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291 results on '"Tsaris, A."'

1. OReole-FM: successes and challenges toward billion-parameter foundation models for high-resolution satellite imagery

Author: Dias, Philipe, Tsaris, Aristeidis, Bowman, Jordan, Potnis, Abhishek, Arndt, Jacob, Yang, H. Lexie, and Lunga, Dalton
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract: While the pretraining of Foundation Models (FMs) for remote sensing (RS) imagery is on the rise, models remain restricted to a few hundred million parameters. Scaling models to billions of parameters has been shown to yield unprecedented benefits including emergent abilities, but requires data scaling and computing resources typically not available outside industry R&D labs. In this work, we pair high-performance computing resources including Frontier supercomputer, America's first exascale system, and high-resolution optical RS data to pretrain billion-scale FMs. Our study assesses performance of different pretrained variants of vision Transformers across image classification, semantic segmentation and object detection benchmarks, which highlight the importance of data scaling for effective model scaling. Moreover, we discuss construction of a novel TIU pretraining dataset, model initialization, with data and pretrained models intended for public release. By discussing technical challenges and details often lacking in the related literature, this work is intended to offer best practices to the geospatial community toward efficient training and benchmarking of larger FMs.
Published: 2024
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2. Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars

Author: Brewer, Wesley, Kashi, Aditya, Dash, Sajal, Tsaris, Aristeidis, Yin, Junqi, Shankar, Mallikarjun, and Wang, Feiyi
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract: In a post-ChatGPT world, this paper explores the potential of leveraging scalable artificial intelligence for scientific discovery. We propose that scaling up artificial intelligence on high-performance computing platforms is essential to address such complex problems. This perspective focuses on scientific use cases like cognitive simulations, large language models for scientific inquiry, medical image analysis, and physics-informed approaches. The study outlines the methodologies needed to address such challenges at scale on supercomputers or the cloud and provides exemplars of such approaches applied to solve a variety of scientific problems., Comment: 17 pages, 5 figures
Published: 2024

3. ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability

Author: Wang, Xiao, Liu, Siyan, Tsaris, Aristeidis, Choi, Jong-Youl, Aji, Ashwin, Fan, Ming, Zhang, Wei, Yin, Junqi, Ashfaq, Moetasim, Lu, Dan, and Balaprakash, Prasanna
Subjects: Physics - Atmospheric and Oceanic Physics, Computer Science - Artificial Intelligence, Computer Science - Distributed, Parallel, and Cluster Computing, Electrical Engineering and Systems Science - Image and Video Processing, Physics - Geophysics
Abstract: Earth system predictability is challenged by the complexity of environmental dynamics and the multitude of variables involved. Current AI foundation models, although advanced by leveraging large and heterogeneous data, are often constrained by their size and data integration, limiting their effectiveness in addressing the full range of Earth system prediction challenges. To overcome these limitations, we introduce the Oak Ridge Base Foundation Model for Earth System Predictability (ORBIT), an advanced vision transformer model that scales up to 113 billion parameters using a novel hybrid tensor-data orthogonal parallelism technique. As the largest model of its kind, ORBIT surpasses the current climate AI foundation model size by a thousandfold. Performance scaling tests conducted on the Frontier supercomputer have demonstrated that ORBIT achieves 684 petaFLOPS to 1.6 exaFLOPS sustained throughput, with scaling efficiency maintained at 41% to 85% across 49,152 AMD GPUs. These breakthroughs establish new advances in AI-driven climate modeling and demonstrate promise to significantly improve the Earth system predictability.
Published: 2024

4. Sequence Length Scaling in Vision Transformers for Scientific Images on Frontier

Author: Tsaris, Aristeidis, Zhang, Chengming, Wang, Xiao, Yin, Junqi, Liu, Siyan, Ashfaq, Moetasim, Fan, Ming, Choi, Jong Youl, Wahib, Mohamed, Lu, Dan, Balaprakash, Prasanna, and Wang, Feiyi
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: Vision Transformers (ViTs) are pivotal for foundational models in scientific imagery, including Earth science applications, due to their capability to process large sequence lengths. While transformers for text has inspired scaling sequence lengths in ViTs, yet adapting these for ViTs introduces unique challenges. We develop distributed sequence parallelism for ViTs, enabling them to handle up to 1M tokens. Our approach, leveraging DeepSpeed-Ulysses and Long-Sequence-Segmentation with model sharding, is the first to apply sequence parallelism in ViT training, achieving a 94% batch scaling efficiency on 2,048 AMD-MI250X GPUs. Evaluating sequence parallelism in ViTs, particularly in models up to 10B parameters, highlighted substantial bottlenecks. We countered these with hybrid sequence, pipeline, tensor parallelism, and flash attention strategies, to scale beyond single GPU memory limits. Our method significantly enhances climate modeling accuracy by 20% in temperature predictions, marking the first training of a transformer model on a full-attention matrix over 188K sequence length.
Published: 2024

5. Pretraining Billion-scale Geospatial Foundational Models on Frontier

Author: Tsaris, Aristeidis, Dias, Philipe Ambrozio, Potnis, Abhishek, Yin, Junqi, Wang, Feiyi, and Lunga, Dalton
Subjects: Computer Science - Artificial Intelligence
Abstract: As AI workloads increase in scope, generalization capability becomes challenging for small task-specific models and their demand for large amounts of labeled training samples increases. On the contrary, Foundation Models (FMs) are trained with internet-scale unlabeled data via self-supervised learning and have been shown to adapt to various tasks with minimal fine-tuning. Although large FMs have demonstrated significant impact in natural language processing and computer vision, efforts toward FMs for geospatial applications have been restricted to smaller size models, as pretraining larger models requires very large computing resources equipped with state-of-the-art hardware accelerators. Current satellite constellations collect 100+TBs of data a day, resulting in images that are billions of pixels and multimodal in nature. Such geospatial data poses unique challenges opening up new opportunities to develop FMs. We investigate billion scale FMs and HPC training profiles for geospatial applications by pretraining on publicly available data. We studied from end-to-end the performance and impact in the solution by scaling the model size. Our larger 3B parameter size model achieves up to 30% improvement in top1 scene classification accuracy when comparing a 100M parameter model. Moreover, we detail performance experiments on the Frontier supercomputer, America's first exascale system, where we study different model and data parallel approaches using PyTorch's Fully Sharded Data Parallel library. Specifically, we study variants of the Vision Transformer architecture (ViT), conducting performance analysis for ViT models with size up to 15B parameters. By discussing throughput and performance bottlenecks under different parallelism configurations, we offer insights on how to leverage such leadership-class HPC resources when developing large models for geospatial imagery applications.
Published: 2024

6. Ultra-Long Sequence Distributed Transformer

Author: Wang, Xiao, Lyngaas, Isaac, Tsaris, Aristeidis, Chen, Peng, Dash, Sajal, Shekar, Mayanka Chandra, Luo, Tao, Yoon, Hong-Jun, Wahib, Mohamed, and Gouley, John
Subjects: Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Artificial Intelligence
Abstract: Transformer models trained on long sequences often achieve higher accuracy than short sequences. Unfortunately, conventional transformers struggle with long sequence training due to the overwhelming computation and memory requirements. Existing methods for long sequence training offer limited speedup and memory reduction, and may compromise accuracy. This paper presents a novel and efficient distributed training method, the Long Short-Sequence Transformer (LSS Transformer), for training transformer with long sequences. It distributes a long sequence into segments among GPUs, with each GPU computing a partial self-attention for its segment. Then, it uses a fused communication and a novel double gradient averaging technique to avoid the need to aggregate partial self-attention and minimize communication overhead. We evaluated the performance between LSS Transformer and the state-of-the-art Nvidia sequence parallelism on a Wikipedia enwik8 dataset. Results show that our proposed method lead to 5.6x faster and 10.2x more memory-efficient implementation compared to state-of-the-art sequence parallelism on 144 Nvidia V100 GPUs. Moreover, our algorithm scales to an extreme sequence length of 50,112 at 3,456 GPUs, achieving 161% super-linear parallel efficiency and a throughput of 32 petaflops.
Published: 2023

7. OReole-FM: successes and challenges toward billion-parameter foundation models for high-resolution satellite imagery.

Author: Philipe A. Dias, Aristeidis Tsaris, Jordan Bowman, Abhishek Potnis, Jacob Arndt, Hsiuhan Lexie Yang, and Dalton D. Lunga
Published: 2024
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8. Pretraining Billion-Scale Geospatial Foundational Models on Frontier.

