque, V., Rodrigues, O. Benevides, Berkman, S., Bhanderi, A., Bhat, A., Bhattacharya, M., Bishai, M., Blake, A., Bogart, B., Bolton, T., Book, J. Y., Brunetti, M. B., Camilleri, L., Cao, Y., Caratelli, D., Cavanna, F., Cerati, G., Chappell, A., Chen, Y., Conrad, J. M., Convery, M., Cooper-Troendle, L., Crespo-Anadon, J. I., Cross, R., Del Tutto, M., Dennis, S. R., Detje, P., Devitt, A., Diurba, R., Djurcic, Z., Dorrill, R., Duffy, K., Dytman, S., Eberly, B., Englezos, P., Ereditato, A., Evans, J. J., Fine, R., Fleming, B. T., Foreman, W., Franco, D., Furmanski, A. P., Gao, F., Garcia-Gamez, D., Gardiner, S., Ge, G., Gollapinni, S., Gramellini, E., Green, P., Greenlee, H., Gu, L., Gu, W., Guenette, R., Guzowski, P., Hagaman, L., Hen, O., Hilgenberg, C., Horton-Smith, G. A., Imani, Z., Irwin, B., Ismail, M. S., James, C., Ji, X., Jo, J. H., Johnson, R. A., Jwa, Y. J., Kalra, D., Kamp, N., Karagiorgi, G., Ketchum, W., Kirby, M., Kobilarcik, T., Kreslo, I., Lane, N., Lepetic, I., Li, J. -Y., Li, Y., Lin, K., Littlejohn, B. R., Liu, H., Louis, W. C., Luo, X., Mariani, C., Marsden, D., Marshall, J., Martinez, N., Caicedo, D. A. Martinez, Martynenko, S., Mastbaum, A., Mawby, I., McConkey, N., Meddage, V., Mendez, J., Micallef, J., Miller, K., Mistry, K., Mohayai, T., Mogan, A., Mooney, M., Moor, A. F., Moore, C. D., Lepin, L. Mora, Moudgalya, M. M., Babu, S. Mulleria, Naples, D., Navrer-Agasson, A., Nayak, N., Nebot-Guinot, M., Nowak, J., Oza, N., Palamara, O., Pallat, N., Paolone, V., Papadopoulou, A., Papavassiliou, V., Parkinson, H., Pate, S. F., Patel, N., Pavlovic, Z., Piasetzky, E., Pletcher, K., Pophale, I., Qian, X., Raaf, J. L., Radeka, V., Rafique, A., Reggiani-Guzzo, M., Ren, L., Rochester, L., Rondon, J. Rodriguez, Rosenberg, M., Ross-Lonergan, M., Safa, I., Scanavini, G., Schmitz, D. W., Schukraft, A., Seligman, W., Shaevitz, M. H., Sharankova, R., Shi, J., Snider, E. L., Soderberg, M., Soldner-Rembold, S., Spitz, J., Stancari, M., John, J. St., Strauss, T., Szelc, A. M., Tang, W., Taniuchi, N., Terao, K., Thorpe, C., Torbunov, D., Totani, D., Toups, M., Trettin, A., Tsai, Y. -T., Tyler, J., Uchida, M. A., Usher, T., Viren, B., Weber, M., Wei, H., White, A. J., Wolbers, S., Wongjirad, T., Wospakrik, M., Wresilo, K., Wu, W., Yandel, E., Yang, T., Yates, L. E., Yu, H. W., Zeller, G. P., Zennamo, J., and Zhang, C.
Subjects: High Energy Physics - Experiment, Physics - Instrumentation and Detectors
Abstract: We present a measurement of neutral pion production in charged-current interactions using data recorded with the MicroBooNE detector exposed to Fermilab's booster neutrino beam. The signal comprises one muon, one neutral pion, any number of nucleons, and no charged pions. Studying neutral pion production in the MicroBooNE detector provides an opportunity to better understand neutrino-argon interactions, and is crucial for future accelerator-based neutrino oscillation experiments. Using a dataset corresponding to $6.86 \times 10^{20}$ protons on target, we present single-differential cross sections in muon and neutral pion momenta, scattering angles with respect to the beam for the outgoing muon and neutral pion, as well as the opening angle between the muon and neutral pion. Data extracted cross sections are compared to generator predictions. We report good agreement between the data and the models for scattering angles, except for an over-prediction by generators at muon forward angles. Similarly, the agreement between data and the models as a function of momentum is good, except for an underprediction by generators in the medium momentum ranges, $200-400$ MeV for muons and $100-200$ MeV for pions.
Published: 2024

46. Noise2Image: Noise-Enabled Static Scene Recovery for Event Cameras

Author: Cao, Ruiming, Galor, Dekel, Kohli, Amit, Yates, Jacob L, and Waller, Laura
Subjects: Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract: Event cameras capture changes of intensity over time as a stream of 'events' and generally cannot measure intensity itself; hence, they are only used for imaging dynamic scenes. However, fluctuations due to random photon arrival inevitably trigger noise events, even for static scenes. While previous efforts have been focused on filtering out these undesirable noise events to improve signal quality, we find that, in the photon-noise regime, these noise events are correlated with the static scene intensity. We analyze the noise event generation and model its relationship to illuminance. Based on this understanding, we propose a method, called Noise2Image, to leverage the illuminance-dependent noise characteristics to recover the static parts of a scene, which are otherwise invisible to event cameras. We experimentally collect a dataset of noise events on static scenes to train and validate Noise2Image. Our results show that Noise2Image can robustly recover intensity images solely from noise events, providing a novel approach for capturing static scenes in event cameras, without additional hardware.