Author: Aristeidis Tsaris, Philipe Ambrozio Dias, Abhishek Potnis, Junqi Yin, Feiyi Wang, and Dalton D. Lunga
Published: 2024
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9. Image Gradient Decomposition for Parallel and Memory-Efficient Ptychographic Reconstruction

Author: Wang, Xiao, Tsaris, Aristeidis, Mukherjee, Debangshu, Wahib, Mohamed, Chen, Peng, Oxley, Mark, Ovchinnikova, Olga, and Hinkle, Jacob
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract: Ptychography is a popular microscopic imaging modality for many scientific discoveries and sets the record for highest image resolution. Unfortunately, the high image resolution for ptychographic reconstruction requires significant amount of memory and computations, forcing many applications to compromise their image resolution in exchange for a smaller memory footprint and a shorter reconstruction time. In this paper, we propose a novel image gradient decomposition method that significantly reduces the memory footprint for ptychographic reconstruction by tessellating image gradients and diffraction measurements into tiles. In addition, we propose a parallel image gradient decomposition method that enables asynchronous point-to-point communications and parallel pipelining with minimal overhead on a large number of GPUs. Our experiments on a Titanate material dataset (PbTiO3) with 16632 probe locations show that our Gradient Decomposition algorithm reduces memory footprint by 51 times. In addition, it achieves time-to-solution within 2.2 minutes by scaling to 4158 GPUs with a super-linear strong scaling efficiency at 364% compared to runtimes at 6 GPUs. This performance is 2.7 times more memory efficient, 9 times more scalable and 86 times faster than the state-of-the-art algorithm.
Published: 2022
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10. MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems

Author: Farrell, Steven, Emani, Murali, Balma, Jacob, Drescher, Lukas, Drozd, Aleksandr, Fink, Andreas, Fox, Geoffrey, Kanter, David, Kurth, Thorsten, Mattson, Peter, Mu, Dawei, Ruhela, Amit, Sato, Kento, Shirahata, Koichi, Tabaru, Tsuguchika, Tsaris, Aristeidis, Balewski, Jan, Cumming, Ben, Danjo, Takumi, Domke, Jens, Fukai, Takaaki, Fukumoto, Naoto, Fukushi, Tatsuya, Gerofi, Balazs, Honda, Takumi, Imamura, Toshiyuki, Kasagi, Akihiko, Kawakami, Kentaro, Kudo, Shuhei, Kuroda, Akiyoshi, Martinasso, Maxime, Matsuoka, Satoshi, Mendonça, Henrique, Minami, Kazuki, Ram, Prabhat, Sawada, Takashi, Shankar, Mallikarjun, John, Tom St., Tabuchi, Akihiro, Vishwanath, Venkatram, Wahib, Mohamed, Yamazaki, Masafumi, and Yin, Junqi
Subjects: Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract: Scientific communities are increasingly adopting machine learning and deep learning models in their applications to accelerate scientific insights. High performance computing systems are pushing the frontiers of performance with a rich diversity of hardware resources and massive scale-out capabilities. There is a critical need to understand fair and effective benchmarking of machine learning applications that are representative of real-world scientific use cases. MLPerf is a community-driven standard to benchmark machine learning workloads, focusing on end-to-end performance metrics. In this paper, we introduce MLPerf HPC, a benchmark suite of large-scale scientific machine learning training applications driven by the MLCommons Association. We present the results from the first submission round, including a diverse set of some of the world's largest HPC systems. We develop a systematic framework for their joint analysis and compare them in terms of data staging, algorithmic convergence, and compute performance. As a result, we gain a quantitative understanding of optimizations on different subsystems such as staging and on-node loading of data, compute-unit utilization, and communication scheduling, enabling overall $>10 \times$ (end-to-end) performance improvements through system scaling. Notably, our analysis shows a scale-dependent interplay between the dataset size, a system's memory hierarchy, and training convergence that underlines the importance of near-compute storage. To overcome the data-parallel scalability challenge at large batch sizes, we discuss specific learning techniques and hybrid data-and-model parallelism that are effective on large systems. We conclude by characterizing each benchmark with respect to low-level memory, I/O, and network behavior to parameterize extended roofline performance models in future rounds.
Published: 2021

11. Measurement of the Double-Differential Muon-neutrino Charged-Current Inclusive Cross Section in the NOvA Near Detector

Author: Acero, M. A., Adamson, P., Aliaga, L., Anfimov, N., Antoshkin, A., Arrieta-Diaz, E., Asquith, L., Aurisano, A., Back, A., Backhouse, C., Baird, M., Balashov, N., Baldi, P., Bambah, B. A., Bashar, S., Bays, K., Behera, B., Bernstein, R., Bhatnagar, V., Bhattarai, D., Bhuyan, B., Bian, J., Blair, J., Booth, A. C., Bowles, R., Bromberg, C., Buchanan, N., Butkevich, A., Calvez, S., Carroll, T. J., Catano-Mur, E., Choudhary, B. C., Christensen, A., Coan, T. E., Colo, M., Cremonesi, L., Davies, G. S., Derwent, P. F., Ding, P., Djurcic, Z., Dolce, M., Doyle, D., Tonguino, D. Duenas, Dukes, E. C., Duyang, H., Edayath, S., Ehrlich, R., Elkins, M., Ewart, E., Feldman, G. J., Filip, P., Franc, J., Frank, M. J., Gallagher, H. R., Gandrajula, R., Gao, F., Giri, A., Gomes, R. A., Goodman, M. C., Grichine, V., Groh, M., Group, R., Guo, B., Habig, A., Hakl, F., Hall, A., Hartnell, J., Hatcher, R., Hausner, H., He, M., Heller, K., Hewes, V, Himmel, A., Holin, A., Huang, J., Jargowsky, B., Jarosz, J., Jediny, F., Johnson, C., Judah, M., Kakorin, I., Kalitkina, A., Kalra, D., Kaplan, D. M., Keloth, R., Klimov, O., Koerner, L. W., Kolupaeva, L., Kotelnikov, S., Kralik, R., Kullenberg, Ch., Kubu, M., Kumar, A., Kuruppu, C. D., Kus, V., Lackey, T., Lang, K., Lasorak, P., Lesmeister, J., Lin, S., Lister, A., Liu, J., Lokajicek, M., Magill, S., Plata, M. Manrique, Mann, W. A., Marshak, M. L., Martinez-Casales, M., Matveev, V., Mayes, B., Méndez, D. P., Messier, M. D., Meyer, H., Miao, T., Miller, W. H., Mishra, S. R., Mislivec, A., Mohanta, R., Moren, A., Morozova, A., Mu, W., Mualem, L., Muether, M., Mulder, K., Naples, D., Nayak, N., Nelson, J. K., Nichol, R., Niner, E., Norman, A., Norrick, A., Nosek, T., Oh, H., Olshevskiy, A., Olson, T., Ott, J., Paley, J., Patterson, R. B., Pawloski, G., Petrova, O., Petti, R., Phan, D. D., Plunkett, R. K., Porter, J. C. C., Rafique, A., Raj, V., Rajaoalisoa, M., Ramson, B., Rebel, B., Rojas, P., Roy, P., Ryabov, V., Sachdev, K., Samoylov, O., Sanchez, M. C., Falero, S. Sanchez, Shanahan, P., Sheshukov, A., Singh, P., Singh, V., Smith, E., Smolik, J., Snopok, P., Solomey, N., Sousa, A., Soustruznik, K., Strait, M., Suter, L., Sutton, A., Swain, S., Sweeney, C., Sztuc, A., Oregui, B. Tapia, Tas, P., Thakore, T., Thayyullathil, R. B., Thomas, J., Tiras, E., Tripathi, J., Trokan-Tenorio, J., Tsaris, A., Torun, Y., Urheim, J., Vahle, P., Vallari, Z., Vasel, J., Vokac, P., Vrba, T., Wallbank, M., Warburton, T. K., Wetstein, M., Whittington, D., Wickremasinghe, D. A., Wojcicki, S. G., Wolcott, J., Wu, W., Xiao, Y., Dombara, A. Yallappa, Yankelevich, A., Yonehara, K., Yu, S., Yu, Y., Zadorozhnyy, S., Zalesak, J., Zhang, Y., and Zwaska, R.
Subjects: High Energy Physics - Experiment
Abstract: We report cross-section measurements of the final-state muon kinematics for \numu charged-current interactions in the NOvA near detector using an accumulated 8.09$\times10^{20}$ protons-on-target (POT) in the NuMI beam. We present the results as a double-differential cross section in the observed outgoing muon energy and angle, as well as single-differential cross sections in the derived neutrino energy, $E_\nu$, and square of the four-momentum transfer, $Q^2$. We compare the results to inclusive cross-section predictions from various neutrino event generators via $\chi^2$ calculations using a covariance matrix that accounts for bin-to-bin correlations of systematic uncertainties. These comparisons show a clear discrepancy between the data and each of the tested predictions at forward muon angle and low $Q^2$, indicating a missing suppression of the cross section in current neutrino-nucleus scattering models.
Published: 2021
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12. An Improved Measurement of Neutrino Oscillation Parameters by the NOvA Experiment

Author: Acero, M. A., Adamson, P., Aliaga, L., Anfimov, N., Antoshkin, A., Arrieta-Diaz, E., Asquith, L., Aurisano, A., Back, A., Backhouse, C., Baird, M., Balashov, N., Baldi, P., Bambah, B. A., Bashar, S., Bays, K., Bernstein, R., Bhatnagar, V., Bhattarai, D., Bhuyan, B., Bian, J., Blair, J., Booth, A. C., Bowles, R., Bromberg, C., Buchanan, N., Butkevich, A., Calvez, S., Carroll, T. J., Catano-Mur, E., Choudhary, B. C., Christensen, A., Coan, T. E., Colo, M., Cremonesi, L., Davies, G. S., Derwent, P. F., Ding, P., Djurcic, Z., Dolce, M., Doyle, D., Tonguino, D. Dueñas, Dukes, E. C., Duyang, H., Ehrlich, R., Elkins, M., Ewart, E., Feldman, G. J., Filip, P., Franc, J., Frank, M. J., Gallagher, H. R., Gandrajula, R., Gao, F., Giri, A., Gomes, R. A., Goodman, M. C., Grichine, V., Groh, M., Group, R., Guo, B., Habig, A., Hakl, F., Hall, A., Hartnell, J., Hatcher, R., Hausner, H., He, M., Heller, K., Hewes, V., Himmel, A., Holin, A., Huang, J., Jargowsky, B., Jarosz, J., Jediny, F., Johnson, C., Judah, M., Kakorin, I., Kaplan, D. M., Kalitkina, A., Keloth, R., Klimov, O., Koerner, L. W., Kolupaeva, L., Kotelnikov, S., Kralik, R., Kullenberg, Ch., Kubu, M., Kumar, A., Kuruppu, C. D., Kus, V., Lackey, T., Lang, K., Lasorak, P., Lesmeister, J., Lin, S., Lister, A., Liu, J., Lokajicek, M., Magill, S., Plata, M. Manrique, Mann, W. A., Marshak, M. L., Martinez-Casales, M., Matveev, V., Mayes, B., Méndez, D. P., Messier, M. D., Meyer, H., Miao, T., Miller, W. H., Mishra, S. R., Mislivec, A., Mohanta, R., Moren, A., Morozova, A., Mu, W., Mualem, L., Muether, M., Mufson, S., Mulder, K., Naples, D., Nayak, N., Nelson, J. K., Nichol, R., Niner, E., Norman, A., Norrick, A., Nosek, T., Oh, H., Olshevskiy, A., Olson, T., Ott, J., Paley, J., Patterson, R. B., Pawloski, G., Petrova, O., Petti, R., Phan, D. D., Plunkett, R. K., Porter, J. C. C., Rafique, A., Psihas, F., Raj, V., Rajaoalisoa, M., Ramson, B., Rebel, B., Rojas, P., Roy, P., Ryabov, V., Samoylov, O., Sanchez, M. C., Falero, S. Sánchez, Shanahan, P., Sheshukov, A., Singh, P., Singh, V., Smith, E., Smolik, J., Snopok, P., Solomey, N., Sousa, A., Soustruznik, K., Strait, M., Suter, L., Sutton, A., Swain, S., Sweeney, C., Sztuc, A., Talaga, R. L., Oregui, B. Tapia, Tas, P., Thakore, T., Thayyullathil, R. B., Thomas, J., Tiras, E., Tripathi, J., Trokan-Tenorio, J., Tsaris, A., Torun, Y., Urheim, J., Vahle, P., Vallari, Z., Vasel, J., Vokac, P., Vrba, T., Wallbank, M., Warburton, T. K., Wetstein, M., Whittington, D., Wickremasinghe, D. A., Wojcicki, S. G., Wolcott, J., Wu, W., Xiao, Y., Dombara, A. Yallappa, Yankelevich, A., Yonehara, K., Yu, S., Yu, Y., Zadorozhnyy, S., Zalesak, J., Zhang, Y., and Zwaska, R.
Subjects: High Energy Physics - Experiment
Abstract: We present new $\nu_\mu\rightarrow\nu_e$, $\nu_\mu\rightarrow\nu_\mu$, $\overline{\nu}_\mu\rightarrow\overline{\nu}_e$, and $\overline{\nu}_\mu\rightarrow\overline{\nu}_\mu$ oscillation measurements by the NOvA experiment, with a 50% increase in neutrino-mode beam exposure over the previously reported results. The additional data, combined with previously published neutrino and antineutrino data, are all analyzed using improved techniques and simulations. A joint fit to the $\nu_e$, $\nu_\mu$, $\overline{\nu}_e$, and $\overline{\nu}_\mu$ candidate samples within the 3-flavor neutrino oscillation framework continues to yield a best-fit point in the normal mass ordering and the upper octant of the $\theta_{23}$ mixing angle, with $\Delta m^{2}_{32} = (2.41\pm0.07)\times 10^{-3}$ eV$^2$ and $\sin^2\theta_{23} = 0.57^{+0.03}_{-0.04}$. The data disfavor combinations of oscillation parameters that give rise to a large asymmetry in the rates of $\nu_e$ and $\overline{\nu}_e$ appearance. This includes values of the CP-violating phase in the vicinity of $\delta_\text{CP} = \pi/2$ which are excluded by $>3\sigma$ for the inverted mass ordering, and values around $\delta_\text{CP} = 3\pi/2$ in the normal ordering which are disfavored at 2$\sigma$ confidence., Comment: 11 pages, 6 figures. Supplementary material attached (7 figures)
Published: 2021
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13. Extended search for supernova-like neutrinos in NOvA coincident with LIGO/Virgo detections