Published: 2024

47. Measurement of double-differential cross sections for mesonless charged-current muon neutrino interactions on argon with final-state protons using the MicroBooNE detector

Author: MicroBooNE collaboration, Abratenko, P., Alterkait, O., Aldana, D. Andrade, Arellano, L., Asaadi, J., Ashkenazi, A., Balasubramanian, S., Baller, B., Barr, G., Barrow, D., Barrow, J., Basque, V., Rodrigues, O. Benevides, Berkman, S., Bhanderi, A., Bhat, A., Bhattacharya, M., Bishai, M., Blake, A., Bogart, B., Bolton, T., Book, J. Y., Brunetti, M. B., Camilleri, L., Cao, Y., Caratelli, D., Cavanna, F., Cerati, G., Chappell, A., Chen, Y., Conrad, J. M., Convery, M., Cooper-Troendle, L., Crespo-Anadon, J. I., Cross, R., Del Tutto, M., Dennis, S. R., Detje, P., Devitt, A., Diurba, R., Djurcic, Z., Dorrill, R., Duffy, K., Dytman, S., Eberly, B., Englezos, P., Ereditato, A., Evans, J. J., Fine, R., Fleming, B. T., Foreman, W., Franco, D., Furmanski, A. P., Gao, F., Garcia-Gamez, D., Gardiner, S., Ge, G., Gollapinni, S., Gramellini, E., Green, P., Greenlee, H., Gu, L., Gu, W., Guenette, R., Guzowski, P., Hagaman, L., Hen, O., Hilgenberg, C., Horton-Smith, G. A., Imani, Z., Irwin, B., Ismail, M. S., James, C., Ji, X., Jo, J. H., Johnson, R. A., Jwa, Y. J., Kalra, D., Kamp, N., Karagiorgi, G., Ketchum, W., Kirby, M., Kobilarcik, T., Kreslo, I., Lane, N., Lepetic, I., Li, J. -Y., Li, Y., Lin, K., Littlejohn, B. R., Liu, H., Louis, W. C., Luo, X., Mariani, C., Marsden, D., Marshall, J., Martinez, N., Caicedo, D. A. Martinez, Martynenko, S., Mastbaum, A., Mawby, I., McConkey, N., Meddage, V., Mendez, J., Micallef, J., Miller, K., Mistry, K., Mohayai, T., Mogan, A., Mooney, M., Moor, A. F., Moore, C. D., Lepin, L. Mora, Moudgalya, M. M., Babu, S. Mulleria, Naples, D., Navrer-Agasson, A., Nayak, N., Nebot-Guinot, M., Nowak, J., Oza, N., Palamara, O., Pallat, N., Paolone, V., Papadopoulou, A., Papavassiliou, V., Parkinson, H., Pate, S. F., Patel, N., Pavlovic, Z., Piasetzky, E., Pletcher, K., Pophale, I., Qian, X., Raaf, J. L., Radeka, V., Rafique, A., Reggiani-Guzzo, M., Ren, L., Rochester, L., Rondon, J. Rodriguez, Rosenberg, M., Ross-Lonergan, M., Safa, I., Scanavini, G., Schmitz, D. W., Schukraft, A., Seligman, W., Shaevitz, M. H., Sharankova, R., Shi, J., Snider, E. L., Soderberg, M., Soldner-Rembold, S., Spitz, J., Stancari, M., John, J. St., Strauss, T., Szelc, A. M., Tang, W., Taniuchi, N., Terao, K., Thorpe, C., Torbunov, D., Totani, D., Toups, M., Trettin, A., Tsai, Y. -T., Tyler, J., Uchida, M. A., Usher, T., Viren, B., Weber, M., Wei, H., White, A. J., Wolbers, S., Wongjirad, T., Wospakrik, M., Wresilo, K., Wu, W., Yandel, E., Yang, T., Yates, L. E., Yu, H. W., Zeller, G. P., Zennamo, J., and Zhang, C.
Subjects: High Energy Physics - Experiment
Abstract: Charged-current neutrino interactions with final states containing zero mesons and at least one proton are of high interest for current and future accelerator-based neutrino oscillation experiments. Using the Booster Neutrino Beam and the MicroBooNE detector at Fermi National Accelerator Laboratory, we have obtained the first double-differential cross section measurements of this channel for muon neutrino scattering on an argon target with a proton momentum threshold of 0.25 GeV/c. We also report a flux-averaged total cross section of $\sigma = (11.8 \pm 1.2) \times 10^{-38}$ cm$^2$ / Ar and several single-differential measurements which extend and improve upon previous results. Statistical and systematic uncertainties are quantified with a full treatment of correlations across 359 kinematic bins, including correlations between distributions describing different observables. The resulting data set provides the most detailed information obtained to date for testing models of mesonless neutrino-argon scattering., Comment: 83 pages, 67 figures (including supplemental material). For v2, added oversized files in extended data release