Author: Acero, M. A., Adamson, P., Aliaga, L., Anfimov, N., Antoshkin, A., Arrieta-Diaz, E., Asquith, L., Aurisano, A., Back, A., Backhouse, C., Baird, M., Balashov, N., Baldi, P., Bambah, B. A., Bashar, S., Bays, K., Bernstein, R., Bhatnagar, V., Bhuyan, B., Bian, J., Blair, J., Booth, A. C., Bowles, R., Bromberg, C., Buchanan, N., Butkevich, A., Calvez, S., Carroll, T. J., Catano-Mur, E., Choudhary, B. C., Christensen, A., Coan, T. E., Colo, M., Corwin, L., Cremonesi, L., Davies, G. S., Derwent, P. F., Ding, P., Djurcic, Z., Dolce, M., Doyle, D., Tonguino, D. Dueñas, Dukes, E. C., Duyang, H., Edayath, S., Ehrlich, R., Elkins, M., Ewart, E., Feldman, G. J., Filip, P., Franc, J., Frank, M. J., Gallagher, H. R., Gandrajula, R., Gao, F., Giri, A., Gomes, R. A., Goodman, M. C., Grichine, V., Groh, M., Group, R., Guo, B., Habig, A., Hakl, F., Hall, A., Hartnell, J., Hatcher, R., Hatzikoutelis, A., Hausner, H., Heller, K., Hewes, V, Himmel, A., Holin, A., Huang, J., Jargowsky, B., Jarosz, J., Jediny, F., Johnson, C., Judah, M., Kakorin, I., Kalra, D., Kaplan, D. M., Kalitkina, A., Keloth, R., Klimov, O., Koerner, L. W., Kolupaeva, L., Kotelnikov, S., Kralik, R., Kullenberg, Ch., Kubu, M., Kumar, A., Kuruppu, C. D., Kus, V., Lackey, T., Lang, K., Lasorak, P., Lesmeister, J., Lin, S., Lister, A., Liu, J., Lokajicek, M., Magill, S., Plata, M. Manrique, Mann, W. A., Marshak, M. L., Martinez-Casales, M., Matveev, V., Mayes, B., Méndez, D. P., Messier, M. D., Meyer, H., Miao, T., Miller, W. H., Mishra, S. R., Mislivec, A., Mohanta, R., Moren, A., Morozova, A., Mu, W., Mualem, L., Muether, M., Mufson, S., Mulder, K., Naples, D., Nayak, N., Nelson, J. K., Nichol, R., Niner, E., Norman, A., Norrick, A., Nosek, T., Oh, H., Olshevskiy, A., Olson, T., Ott, J., Paley, J., Patterson, R. B., Pawloski, G., Petrova, O., Petti, R., Phan, D. D., Plunkett, R. K., Porter, J. C. C., Rafique, A., Psihas, F., Raj, V., Rajaoalisoa, M., Ramson, B., Rebel, B., Rojas, P., Ryabov, V., Samoylov, O., Sanchez, M. C., Falero, S. Sánchez, Shanahan, P., Sheshukov, A., Singh, P., Singh, V., Smith, E., Smolik, J., Snopok, P., Solomey, N., Sousa, A., Soustruznik, K., Strait, M., Suter, L., Sutton, A., Swain, S., Sweeney, C., Oregui, B. Tapia, Tas, P., Thakore, T., Thayyullathil, R. B., Thomas, J., Tiras, E., Tripathi, J., Trokan-Tenorio, J., Tsaris, A., Torun, Y., Urheim, J., Vahle, P., Vallari, Z., Vasel, J., Vokac, P., Vrba, T., Wallbank, M., Warburton, T. K., Wetstein, M., Whittington, D., Wickremasinghe, D. A., Wojcicki, S. G., Wolcott, J., Wu, W., Xiao, Y., Dombara, A. Yallappa, Yonehara, K., Yu, S., Yu, Y., Zadorozhnyy, S., Zalesak, J., Zhang, Y., and Zwaska, R.
Subjects: High Energy Physics - Experiment, Astrophysics - High Energy Astrophysical Phenomena
Abstract: A search is performed for supernova-like neutrino interactions coincident with 76 gravitational wave events detected by the LIGO/Virgo Collaboration. For 40 of these events, full readout of the time around the gravitational wave is available from the NOvA Far Detector. For these events, we set limits on the fluence of the sum of all neutrino flavors of $F < 7(4)\times 10^{10}\mathrm{cm}^{-2}$ at 90% C.L. assuming energy and time distributions corresponding to the Garching supernova models with masses 9.6(27)$\mathrm{M}_\odot$. Under the hypothesis that any given gravitational wave event was caused by a supernova, this corresponds to a distance of $r > 29(50)$kpc at 90% C.L. Weaker limits are set for other gravitational wave events with partial Far Detector data and/or Near Detector data., Comment: 10 pages, 2 figures
Published: 2021
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14. Search for active-sterile antineutrino mixing using neutral-current interactions with the NOvA experiment

Author: Acero, M. A., Adamson, P., Aliaga, L., Anfimov, N., Antoshkin, A., Arrieta-Diaz, E., Asquith, L., Aurisano, A., Back, A., Backhouse, C., Baird, M., Balashov, N., Baldi, P., Bambah, B. A., Bashar, S., Bays, K., Bernstein, R., Bhatnagar, V., Bhuyan, B., Bian, J., Blair, J., Booth, A. C., Bowles, R., Bromberg, C., Buchanan, N., Butkevich, A., Calvez, S., Carroll, T. J., Catano-Mur, E., Choudhary, B. C., Christensen, A., Coan, T. E., Colo, M., Cremonesi, L., Davies, G. S., Derwent, P. F., Ding, P., Djurcic, Z., Dolce, M., Doyle, D., Tonguino, D. Dueñas, Dukes, E. C., Duyang, H., Edayath, S., Ehrlich, R., Elkins, M., Ewart, E., Feldman, G. J., Filip, P., Franc, J., Frank, M. J., Gallagher, H. R., Gandrajula, R., Gao, F., Giri, A., Gomes, R. A., Goodman, M. C., Grichine, V., Groh, M., Group, R., Guo, B., Habig, A., Hakl, F., Hall, A., Hartnell, J., Hatcher, R., Hausner, H., Heller, K., Hewes, V, Himmel, A., Holin, A., Huang, J., Jargowsky, B., Jarosz, J., Jediny, F., Johnson, C., Judah, M., Kakorin, I., Kalra, D., Kalitkina, A., Kaplan, D. M., Keloth, R., Klimov, O., Koerner, L. W., Kolupaeva, L., Kotelnikov, S., Kralik, R., Kullenberg, Ch., Kubu, M., Kumar, A., Kuruppu, C. D., Kus, V., Lackey, T., Lasorak, P., Lang, K., Lesmeister, J., Lin, S., Lister, A., Liu, J., Lokajicek, M., Magill, S., Plata, M. Manrique, Mann, W. A., Marshak, M. L., Martinez-Casales, M., Matveev, V., Mayes, B., Méndez, D. P., Messier, M. D., Meyer, H., Miao, T., Miller, W. H., Mishra, S. R., Mislivec, A., Mohanta, R., Moren, A., Morozova, A., Mu, W., Mualem, L., Muether, M., Mulder, K., Naples, D., Nayak, N., Nelson, J. K., Nichol, R., Niner, E., Norman, A., Norrick, A., Nosek, T., Oh, H., Olshevskiy, A., Olson, T., Ott, J., Paley, J., Patterson, R. B., Pawloski, G., Petrova, O., Petti, R., Phan, D. D., Plunkett, R. K., Porter, J. C. C., Rafique, A., Raj, V., Rajaoalisoa, M., Ramson, B., Rebel, B., Rojas, P., Ryabov, V., Samoylov, O., Sanchez, M. C., Falero, S. Sánchez, Shanahan, P., Sheshukov, A., Singh, P., Singh, V., Smith, E., Smolik, J., Snopok, P., Solomey, N., Sousa, A., Soustruznik, K., Strait, M., Suter, L., Sutton, A., Swain, S., Sweeney, C., Oregui, B. Tapia, Tas, P., Thakore, T., Thayyullathil, R. B., Thomas, J., Tiras, E., Tripathi, J., Trokan-Tenorio, J., Tsaris, A., Torun, Y., Urheim, J., Vahle, P., Vallari, Z., Vasel, J., Vokac, P., Vrba, T., Wallbank, M., Warburton, T. K., Wetstein, M., Whittington, D., Wickremasinghe, D. A., Wojcicki, S. G., Wolcott, J., Wu, W., Xiao, Y., Dombara, A. Yallappa, Yonehara, K., Yu, S., Yu, Y., Zadorozhnyy, S., Zalesak, J., Zhang, Y., and Zwaska, R.
Subjects: High Energy Physics - Experiment
Abstract: This Letter reports results from the first long-baseline search for sterile antineutrinos mixing in an accelerator-based antineutrino-dominated beam. The rate of neutral-current interactions in the two NOvA detectors, at distances of 1 km and 810 km from the beam source, is analyzed using an exposure of $12.51\times10^{20}$ protons-on-target from the NuMI beam at Fermilab running in antineutrino mode. A total of $121$ of neutral-current candidates are observed at the Far Detector, compared to a prediction of $122\pm11$(stat.)$\pm15$(syst.) assuming mixing between three active flavors. No evidence for $\bar{\nu}_{\mu}\rightarrow\bar{\nu}_{s}$ oscillation is observed. Interpreting this result within a 3+1 model, constraints are placed on the mixing angles ${\theta}_{24} < 25^{\circ}$ and ${\theta}_{34} < 32^{\circ}$ at the 90% C.L. for $0.05$eV$^{2} \leq \Delta m^{2}_{41} \leq 0.5$eV$^{2}$, the range of mass splittings that produces no significant oscillations at the Near Detector. These are the first 3+1 confidence limits set using long-baseline accelerator antineutrinos., Comment: 8 pages, 4 figures
Published: 2021
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15. Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars.

Author: Wesley Brewer, Aditya Kashi, Sajal Dash, Aristeidis Tsaris, Junqi Yin, Mallikarjun Shankar, and Feiyi Wang
Published: 2024
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16. Sequence Length Scaling in Vision Transformers for Scientific Images on Frontier.

Author: Aristeidis Tsaris, Chengming Zhang 0006, Xiao Wang 0004, Junqi Yin, Siyan Liu, Moetasim Ashfaq, Ming Fan, Jong-Youl Choi, Mohamed Wahib, Dan Lu 0001, Prasanna Balaprakash, and Fei-Yue Wang 0001
Published: 2024
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17. ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability.

Author: Xiao Wang 0004, Aristeidis Tsaris, Siyan Liu, Jong-Youl Choi, Ming Fan, Wei Zhang, Junqi Yin, Moetasim Ashfaq, Dan Lu 0001, and Prasanna Balaprakash
Published: 2024
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18. Scaling Resolution of Gigapixel Whole Slide Images Using Spatial Decomposition on Convolutional Neural Networks.

Author: Aristeidis Tsaris, Joshua Romero, Thorsten Kurth, Jacob D. Hinkle, Hong-Jun Yoon, Feiyi Wang, Sajal Dash, and Georgia D. Tourassi
Published: 2023
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19. An Agenda for Multimodal Foundation Models for Earth Observation.

Author: Philipe A. Dias, Abhishek Potnis, Sreelekha Guggilam, Lexie Yang, Aristeidis Tsaris, Henry Medeiros 0001, and Dalton D. Lunga
Published: 2023
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20. IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads

Author: Saadi, Aymen Al, Alfe, Dario, Babuji, Yadu, Bhati, Agastya, Blaiszik, Ben, Brettin, Thomas, Chard, Kyle, Chard, Ryan, Coveney, Peter, Trifan, Anda, Brace, Alex, Clyde, Austin, Foster, Ian, Gibbs, Tom, Jha, Shantenu, Keipert, Kristopher, Kurth, Thorsten, Kranzlmüller, Dieter, Lee, Hyungro, Li, Zhuozhao, Ma, Heng, Merzky, Andre, Mathias, Gerald, Partin, Alexander, Yin, Junqi, Ramanathan, Arvind, Shah, Ashka, Stern, Abraham, Stevens, Rick, Tan, Li, Titov, Mikhail, Tsaris, Aristeidis, Turilli, Matteo, Van Dam, Huub, Wan, Shunzhou, and Wifling, David
Subjects: Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Computational Engineering, Finance, and Science, Quantitative Biology - Quantitative Methods
Abstract: The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2-3 billion to deliver one new drug. This is both too expensive and too slow, especially in emergencies like the COVID-19 pandemic. In silicomethodologies need to be improved to better select lead compounds that can proceed to later stages of the drug discovery protocol accelerating the entire process. No single methodological approach can achieve the necessary accuracy with required efficiency. Here we describe multiple algorithmic innovations to overcome this fundamental limitation, development and deployment of computational infrastructure at scale integrates multiple artificial intelligence and simulation-based approaches. Three measures of performance are:(i) throughput, the number of ligands per unit time; (ii) scientific performance, the number of effective ligands sampled per unit time and (iii) peak performance, in flop/s. The capabilities outlined here have been used in production for several months as the workhorse of the computational infrastructure to support the capabilities of the US-DOE National Virtual Biotechnology Laboratory in combination with resources from the EU Centre of Excellence in Computational Biomedicine.
Published: 2020

21. Track Seeding and Labelling with Embedded-space Graph Neural Networks

Author: Choma, Nicholas, Murnane, Daniel, Ju, Xiangyang, Calafiura, Paolo, Conlon, Sean, Farrell, Steven, Prabhat, Cerati, Giuseppe, Gray, Lindsey, Klijnsma, Thomas, Kowalkowski, Jim, Spentzouris, Panagiotis, Vlimant, Jean-Roch, Spiropulu, Maria, Aurisano, Adam, Hewes, V, Tsaris, Aristeidis, Terao, Kazuhiro, and Usher, Tracy
Subjects: Physics - Instrumentation and Detectors, Computer Science - Machine Learning, High Energy Physics - Experiment, Physics - Computational Physics
Abstract: To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promising of these solutions, graph neural networks (GNN), process the event as a graph that connects track measurements (detector hits corresponding to nodes) with candidate line segments between the hits (corresponding to edges). Detector information can be associated with nodes and edges, enabling a GNN to propagate the embedded parameters around the graph and predict node-, edge- and graph-level observables. Previously, message-passing GNNs have shown success in predicting doublet likelihood, and we here report updates on the state-of-the-art architectures for this task. In addition, the Exa.TrkX project has investigated innovations in both graph construction, and embedded representations, in an effort to achieve fully learned end-to-end track finding. Hence, we present a suite of extensions to the original model, with encouraging results for hitgraph classification. In addition, we explore increased performance by constructing graphs from learned representations which contain non-linear metric structure, allowing for efficient clustering and neighborhood queries of data points. We demonstrate how this framework fits in with both traditional clustering pipelines, and GNN approaches. The embedded graphs feed into high-accuracy doublet and triplet classifiers, or can be used as an end-to-end track classifier by clustering in an embedded space. A set of post-processing methods improve performance with knowledge of the detector physics. Finally, we present numerical results on the TrackML particle tracking challenge dataset, where our framework shows favorable results in both seeding and track finding., Comment: Proceedings submission in Connecting the Dots Workshop 2020, 10 pages
Published: 2020

22. The GlueX Beamline and Detector

Author: Adhikari, S., Akondi, C. S., Ghoul, H. Al, Ali, A., Amaryan, M., Anassontzis, E. G., Austregesilo, A., Barbosa, F., Barlow, J., Barnes, A., Barriga, E., Barsotti, R., Beattie, T. D., Benesch, J., Berdnikov, V. V., Biallas, G., Black, T., Boeglin, W., Brindza, P., Briscoe, W. J., Britton, T., Brock, J., Brooks, W. K., Cannon, B. E., Carlin, C., Carman, D. S., Carstens, T., Cao, N., Chernyshov, O., Chudakov, E., Cole, S., Cortes, O., Crahen, W. D., Crede, V., Dalton, M. M., Daniels, T., Deur, A., Dickover, C., Dobbs, S., Dolgolenko, A., Dotel, R., Dugger, M., Dzhygadlo, R., Dzierba, A., Egiyan, H., Erbora, T., Ernst, A., Eugenio, P., Fanelli, C., Fegan, S., Foda, A. M., Foote, J., Frye, J., Furletov, S., Gan, L., Gasparian, A., Gerasimov, A., Gevorgyan, N., Gleason, C., Goetzen, K., Goryachev, A. Goncalves V. S., Guo, L., Hakobyan, H., Hamdi, A., Hardin, J., Henschel, C. L., Huber, G. M., Hutton, C., Hurley, A., Ioannou, P., Ireland, D. G., Ito, M. M., Jarvis, N. S., Jones, R. T., Kakoyan, V., Katsaganis, S., Kalicy, G., Kamel, M., Keith, C. D., Klein, F. J., Kliemt, R., Kolybaba, D., Kourkoumelis, C., Krueger, S. T., Kuleshov, S., Larin, I., Lawrence, D., Leckey, J. P., Lersch, D. I., Leverington, B. D., Levine, W. I., Li, W., Liu, B., Livingston, K., Lolos, G. J., Lyubovitskij, V., Mack, D., Marukyan, H., Mattione, P. T., Matveev, V., McCaughan, M., McCracken, M., McGinley, W., McIntyre, J., Meekins, D., Mendez, R., Meyer, C. A., Miskimen, R., Mitchell, R. E., Mokaya, F., Moriya, K., Nerling, F., Ng, L., Ni, H., Ostrovidov, A. I., Papandreou, Z., Patsyuk, M., Paudel, C., Pauli, P., Pedroni, R., Pentchev, L., Peters, K. J., Phelps, W., Pierce, J., Pooser, E., Popov, V., Pratt, B., Qiang, Y., Qin, N., Razmyslovich, V., Reinhold, J., Ritchie, B. G., Ritman, J., Robison, L., Romanov, D., Romero, C., Salgado, C., Sandoval, N., Satogata, T., Schertz, A. M., Schadmand, S., Schick, A., Schumacher, R. A., Schwarz, C., Schwiening, J., Semenov, A. Yu., Semenova, I. A., Seth, K. K., Shen, X., Shepherd, M. R., Smith, E. S., Sober, D. I., Somov, A., Somov, S., Soto, O., Sparks, N., Staib, M. J., Stanislav, C., Stevens, J. R., Stewart, J., Strakovsky, I. I., Summner, B. C. L., Suresh, K., Tarasov, V. V., Taylor, S., Teigrob, L. A., Teymurazyan, A., Thiel, A., Tolstukhin, I., Tomaradze, A., Toro, A., Tsaris, A., Van Haarlem, Y., Vasileiadis, G., Vega, I., Visser, G., Voulgaris, G., Walford, N. K., Werthmüller, D., Whitlatch, T., Wickramaarachchi, N., Williams, M., Wolin, E., Xiao, T., Yang, Y., Zarling, J., Zhang, Z., Zhou, Q., Zhou, X., and Zihlmann, B.
Subjects: Physics - Instrumentation and Detectors, Nuclear Experiment
Abstract: The GlueX experiment at Jefferson Lab has been designed to study photoproduction reactions with a 9-GeV linearly polarized photon beam. The energy and arrival time of beam photons are tagged using a scintillator hodoscope and a scintillating fiber array. The photon flux is determined using a pair spectrometer, while the linear polarization of the photon beam is determined using a polarimeter based on triplet photoproduction. Charged-particle tracks from interactions in the central target are analyzed in a solenoidal field using a central straw-tube drift chamber and six packages of planar chambers with cathode strips and drift wires. Electromagnetic showers are reconstructed in a cylindrical scintillating fiber calorimeter inside the magnet and a lead-glass array downstream. Charged particle identification is achieved by measuring energy loss in the wire chambers and using the flight time of particles between the target and detectors outside the magnet. The signals from all detectors are recorded with flash ADCs and/or pipeline TDCs into memories allowing trigger decisions with a latency of 3.3 $\mu$s. The detector operates routinely at trigger rates of 40 kHz and data rates of 600 megabytes per second. We describe the photon beam, the GlueX detector components, electronics, data-acquisition and monitoring systems, and the performance of the experiment during the first three years of operation., Comment: Accepted by Nuclear Instruments and Methods A, 78 pages, 54 figures
Published: 2020
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23. Supernova neutrino detection in NOvA