Published: 2024

48. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Author: Gemini Team, Georgiev, Petko, Lei, Ving Ian, Burnell, Ryan, Bai, Libin, Gulati, Anmol, Tanzer, Garrett, Vincent, Damien, Pan, Zhufeng, Wang, Shibo, Mariooryad, Soroosh, Ding, Yifan, Geng, Xinyang, Alcober, Fred, Frostig, Roy, Omernick, Mark, Walker, Lexi, Paduraru, Cosmin, Sorokin, Christina, Tacchetti, Andrea, Gaffney, Colin, Daruki, Samira, Sercinoglu, Olcan, Gleicher, Zach, Love, Juliette, Voigtlaender, Paul, Jain, Rohan, Surita, Gabriela, Mohamed, Kareem, Blevins, Rory, Ahn, Junwhan, Zhu, Tao, Kawintiranon, Kornraphop, Firat, Orhan, Gu, Yiming, Zhang, Yujing, Rahtz, Matthew, Faruqui, Manaal, Clay, Natalie, Gilmer, Justin, Co-Reyes, JD, Penchev, Ivo, Zhu, Rui, Morioka, Nobuyuki, Hui, Kevin, Haridasan, Krishna, Campos, Victor, Mahdieh, Mahdis, Guo, Mandy, Hassan, Samer, Kilgour, Kevin, Vezer, Arpi, Cheng, Heng-Tze, de Liedekerke, Raoul, Goyal, Siddharth, Barham, Paul, Strouse, DJ, Noury, Seb, Adler, Jonas, Sundararajan, Mukund, Vikram, Sharad, Lepikhin, Dmitry, Paganini, Michela, Garcia, Xavier, Yang, Fan, Valter, Dasha, Trebacz, Maja, Vodrahalli, Kiran, Asawaroengchai, Chulayuth, Ring, Roman, Kalb, Norbert, Soares, Livio Baldini, Brahma, Siddhartha, Steiner, David, Yu, Tianhe, Mentzer, Fabian, He, Antoine, Gonzalez, Lucas, Xu, Bibo, Kaufman, Raphael Lopez, Shafey, Laurent El, Oh, Junhyuk, Hennigan, Tom, Driessche, George van den, Odoom, Seth, Lucic, Mario, Roelofs, Becca, Lall, Sid, Marathe, Amit, Chan, Betty, Ontanon, Santiago, He, Luheng, Teplyashin, Denis, Lai, Jonathan, Crone, Phil, Damoc, Bogdan, Ho, Lewis, Riedel, Sebastian, Lenc, Karel, Yeh, Chih-Kuan, Chowdhery, Aakanksha, Xu, Yang, Kazemi, Mehran, Amid, Ehsan, Petrushkina, Anastasia, Swersky, Kevin, Khodaei, Ali, Chen, Gowoon, Larkin, Chris, Pinto, Mario, Yan, Geng, Badia, Adria Puigdomenech, Patil, Piyush, Hansen, Steven, Orr, Dave, Arnold, Sebastien M. R., Grimstad, Jordan, Dai, Andrew, Douglas, Sholto, Sinha, Rishika, Yadav, Vikas, Chen, Xi, Gribovskaya, Elena, Austin, Jacob, Zhao, Jeffrey, Patel, Kaushal, Komarek, Paul, Austin, Sophia, Borgeaud, Sebastian, Friso, Linda, Goyal, Abhimanyu, Caine, Ben, Cao, Kris, Chung, Da-Woon, Lamm, Matthew, Barth-Maron, Gabe, Kagohara, Thais, Olszewska, Kate, Chen, Mia, Shivakumar, Kaushik, Agarwal, Rishabh, Godhia, Harshal, Rajwar, Ravi, Snaider, Javier, Dotiwalla, Xerxes, Liu, Yuan, Barua, Aditya, Ungureanu, Victor, Zhang, Yuan, Batsaikhan, Bat-Orgil, Wirth, Mateo, Qin, James, Danihelka, Ivo, Doshi, Tulsee, Chadwick, Martin, Chen, Jilin, Jain, Sanil, Le, Quoc, Kar, Arjun, Gurumurthy, Madhu, Li, Cheng, Sang, Ruoxin, Liu, Fangyu, Lamprou, Lampros, Munoz, Rich, Lintz, Nathan, Mehta, Harsh, Howard, Heidi, Reynolds, Malcolm, Aroyo, Lora, Wang, Quan, Blanco, Lorenzo, Cassirer, Albin, Griffith, Jordan, Das, Dipanjan, Lee, Stephan, Sygnowski, Jakub, Fisher, Zach, Besley, James, Powell, Richard, Ahmed, Zafarali, Paulus, Dominik, Reitter, David, Borsos, Zalan, Joshi, Rishabh, Pope, Aedan, Hand, Steven, Selo, Vittorio, Jain, Vihan, Sethi, Nikhil, Goel, Megha, Makino, Takaki, May, Rhys, Yang, Zhen, Schalkwyk, Johan, Butterfield, Christina, Hauth, Anja, Goldin, Alex, Hawkins, Will, Senter, Evan, Brin, Sergey, Woodman, Oliver, Ritter, Marvin, Noland, Eric, Giang, Minh, Bolina, Vijay, Lee, Lisa, Blyth, Tim, Mackinnon, Ian, Reid, Machel, Sarvana, Obaid, Silver, David, Chen, Alexander, Wang, Lily, Maggiore, Loren, Chang, Oscar, Attaluri, Nithya, Thornton, Gregory, Chiu, Chung-Cheng, Bunyan, Oskar, Levine, Nir, Chung, Timothy, Eltyshev, Evgenii, Si, Xiance, Lillicrap, Timothy, Brady, Demetra, Aggarwal, Vaibhav, Wu, Boxi, Xu, Yuanzhong, McIlroy, Ross, Badola, Kartikeya, Sandhu, Paramjit, Moreira, Erica, Stokowiec, Wojciech, Hemsley, Ross, Li, Dong, Tudor, Alex, Shyam, Pranav, Rahimtoroghi, Elahe, Haykal, Salem, Sprechmann, Pablo, Zhou, Xiang, Mincu, Diana, Li, Yujia, Addanki, Ravi, Krishna, Kalpesh, Wu, Xiao, Frechette, Alexandre, Eyal, Matan, Dafoe, Allan, Lacey, Dave, Whang, Jay, Avrahami, Thi, Zhang, Ye, Taropa, Emanuel, Lin, Hanzhao, Toyama, Daniel, Rutherford, Eliza, Sano, Motoki, Choe, HyunJeong, Tomala, Alex, Safranek-Shrader, Chalence, Kassner, Nora, Pajarskas, Mantas, Harvey, Matt, Sechrist, Sean, Fortunato, Meire, Lyu, Christina, Elsayed, Gamaleldin, Kuang, Chenkai, Lottes, James, Chu, Eric, Jia, Chao, Chen, Chih-Wei, Humphreys, Peter, Baumli, Kate, Tao, Connie, Samuel, Rajkumar, Santos, Cicero Nogueira dos, Andreassen, Anders, Rakićević, Nemanja, Grewe, Dominik, Kumar, Aviral, Winkler, Stephanie, Caton, Jonathan, Brock, Andrew, Dalmia, Sid, Sheahan, Hannah, Barr, Iain, Miao, Yingjie, Natsev, Paul, Devlin, Jacob, Behbahani, Feryal, Prost, Flavien, Sun, Yanhua, Myaskovsky, Artiom, Pillai, Thanumalayan Sankaranarayana, Hurt, Dan, Lazaridou, Angeliki, Xiong, Xi, Zheng, Ce, Pardo, Fabio, Li, Xiaowei, Horgan, Dan, Stanton, Joe, Ambar, Moran, Xia, Fei, Lince, Alejandro, Wang, Mingqiu, Mustafa, Basil, Webson, Albert, Lee, Hyo, Anil, Rohan, Wicke, Martin, Dozat, Timothy, Sinha, Abhishek, Piqueras, Enrique, Dabir, Elahe, Upadhyay, Shyam, Boral, Anudhyan, Hendricks, Lisa Anne, Fry, Corey, Djolonga, Josip, Su, Yi, Walker, Jake, Labanowski, Jane, Huang, Ronny, Misra, Vedant, Chen, Jeremy, Skerry-Ryan, RJ, Singh, Avi, Rijhwani, Shruti, Yu, Dian, Castro-Ros, Alex, Changpinyo, Beer, Datta, Romina, Bagri, Sumit, Hrafnkelsson, Arnar Mar, Maggioni, Marcello, Zheng, Daniel, Sulsky, Yury, Hou, Shaobo, Paine, Tom Le, Yang, Antoine, Riesa, Jason, Rogozinska, Dominika, Marcus, Dror, Badawy, Dalia El, Zhang, Qiao, Wang, Luyu, Miller, Helen, Greer, Jeremy, Sjos, Lars Lowe, Nova, Azade, Zen, Heiga, Chaabouni, Rahma, Rosca, Mihaela, Jiang, Jiepu, Chen, Charlie, Liu, Ruibo, Sainath, Tara, Krikun, Maxim, Polozov, Alex, Lespiau, Jean-Baptiste, Newlan, Josh, Cankara, Zeyncep, Kwak, Soo, Xu, Yunhan, Chen, Phil, Coenen, Andy, Meyer, Clemens, Tsihlas, Katerina, Ma, Ada, Gottweis, Juraj, Xing, Jinwei, Gu, Chenjie, Miao, Jin, Frank, Christian, Cankara, Zeynep, Ganapathy, Sanjay, Dasgupta, Ishita, Hughes-Fitt, Steph, Chen, Heng, Reid, David, Rong, Keran, Fan, Hongmin, van Amersfoort, Joost, Zhuang, Vincent, Cohen, Aaron, Gu, Shixiang Shane, Mohananey, Anhad, Ilic, Anastasija, Tobin, Taylor, Wieting, John, Bortsova, Anna, Thacker, Phoebe, Wang, Emma, Caveness, Emily, Chiu, Justin, Sezener, Eren, Kaskasoli, Alex, Baker, Steven, Millican, Katie, Elhawaty, Mohamed, Aisopos, Kostas, Lebsack, Carl, Byrd, Nathan, Dai, Hanjun, Jia, Wenhao, Wiethoff, Matthew, Davoodi, Elnaz, Weston, Albert, Yagati, Lakshman, Ahuja, Arun, Gao, Isabel, Pundak, Golan, Zhang, Susan, Azzam, Michael, Sim, Khe Chai, Caelles, Sergi, Keeling, James, Sharma, Abhanshu, Swing, Andy, Li, YaGuang, Liu, Chenxi, Bostock, Carrie Grimes, Bansal, Yamini, Nado, Zachary, Anand, Ankesh, Lipschultz, Josh, Karmarkar, Abhijit, Proleev, Lev, Ittycheriah, Abe, Yeganeh, Soheil Hassas, Polovets, George, Faust, Aleksandra, Sun, Jiao, Rrustemi, Alban, Li, Pen, Shivanna, Rakesh, Liu, Jeremiah, Welty, Chris, Lebron, Federico, Baddepudi, Anirudh, Krause, Sebastian, Parisotto, Emilio, Soricut, Radu, Xu, Zheng, Bloxwich, Dawn, Johnson, Melvin, Neyshabur, Behnam, Mao-Jones, Justin, Wang, Renshen, Ramasesh, Vinay, Abbas, Zaheer, Guez, Arthur, Segal, Constant, Nguyen, Duc Dung, Svensson, James, Hou, Le, York, Sarah, Milan, Kieran, Bridgers, Sophie, Gworek, Wiktor, Tagliasacchi, Marco, Lee-Thorp, James, Chang, Michael, Guseynov, Alexey, Hartman, Ale Jakse, Kwong, Michael, Zhao, Ruizhe, Kashem, Sheleem, Cole, Elizabeth, Miech, Antoine, Tanburn, Richard, Phuong, Mary, Pavetic, Filip, Cevey, Sebastien, Comanescu, Ramona, Ives, Richard, Yang, Sherry, Du, Cosmo, Li, Bo, Zhang, Zizhao, Iinuma, Mariko, Hu, Clara