Author: NOvA Collaboration, Acero, M. A., Adamson, P., Agam, G., Aliaga, L., Alion, T., Allakhverdian, V., Anfimov, N., Antoshkin, A., Arrieta-Diaz, E., Asquith, L., Aurisano, A., Back, A., Backhouse, C., Baird, M., Balashov, N., Baldi, P., Bambah, B. A., Bashar, S., Bays, K., Bending, S., Bernstein, R., Bhatnagar, V., Bhuyan, B., Bian, J., Blair, J., Booth, A. C., Bour, P., Bowles, R., Bromberg, C., Buchanan, N., Butkevich, A., Bychkov, V., Calvez, S., Carroll, T. J., Catano-Mur, E., Childress, S., Choudhary, B. C., Coan, T. E., Colo, M., Corwin, L., Cremonesi, L., Davies, G. S., Derwent, P. F., Ding, P., Djurcic, Z., Dolce, M., Doyle, D., Tonguino, D. Dueñas, Dukes, E. C., Dung, P., Duyang, H., Edayath, S., Ehrlich, R., Elkins, M., Feldman, G. J., Filip, P., Flanagan, W., Franc, J., Frank, M. J., Gallagher, H. R., Gandrajula, R., Gao, F., Germani, S., Giri, A., Gomes, R. A., Goodman, M. C., Grichine, V., Groh, M., Group, R., Guo, B., Habig, A., Hakl, F., Hall, A., Hartnell, J., Hatcher, R., Hatzikoutelis, A., Heller, K., Hewes, V, Himmel, A., Holin, A., Howard, B., Huang, J., Hylen, J., Jediny, F., Johnson, C., Judah, M., Kakorin, I., Kalra, D., Kaplan, D. M., Keloth, R., Klimov, O., Koerner, L. W., Kolupaeva, L., Kotelnikov, S., Kubu, M., Kullenberg, Ch., Kumar, A., Kuruppu, C. D., Kus, V., Lackey, T., Lang, K., Li, L., Lin, S., Lister, A., Lokajicek, M., Luchuk, S., Magill, S., Mann, W. A., Marshak, M. L., Martinez-Casales, M., Matveev, V., Mayes, B., Méndez, D. P., Messier, M. D., Meyer, H., Miao, T., Miller, W. H., Mishra, S. R., Mislivec, A., Mohanta, R., Moren, A., Morozova, A., Mualem, L., Muether, M., Mufson, S., Mulder, K., Murphy, R., Musser, J., Naples, D., Nayak, N., Nelson, J. K., Nichol, R., Nikseresht, G., Niner, E., Norman, A., Norrick, A., Nosek, T., Olshevskiy, A., Olson, T., Paley, J., Patterson, R. B., Pawloski, G., Petrova, O., Petti, R., Plunkett, R. K., Psihas, F., Rafique, A., Raj, V., Ramson, B., Rebel, B., Rojas, P., Ryabov, V., Samoylov, O., Sanchez, M. C., Falero, S. Sánchez, Seong, I. S., Shanahan, P., Sheshukov, A., Singh, P., Singh, V., Smith, E., Smolik, J., Snopok, P., Solomey, N., Sousa, A., Soustruznik, K., Strait, M., Suter, L., Sutton, A., Sweeney, C., Talaga, R. L., Oregui, B. Tapia, Tas, P., Thayyullathil, R. B., Thomas, J., Tiras, E., Torbunov, D., Tripathi, J., Tsaris, A., Torun, Y., Urheim, J., Vahle, P., Vallari, Z., Vasel, J., Vokac, P., Vrba, T., Wallbank, M., Warburton, T. K., Wetstein, M., Whittington, D., Wickremasinghe, D. A., Wojcicki, S. G., Wolcott, J., Dombara, A. Yallappa, Yonehara, K., Yu, S., Yu, Y., Zadorozhnyy, S., Zalesak, J., Zhang, Y., and Zwaska, R.
Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract: The NOvA long-baseline neutrino experiment uses a pair of large, segmented, liquid-scintillator calorimeters to study neutrino oscillations, using GeV-scale neutrinos from the Fermilab NuMI beam. These detectors are also sensitive to the flux of neutrinos which are emitted during a core-collapse supernova through inverse beta decay interactions on carbon at energies of $\mathcal{O}(10~\text{MeV})$. This signature provides a means to study the dominant mode of energy release for a core-collapse supernova occurring in our galaxy. We describe the data-driven software trigger system developed and employed by the NOvA experiment to identify and record neutrino data from nearby galactic supernovae. This technique has been used by NOvA to self-trigger on potential core-collapse supernovae in our galaxy, with an estimated sensitivity reaching out to 10~kpc distance while achieving a detection efficiency of 23\% to 49\% for supernovae from progenitor stars with masses of 9.6\~M$_\odot$ to 27\~M$_\odot$, respectively., Comment: 30 pages, 17 figures
Published: 2020
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24. Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

Author: Ju, Xiangyang, Farrell, Steven, Calafiura, Paolo, Murnane, Daniel, Prabhat, Gray, Lindsey, Klijnsma, Thomas, Pedro, Kevin, Cerati, Giuseppe, Kowalkowski, Jim, Perdue, Gabriel, Spentzouris, Panagiotis, Tran, Nhan, Vlimant, Jean-Roch, Zlokapa, Alexander, Pata, Joosep, Spiropulu, Maria, An, Sitong, Aurisano, Adam, Hewes, V, Tsaris, Aristeidis, Terao, Kazuhiro, and Usher, Tracy
Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment, Physics - Computational Physics, Physics - Data Analysis, Statistics and Probability
Abstract: Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems., Comment: Presented at NeurIPS 2019 Workshop "Machine Learning and the Physical Sciences"
Published: 2020