Huiyi, Roy, Aurko, Bijwadia, Shaan, Zhu, Zhenkai, Martins, Danilo, Saputro, Rachel, Gergely, Anita, Zheng, Steven, Jia, Dawei, Antonoglou, Ioannis, Sadovsky, Adam, Gu, Shane, Bi, Yingying, Andreev, Alek, Samangooei, Sina, Khan, Mina, Kocisky, Tomas, Filos, Angelos, Kumar, Chintu, Bishop, Colton, Yu, Adams, Hodkinson, Sarah, Mittal, Sid, Shah, Premal, Moufarek, Alexandre, Cheng, Yong, Bloniarz, Adam, Lee, Jaehoon, Pejman, Pedram, Michel, Paul, Spencer, Stephen, Feinberg, Vladimir, Xiong, Xuehan, Savinov, Nikolay, Smith, Charlotte, Shakeri, Siamak, Tran, Dustin, Chesus, Mary, Bohnet, Bernd, Tucker, George, von Glehn, Tamara, Muir, Carrie, Mao, Yiran, Kazawa, Hideto, Slone, Ambrose, Soparkar, Kedar, Shrivastava, Disha, Cobon-Kerr, James, Sharman, Michael, Pavagadhi, Jay, Araya, Carlos, Misiunas, Karolis, Ghelani, Nimesh, Laskin, Michael, Barker, David, Li, Qiujia, Briukhov, Anton, Houlsby, Neil, Glaese, Mia, Lakshminarayanan, Balaji, Schucher, Nathan, Tang, Yunhao, Collins, Eli, Lim, Hyeontaek, Feng, Fangxiaoyu, Recasens, Adria, Lai, Guangda, Magni, Alberto, De Cao, Nicola, Siddhant, Aditya, Ashwood, Zoe, Orbay, Jordi, Dehghani, Mostafa, Brennan, Jenny, He, Yifan, Xu, 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Henryk, Viola, Fabio, Quitry, Felix de Chaumont, Lan, Charline Le, Hudson, Tom, Wang, Qingze, Fischer, Felix, Zheng, Ivy, White, Elspeth, Dragan, Anca, Alayrac, Jean-baptiste, Ni, Eric, Pritzel, Alexander, Iwanicki, Adam, Isard, Michael, Bulanova, Anna, Zilka, Lukas, Dyer, Ethan, Sachan, Devendra, Srinivasan, Srivatsan, Muckenhirn, Hannah, Cai, Honglong, Mandhane, Amol, Tariq, Mukarram, Rae, Jack W., Wang, Gary, Ayoub, Kareem, FitzGerald, Nicholas, Zhao, Yao, Han, Woohyun, Alberti, Chris, Garrette, Dan, Krishnakumar, Kashyap, Gimenez, Mai, Levskaya, Anselm, Sohn, Daniel, Matak, Josip, Iturrate, Inaki, Chang, Michael B., Xiang, Jackie, Cao, Yuan, Ranka, Nishant, Brown, Geoff, Hutter, Adrian, Mirrokni, Vahab, Chen, Nanxin, Yao, Kaisheng, Egyed, Zoltan, Galilee, Francois, Liechty, Tyler, Kallakuri, Praveen, Palmer, Evan, Ghemawat, Sanjay, Liu, Jasmine, Tao, David, Thornton, Chloe, Green, Tim, Jasarevic, Mimi, Lin, Sharon, Cotruta, Victor, Tan, Yi-Xuan, Fiedel, Noah, Yu, Hongkun, Chi, Ed, Neitz, Alexander, Heitkaemper, Jens, Sinha, Anu, Zhou, Denny, Sun, Yi, Kaed, Charbel, Hulse, Brice, Mishra, Swaroop, Georgaki, Maria, Kudugunta, Sneha, Farabet, Clement, Shafran, Izhak, Vlasic, Daniel, Tsitsulin, Anton, Ananthanarayanan, Rajagopal, Carin, Alen, Su, Guolong, Sun, Pei, V, Shashank, Carvajal, Gabriel, Broder, Josef, Comsa, Iulia, Repina, Alena, Wong, William, Chen, Warren Weilun, Hawkins, Peter, Filonov, Egor, Loher, Lucia, Hirnschall, Christoph, Wang, Weiyi, Ye, Jingchen, Burns, Andrea, Cate, Hardie, Wright, Diana Gage, Piccinini, Federico, Zhang, Lei, Lin, Chu-Cheng, Gog, Ionel, Kulizhskaya, Yana, Sreevatsa, Ashwin, Song, Shuang, Cobo, Luis C., Iyer, Anand, Tekur, Chetan, Garrido, Guillermo, Xiao, Zhuyun, Kemp, Rupert, Zheng, Huaixiu Steven, Li, Hui, Agarwal, Ananth, Ngani, Christel, Goshvadi, Kati, Santamaria-Fernandez, Rebeca, Fica, Wojciech, Chen, Xinyun, Gorgolewski, Chris, Sun, Sean, Garg, Roopal, Ye, Xinyu, Eslami, S. M. Ali, Hua, Nan, Simon, Jon, Joshi, Pratik, Kim, Yelin, Tenney, Ian, Potluri, Sahitya, Thiet, Lam Nguyen, Yuan, Quan, Luisier, Florian, Chronopoulou, Alexandra, Scellato, Salvatore, Srinivasan, Praveen, Chen, Minmin, Koverkathu, Vinod, Dalibard, Valentin, Xu, Yaming, Saeta, Brennan, Anderson, Keith, Sellam, Thibault, Fernando, Nick, Huot, Fantine, Jung, Junehyuk, Varadarajan, Mani, Quinn, Michael, Raul, Amit, Le, Maigo, Habalov, Ruslan, Clark, Jon, Jalan, Komal, Bullard, Kalesha, Singhal, Achintya, Luong, Thang, Wang, Boyu, Rajayogam, Sujeevan, Eisenschlos, Julian, Jia, Johnson, Finchelstein, Daniel, Yakubovich, Alex, Balle, Daniel, Fink, Michael, Agarwal, Sameer, Li, Jing, Dvijotham, Dj, Pal, Shalini, Kang, Kai, Konzelmann, Jaclyn, Beattie, Jennifer, Dousse, Olivier, Wu, Diane, Crocker, Remi, Elkind, Chen, Jonnalagadda, Siddhartha Reddy, Lee, Jong, Holtmann-Rice, Dan, Kallarackal, Krystal, Liu, Rosanne, Vnukov, Denis, Vats, Neera, Invernizzi, Luca, Jafari, Mohsen, Zhou, Huanjie, Taylor, Lilly, Prendki, Jennifer, Wu, Marcus, Eccles, Tom, Liu, Tianqi, Kopparapu, Kavya, Beaufays, Francoise, Angermueller, Christof, Marzoca, Andreea, Sarcar, Shourya, Dib, Hilal, Stanway, Jeff, Perbet, Frank, Trdin, Nejc, Sterneck, Rachel, Khorlin, Andrey, Li, Dinghua, Wu, Xihui, Goenka, Sonam, Madras, David, Goldshtein, Sasha, Gierke, Willi, Zhou, Tong, Liu, Yaxin, Liang, Yannie, White, Anais, Li, Yunjie, Singh, Shreya, Bahargam, Sanaz, Epstein, Mark, Basu, Sujoy, Lao, Li, Ozturel, Adnan, Crous, Carl, Zhai, Alex, Lu, Han, Tung, Zora, Gaur, Neeraj, Walton, Alanna, Dixon, Lucas, Zhang, Ming, Globerson, Amir, Uy, Grant, Bolt, Andrew, Wiles, Olivia, Nasr, Milad, Shumailov, Ilia, Selvi, Marco, Piccinno, Francesco, Aguilar, Ricardo, McCarthy, Sara, Khalman, Misha, Shukla, Mrinal, Galic, Vlado, Carpenter, John, Villela, Kevin, Zhang, Haibin, Richardson, Harry, Martens, James, Bosnjak, Matko, Belle, Shreyas Rammohan, Seibert, Jeff, Alnahlawi, Mahmoud, McWilliams, Brian, Singh, Sankalp, Louis, Annie, Ding, Wen, Popovici, Dan, Simicich, Lenin, Knight, Laura, Mehta, Pulkit, Gupta, Nishesh, Shi, Chongyang, Fatehi, Saaber, Mitrovic, Jovana, Grills, Alex, Pagadora, Joseph, Petrova, Dessie, Eisenbud, Danielle, Zhang, Zhishuai, Yates, Damion, Mittal, Bhavishya, Tripuraneni, Nilesh, Assael, Yannis, Brovelli, Thomas, Jain, Prateek, Velimirovic, Mihajlo, Akbulut, Canfer, Mu, Jiaqi, Macherey, Wolfgang, Kumar, Ravin, Xu, Jun, Qureshi, Haroon, Comanici, Gheorghe, Wiesner, Jeremy, Gong, Zhitao, Ruddock, Anton, Bauer, Matthias, Felt, Nick, GP, Anirudh, Arnab, Anurag, Zelle, Dustin, Rothfuss, Jonas, Rosgen, Bill, Shenoy, Ashish, Seybold, Bryan, Li, Xinjian, Mudigonda, Jayaram, Erdogan, Goker, Xia, Jiawei, Simsa, Jiri, Michi, Andrea, Yao, Yi, Yew, Christopher, Kan, Steven, Caswell, Isaac, Radebaugh, Carey, Elisseeff, Andre, Valenzuela, Pedro, McKinney, Kay, Paterson, Kim, Cui, Albert, Latorre-Chimoto, Eri, Kim, Solomon, Zeng, William, Durden, Ken, Ponnapalli, Priya, Sosea, Tiberiu, Choquette-Choo, Christopher A., Manyika, James, Robenek, Brona, Vashisht, Harsha, Pereira, Sebastien, Lam, Hoi, Velic, Marko, Owusu-Afriyie, Denese, Lee, Katherine, Bolukbasi, Tolga, Parrish, Alicia, Lu, Shawn, Park, Jane, Venkatraman, Balaji, Talbert, Alice, Rosique, Lambert, Cheng, Yuchung, Sozanschi, Andrei, Paszke, Adam, Kumar, Praveen, Austin, Jessica, Li, Lu, Salama, Khalid, Kim, Wooyeol, Dukkipati, Nandita, Baryshnikov, Anthony, Kaplanis, Christos, Sheng, XiangHai, Chervonyi, Yuri, Unlu, Caglar, Casas, Diego de Las, Askham, Harry, Tunyasuvunakool, Kathryn, Gimeno, Felix, Poder, Siim, Kwak, Chester, Miecnikowski, Matt, Dimitriev, Alek, Parisi, Aaron, Liu, Dangyi, Tsai, Tomy, Shevlane, Toby, Kouridi, Christina, Garmon, Drew, Goedeckemeyer, Adrian, Brown, Adam R., Vijayakumar, Anitha, Elqursh, Ali, Jazayeri, Sadegh, Huang, Jin, Carthy, Sara Mc, Hoover, Jay, Kim, Lucy, Kumar, Sandeep, Chen, Wei, Biles, Courtney, Bingham, Garrett, Rosen, Evan, Wang, Lisa, Tan, Qijun, Engel, David, Pongetti, Francesco, de Cesare, Dario, Hwang, Dongseong, Yu, Lily, Pullman, Jennifer, Narayanan, Srini, Levin, Kyle, Gopal, Siddharth, Li, Megan, Aharoni, Asaf, Trinh, Trieu, Lo, Jessica, Casagrande, Norman, Vij, Roopali, Matthey, Loic, Ramadhana, Bramandia, Matthews, Austin, Carey, CJ, Johnson, Matthew, Goranova, Kremena, Shah, Rohin, Ashraf, Shereen, Dasgupta, Kingshuk, Larsen, Rasmus, Wang, Yicheng, Vuyyuru, Manish Reddy, Jiang, Chong, Ijazi, Joana, Osawa, Kazuki, Smith, Celine, Boppana, Ramya Sree, Bilal, Taylan, Koizumi, Yuma, Xu, Ying, Altun, Yasemin, Shabat, Nir, Bariach, Ben, Korchemniy, Alex, Choo, Kiam, Ronneberger, Olaf, Iwuanyanwu, Chimezie, Zhao, Shubin, Soergel, David, Hsieh, Cho-Jui, Cai, Irene, Iqbal, Shariq, Sundermeyer, Martin, Chen, Zhe, Bursztein, Elie, Malaviya, Chaitanya, Biadsy, Fadi, Shroff, Prakash, Dhillon, Inderjit, Latkar, Tejasi, Dyer, Chris, Forbes, Hannah, Nicosia, Massimo, Nikolaev, Vitaly, Greene, Somer, Georgiev, Marin, Wang, Pidong, Martin, Nina, Sedghi, Hanie, Zhang, John, Banzal, Praseem, Fritz, Doug, Rao, Vikram, Wang, Xuezhi, Zhang, Jiageng, Patraucean, Viorica, Du, Dayou, Mordatch, Igor, Jurin, Ivan, Liu, Lewis, Dubey, Ayush, Mohan, Abhi, Nowakowski, Janek, Ion, Vlad-Doru, Wei, Nan, Tojo, Reiko, Raad, Maria Abi, Hudson, Drew A., Keshava, Vaishakh, Agrawal, Shubham, Ramirez, Kevin, Wu, Zhichun, Nguyen, Hoang, Liu, Ji, Sewak, Madhavi, Petrini, Bryce, Choi, DongHyun, Philips, Ivan, Wang, Ziyue, Bica, Ioana, Garg, Ankush, Wilkiewicz, Jarek, Agrawal, Priyanka, Guo, Danhao, Xue, Emily, Shaik, Naseer, Leach, Andrew, Khan, Sadh MNM, Wiesinger, Julia, Jerome, Sammy, Chakladar, Abhishek, Wang, Alek Wenjiao, Ornduff, Tina, Abu, Folake, Ghaffarkhah, Alireza, Wainwright, Marcus, Cortes, Mario, Liu, Frederick, Maynez, Joshua, Terzis, Andreas, Samangouei, Pouya, Mansour, Riham, Kępa, Tomasz, Aubet, François-Xavier, Algymr, Anton, Banica, Dan, Weisz, Agoston, Orban, Andras, Senges, Alexandre, Andrejczuk, Ewa, Geller, Mark, Santo, Niccolo Dal, Anklin, Valentin, Merey, Majd Al, Baeuml, Martin, Strohman, Trevor, Bai, Junwen, Petrov, Slav, Wu, Yonghui, Hassabis, Demis, Kavukcuoglu, Koray, Dean, Jeffrey, and Vinyals, Oriol
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.
Published: 2024

49. Performance of a modular ton-scale pixel-readout liquid argon time projection chamber

Author: DUNE Collaboration, Abud, A. Abed, Abi, B., Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adamowski, M., Adams, D., Adinolfi, M., Adriano, C., Aduszkiewicz, A., Aguilar, J., Aimard, B., Akbar, F., Allison, K., Monsalve, S. Alonso, Alrashed, M., Alton, A., Alvarez, R., Alves, T., Amar, H., Amedo, P., Anderson, J., Andrade, D. A., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Ankowski, A., Antoniassi, M., Antonova, M., Antoshkin, A., Aranda-Fernandez, A., Arellano, L., Diaz, E. Arrieta, Arroyave, M. A., Asaadi, J., Ashkenazi, A., Asner, D., Asquith, L., Atkin, E., Auguste, D., Aurisano, A., Aushev, V., Autiero, D., Azfar, F., Back, A., Back, H., Back, J. J., Bagaturia, I., Bagby, L., Balashov, N., Balasubramanian, S., Baldi, P., Baldini, W., Baldonedo, J., Baller, B., Bambah, B., Banerjee, R., Barao, F., Barenboim, G., Alzás, P. Barham, Barker, G. J., Barkhouse, W., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, D., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Batchelor, C., Bathe-Peters, L., Battat, J. B. R., Battisti, F., Bay, F., Bazetto, M. C. Q., Alba, J. L. L. Bazo, Beacom, J. F., Bechetoille, E., Behera, B., Belchior, E., Bell, G., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Benekos, N., Montiel, C. Benitez, Benjamin, D., Neves, F. Bento, Berger, J., Berkman, S., Bernal, J., Bernardini, P., Bersani, A., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bevan, A., Bezawada, Y., Bezerra, A. T., Bezerra, T. J., Bhat, A., Bhatnagar, V., Bhatt, J., Bhattacharjee, M., Bhattacharya, M., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biery, K., Bilki, B., Bishai, M., Bitadze, A., Blake, A., Blaszczyk, F. D., Blazey, G. C., Blucher, E., Bogenschuetz, J., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bonilla-Diaz, C., Bonini, F., Booth, A., Boran, F., Bordoni, S., Merlo, R. 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O., Waters, D., Watson, A., Wawrowska, K., Weber, A., Weber, C. M., Weber, M., Wei, H., Weinstein, A., Wenzel, H., Westerdale, S., Wetstein, M., Whalen, K., Whilhelmi, J., White, A., Whitehead, L. H., Whittington, D., Wilking, M. J., Wilkinson, A., Wilkinson, C., Wilson, F., Wilson, R. J., Winter, P., Wisniewski, W., Wolcott, J., Wolfs, J., Wongjirad, T., Wood, A., Wood, K., Worcester, E., Worcester, M., Wospakrik, M., Wresilo, K., Wret, C., Wu, S., Wu, W., Wurm, M., Wyenberg, J., Xiao, Y., Xiotidis, I., Yaeggy, B., Yahlali, N., Yandel, E., Yang, K., Yang, T., Yankelevich, A., Yershov, N., Yonehara, K., Young, T., Yu, B., Yu, H., Yu, J., Yu, Y., Yuan, W., Zaki, R., Zalesak, J., Zambelli, L., Zamorano, B., Zani, A., Zapata, O., Zazueta, L., Zeller, G. P., Zennamo, J., Zeug, K., Zhang, C., Zhang, S., Zhao, M., Zhivun, E., Zimmerman, E. D., Zucchelli, S., Zuklin, J., Zutshi, V., and Zwaska, R.
Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract: The Module-0 Demonstrator is a single-phase 600 kg liquid argon time projection chamber operated as a prototype for the DUNE liquid argon near detector. Based on the ArgonCube design concept, Module-0 features a novel 80k-channel pixelated charge readout and advanced high-coverage photon detection system. In this paper, we present an analysis of an eight-day data set consisting of 25 million cosmic ray events collected in the spring of 2021. We use this sample to demonstrate the imaging performance of the charge and light readout systems as well as the signal correlations between the two. We also report argon purity and detector uniformity measurements, and provide comparisons to detector simulations., Comment: 47 pages, 41 figures
Published: 2024

50. Biodiversity impacts of the 2019–2020 Australian megafires

Author: Driscoll, Don A., Macdonald, Kristina J., Gibson, Rebecca K., Doherty, Tim S., Nimmo, Dale G., Nolan, Rachael H., Ritchie, Euan G., Williamson, Grant J., Heard, Geoffrey W., Tasker, Elizabeth M., Bilney, Rohan, Porch, Nick, Collett, Rachael A., Crates, Ross A., Hewitt, Alison C., Pendall, Elise, Boer, Matthias M., Gates, Jody, Boulton, Rebecca L., Mclean, Christopher M., Groffen, Heidi, Maisey, Alex C., Beranek, Chad T., Ryan, Shelby A., Callen, Alex, Hamer, Andrew J., Stauber, Andrew, Daly, Garry J., Gould, John, Klop-Toker, Kaya L., Mahony, Michael J., Kelly, Oliver W., Wallace, Samantha L., Stock, Sarah E., Weston, Christopher J., Volkova, Liubov, Black, Dennis, Gibb, Heloise, Grubb, Joshua J., McGeoch, Melodie A., Murphy, Nick P., Lee, Joshua S., Dickman, Chris R., Neldner, Victor J., Ngugi, Michael R., Miritis, Vivianna, Köhler, Frank, Perri, Marc, Denham, Andrew J., Mackenzie, Berin D. E., Reid, Chris A. M., Rayment, Julia T., Arriaga-Jiménez, Alfonsina, Hewins, Michael W., Hicks, Andrew, Melbourne, Brett A., Davies, Kendi F., Bitters, Matthew E., Linley, Grant D., Greenville, Aaron C., Webb, Jonathan K., Roberts, Bridget, Letnic, Mike, Price, Owen F., Walker, Zac C., Murray, Brad R., Verhoeven, Elise M., Thomsen, Alexandria M., Keith, David, Lemmon, Jedda S., Ooi, Mark K. J., Allen, Vanessa L., Decker, Orsi T., Green, Peter T., Moussalli, Adnan, Foon, Junn K., Bryant, David B., Walker, Ken L., Bruce, Matthew J., Madani, George, Tscharke, Jeremy L., Wagner, Benjamin, Nitschke, Craig R., Gosper, Carl R., Yates, Colin J., Dillon, Rebecca, Barrett, Sarah, Spencer, Emma E., Wardle, Glenda M., Newsome, Thomas M., Pulsford, Stephanie A., Singh, Anu, Roff, Adam, Marsh, Karen J., Mcdonald, Kye, Howell, Lachlan G., Lane, Murraya R., Cristescu, Romane H., Witt, Ryan R., Cook, Emma J., Grant, Felicity, Law, Bradley S., Seddon, Julian, Berris, Karleah K., Shofner, Ryan M., Barth, Mike, Welz, Torran, Foster, Alison, Hancock, David, Beitzel, Matthew, Tan, Laura X. L., Waddell, Nathan A., Fallow, Pamela M., Schweickle, Laura, Le Breton, Tom D., Dunne, Craig, Green, Mikayla, Gilpin, Amy-Marie, Cook, James M., Power, Sally A., Hogendoorn, Katja, Brawata, Renee, Jolly, Chris J., Tozer, Mark, Reiter, Noushka, and Phillips, Ryan D.
Published: 2024
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