25. First measurement of neutrino oscillation parameters using neutrinos and antineutrinos by NOvA

Author: Acero, M. A., Adamson, P., Aliaga, L., Alion, T., Allakhverdian, V., Altakarli, S., Anfimov, N., Antoshkin, A., Aurisano, A., Back, A., Backhouse, C., Baird, M., Balashov, N., Baldi, P., Bambah, B. A., Bashar, S., Bays, K., Bending, S., Bernstein, R., Bhatnagar, V., Bhuyan, B., Bian, J., Blackburn, T., Blair, J., Booth, A. C., Bour, P., Bromberg, C., Buchanan, N., Butkevich, A., Calvez, S., Campbell, M., Carroll, T. J., Catano-Mur, E., Cedeno, A., Childress, S., Choudhary, B. C., Chowdhury, B., Coan, T. E., Colo, M., Cooper, J., Corwin, L., Cremonesi, L., Davies, G. S., Derwent, P. F., Ding, P., Djurcic, Z., Doyle, D., Dukes, E. C., Duyang, H., Edayath, S., Ehrlich, R., Elkins, M., Feldman, G. J., Filip, P., Flanagan, W., Frank, M. J., Gallagher, H. R., Gandrajula, R., Gao, F., Germani, S., Giri, A., Gomes, R. A., Goodman, M. C., Grichine, V., Groh, M., Group, R., Guo, B., Habig, A., Hakl, F., Hartnell, J., Hatcher, R., Hatzikoutelis, A., Heller, K., Hewes, V, Himmel, A., Holin, A., Howard, B., Huang, J., Hylen, J., Jediny, F., Johnson, C., Judah, M., Kakorin, I., Kalra, D., Kaplan, D. M., Keloth, R., Klimov, O., Koerner, L. W., Kolupaeva, L., Kotelnikov, S., Kreymer, A., Kulenberg, Ch., Kumar, A., Kuruppu, C. D., Kus, V., Lackey, T., Lang, K., Lin, S., Lokajicek, M., Lozier, J., Luchuk, S., Maan, K., Magill, S., Mann, W. A., Marshak, M. L., Martinez-Casales, M., Matveev, V., Mendez, D. P., Messier, M. D., Meyer, H., Miao, T., Miller, W. H., Mishra, S. R., Mislivec, A., Mohanta, R., Moren, A., Mualem, L., Muether, M., Mufson, S., Mulder, K., Murphy, R., Musser, J., Naples, D., Nayak, N., Nelson, J. K., Nichol, R., Nikseresht, G., Niner, E., Norman, A., Nosek, T., Olshevskiy, A., Olson, T., Paley, J., Patterson, R. B., Pawloski, G., Pershey, D., Petrova, O., Petti, R., Phan, D. D., Plunkett, R. K., Potukuchi, B., Principato, C., Psihas, F., Radovic, A., Raj, V., Rameika, R. A., Rebel, B., Rojas, P., Ryabov, V., Samoylov, O., Sanchez, M. C., Falero, S. Sanchez, Seong, I. S., Shanahan, P., Sheshukov, A., Singh, P., Singh, V., Smith, E., Smolik, J., Snopok, P., Solomey, N., Song, E., Sousa, A., Soustruznik, K., Strait, M., Suter, L., Sutton, A., Talaga, R. L., Oregui, B. Tapia, Tas, P., Thayyullathil, R. B., Thomas, J., Tiras, E., Torbunov, D., Tripathi, J., Tsaris, A., Torun, Y., Urheim, J., Vahle, P., Vasel, J., Vinton, L., Vokac, P., Vrba, T., Wallbank, M., Wang, B., Warburton, T. K., Wetstein, M., While, M., Whittington, D., Wojcicki, S. G., Wolcott, J., Yadav, N., Dombara, A. Yallappa, Yonehara, K., Yu, S., Zadorozhnyy, S., Zalesak, J., Zamorano, B., and Zwaska, R.
Subjects: High Energy Physics - Experiment
Abstract: The NOvA experiment has made a $4.4\sigma$-significant observation of $\bar\nu_{e}$ appearance in a 2 GeV $\bar\nu_{\mu}$ beam at a distance of 810 km. Using $12.33\times10^{20}$ protons on target delivered to the Fermilab NuMI neutrino beamline, the experiment recorded 27 $\bar\nu_{\mu} \rightarrow \bar\nu_{e}$ candidates with a background of 10.3 and 102 $\bar\nu_{\mu} \rightarrow \bar\nu_{\mu}$ candidates. This new antineutrino data is combined with neutrino data to measure the oscillation parameters $|\Delta m^2_{32}| = 2.48^{+0.11}_{-0.06}\times10^{-3}$ eV$^2/c^4$, $\sin^2 \theta_{23} = 0.56^{+0.04}_{-0.03}$ in the normal neutrino mass hierarchy and upper octant and excludes most values near $\delta_{\rm CP}=\pi/2$ for the inverted mass hierarchy by more than 3$\sigma$. The data favor the normal neutrino mass hierarchy by 1.9$\sigma$ and $\theta_{23}$ values in the upper octant by 1.6$\sigma$., Comment: 8 pages, 3 figures. Supplementary material attached (6 figures). To view attachments, please download and extract the gzipped tar source file listed under "Other formats". Fixed supplementary material to include just the compiled pdf not the Latex Source
Published: 2019
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26. Observation of seasonal variation of atmospheric multiple-muon events in the NOvA Near Detector

Author: Acero, M. A., Adamson, P., Aliaga, L., Alion, T., Allakhverdian, V., Altakarli, S., Anmov, N., Antoshkin, A., Aurisano, A., Back, A., Backhouse, C., Baird, M., Balashov, N., Baldi, P., Bambah, B. A., Bashar, S., Bays, K., Bending, S., Bernstein, R., Bhatnagar, V., Bhuyan, B., Bian, J., Blair, J., Booth, A. C., Bour, P., Bromberg, C., Buchanan, N., Butkevich, A., Calvez, S., Campbell, M., Carroll, T. J., Catano-Mur, E., Cedeno, A., Childress, S., Choudhary, B. C., Chowdhury, B., Coan, T. E., Colo, M., Corwin, L., Cremonesi, L., Davies, G. S., Derwent, P. F., Ding, P., Djurcic, Z., Doyle, D., Dukes, E. C., Duyang, H., Edayath, S., Ehrlich, R., Feldman, G. J., Filip, P., Flanagan, W., Frank, M. J., Gallagher, H. R., Gandrajula, R., Gao, F., Germani, S., Giri, A., Gomes, R. A., Goodman, M. C., Grichine, V., Groh, M., Group, R., Guo, B., Habig, A., Hakl, F., Hartnell, J., Hatcher, R., Hatzikoutelis, A., Heller, K., Hewes, V, Himmel, A., Holin, A., Howard, B., Huang, J., Hylen, J., Jediny, F., Johnson, C., Judah, M., Kakorin, I., Kalra, D., Kaplan, D. M., Keloth, R., Klimov, O., Koerner, L. W., Kolupaeva, L., Kotelnikov, S., Kreymer, A., Kulenberg, Ch., Kumar, A., Kuruppu, C. D., Kus, V., Lackey, T., Lang, K., Lin, S., Lokajicek, M., Lozier, J., Luchuk, S., Magill, S., Mann, W. A., Marshak, M. L., Matveev, V., Mendez, D. P., Messier, M. D., Meyer, H., Miao, T., Miller, W. H., Mishra, S. R., Mislivec, A., Mohanta, R., Moren, A., Mualem, L., Muether, M., Mufson, S., Mulder, K., Murphy, R., Musser, J., Naples, D., Nayak, N., Nelson, J. K., Nichol, R., Nikseresht, G., Niner, E., Norman, A., Nosek, T., Olshevskiy, A., Olson, T., Paley, J., Patterson, R. B., Pawloski, G., Petrova, O., Petti, R., Phan, D. D., Plunkett, R. K., Potukuchi, B., Principato, C., Psihas, F., Raj, V., Rameika, R. A., Rebel, B., Rojas, P., Ryabov, V., Samoylov, O., Sanchez, M. C., Schreiner, P., Seong, I. S., Shanahan, P., Sheshukov, A., Singh, P., Singh, V., Smith, E., Smolik, J., Snopok, P., Solomey, N., Song, E., Sousa, A., Soustruznik, K., Strait, M., Suter, L., Sutton, A., Talaga, R. L., Tas, P., Thayyullathil, R. B., Thomas, J., Tiras, E., Tognini, S. C., Torbunov, D., Tripathi, J., Tsaris, A., Torun, Y., Urheim, J., Vahle, P., Vasel, J., Vinton, L., Vokac, P., Vrba, T., Wallbank, M., Wang, B., Warburton, T. K., Wetstein, M., While, M., Whittington, D., Wojcicki, S. G., Wolcott, J., Yadav, N., Dombara, A. Yallappa, Yonehara, K., Yu, S., Zadorozhnyy, S., Zalesak, J., and Zwaska, R.
Subjects: Physics - Instrumentation and Detectors, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: Using two years of data from the NOvA Near Detector at Fermilab, we report a seasonal variation of cosmic ray induced multiple-muon event rates which has an opposite phase to the seasonal variation in the atmospheric temperature. The strength of the seasonal multipl$ increase as a function of the muon multiplicity. However, no significant dependence of the strength of the seasonal variation of the multiple-muon variation is seen as a function of the muon zenith angle, or the spatial or angular separation between the correlated muons.
Published: 2019
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27. FPGA-accelerated machine learning inference as a service for particle physics computing

Author: Duarte, Javier, Harris, Philip, Hauck, Scott, Holzman, Burt, Hsu, Shih-Chieh, Jindariani, Sergo, Khan, Suffian, Kreis, Benjamin, Lee, Brian, Liu, Mia, Lončar, Vladimir, Ngadiuba, Jennifer, Pedro, Kevin, Perez, Brandon, Pierini, Maurizio, Rankin, Dylan, Tran, Nhan, Trahms, Matthew, Tsaris, Aristeidis, Versteeg, Colin, Way, Ted W., Werran, Dustin, and Wu, Zhenbin
Subjects: Physics - Data Analysis, Statistics and Probability, High Energy Physics - Experiment, Physics - Computational Physics, Physics - Instrumentation and Detectors
Abstract: New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains. The growing applications of machine learning algorithms in particle physics for simulation, reconstruction, and analysis are naturally deployed on such platforms. We demonstrate that the acceleration of machine learning inference as a web service represents a heterogeneous computing solution for particle physics experiments that potentially requires minimal modification to the current computing model. As examples, we retrain the ResNet-50 convolutional neural network to demonstrate state-of-the-art performance for top quark jet tagging at the LHC and apply a ResNet-50 model with transfer learning for neutrino event classification. Using Project Brainwave by Microsoft to accelerate the ResNet-50 image classification model, we achieve average inference times of 60 (10) milliseconds with our experimental physics software framework using Brainwave as a cloud (edge or on-premises) service, representing an improvement by a factor of approximately 30 (175) in model inference latency over traditional CPU inference in current experimental hardware. A single FPGA service accessed by many CPUs achieves a throughput of 600--700 inferences per second using an image batch of one, comparable to large batch-size GPU throughput and significantly better than small batch-size GPU throughput. Deployed as an edge or cloud service for the particle physics computing model, coprocessor accelerators can have a higher duty cycle and are potentially much more cost-effective., Comment: 16 pages, 14 figures, 2 tables
Published: 2019
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28. Measurement of Neutrino-Induced Neutral-Current Coherent $\pi^0$ Production in the NOvA Near Detector

Author: Acero, M. A., Adamson, P., Aliaga, L., Alion, T., Allakhverdian, V., Anfimov, N., Antoshkin, A., Arrieta-Diaz, E., Aurisano, A., Back, A., Backhouse, C., Baird, M., Balashov, N., Baldi, P., Bambah, B. A., Basher, S., Bays, K., Behera, B., Bending, S., Bernstein, R., Bhatnagar, V., Bhuyan, B., Bian, J., Blair, J., Booth, A. C., Bolshakova, A., Bour, P., Bromberg, C., Buchanan, N., Butkevich, A., Campbell, M., Carroll, T. J., Catano-Mur, E., Childress, S., Choudhary, B. C., Chowdhury, B., Coan, T. E., Colo, M., Corwin, L., Cremonesi, L., Cronin-Hennessy, D., Davies, G. S., Derwent, P. F., Ding, P., Djurcic, Z., Doyle, D., Dukes, E. C., Dung, P., Duyang, H., Edayath, S., Ehrlich, R., Feldman, G. J., Flanagan, W., Frank, M. J., Gallagher, H. R., Gandrajula, R., Gao, F., Germani, S., Giri, A., Gomes, R. A., Goodman, M. C., Grichine, V., Groh, M., Group, R., Guo, B., Habig, A., Hakl, F., Hartnell, J., Hatcher, R., Hatzikoutelis, A., Heller, K., Himmel, A., Holin, A., Howard, B., Huang, J., Hylen, J., Jediny, F., Johnson, C., Judah, M., Kakorin, I., Kalra, D., Kaplan, D. M., Keloth, R., Klimov, O., Koerner, L. W., Kolupaeva, L., Kotelnikov, S., Kreymer, A., Kulenberg, Ch., Kumar, A., Kuruppu, C. D., Kus, V., Lackey, T., Lang, K., Lin, S., Lokajicek, M., Lozier, J., Luchuk, S., Maan, K., Magill, S., Mann, W. A., Marshak, M. L., Matveev, V., Méndez, D. P., Messier, M. D., Meyer, H., Miao, T., Miller, W. H., Mishra, S. R., Mislivec, A., Mohanta, R., Moren, A., Mualem, L., Muether, M., Mulder, K., Mufson, S., Murphy, R., Musser, J., Naples, D., Nayak, N., Nelson, J. K., Nichol, R., Niner, E., Norman, A., Nosek, T., Oksuzian, Y., Olshevskiy, A., Olson, T., Paley, J., Patterson, R. B., Pawloski, G., Pershey, D., Petrova, O., Petti, R., Plunkett, R. K., Potukuchi, B., Principato, C., Psihas, F., Raj, V., Radovic, A., Rameika, R. A., Rebel, B., Rojas, P., Ryabov, V., Sachdev, K., Samoylov, O., Sanchez, M. C., Seong, I. S., Shanahan, P., Sheshukov, A., Singh, P., Singh, V., Smith, E., Smolik, J., Snopok, P., Solomey, N., Song, E., Sousa, A., Soustruznik, K., Strait, M., Suter, L., Talaga, R. L., Tas, P., Thayyullathil, R. B., Thomas, J., Tiras, E., Torbunov, D., Tripathi, J., Tsaris, A., Torun, Y., Urheim, J., Vahle, P., Vasel, J., Vinton, L., Vokac, P., Vrba, T., Wang, B., Warburton, T. K., Wetstein, M., While, M., Whittington, D., Wojcicki, S. G., Wolcott, J., Yadav, N., Dombara, A. Yallappa, Yang, S., Yonehara, K., Yu, S., Zalesak, J., Zamorano, B., and Zwaska, R.
Subjects: High Energy Physics - Experiment
Abstract: The cross section of neutrino-induced neutral-current coherent $\pi^0$ production on a carbon-dominated target is measured in the NOvA near detector. This measurement uses a narrow-band neutrino beam with an average neutrino energy of 2.7\,GeV, which is of interest to ongoing and future long-baseline neutrino oscillation experiments. The measured, flux-averaged cross section is $\sigma = 13.8\pm0.9 (\text{stat})\pm2.3 (\text{syst}) \times 10^{-40}\,\text{cm}^2/\text{nucleus}$, consistent with model prediction. This result is the most precise measurement of neutral-current coherent $\pi^0$ production in the few-GeV neutrino energy region.
Published: 2019
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29. A Scalable Pipeline for Gigapixel Whole Slide Imaging Analysis on Leadership Class HPC Systems.

Author: Sajal Dash, Benjamín Hernández, Aristeidis Tsaris, Folami T. Alamudun, Hong-Jun Yoon, and Feiyi Wang
Published: 2022
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30. Image Gradient Decomposition for Parallel and Memory-Efficient Ptychographic Reconstruction.

Author: Xiao Wang 0004, Aristeidis Tsaris, Debangshu Mukherjee, Mohamed Wahib, Peng Chen 0035, Mark Oxley, Olga Ovchinnikova, and Jacob D. Hinkle
Published: 2022
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31. Distilling Knowledge from Ensembles of Cluster-Constrained-Attention Multiple-Instance Learners for Whole Slide Image Classification.

Author: Folami Alamudun, Jacob D. Hinkle, Sajal Dash, Benjamín Hernández, Aristeidis Tsaris, and Hong-Jun Yoon
Published: 2022
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32. AI Benchmarking for Science: Efforts from the MLCommons Science Working Group.

Author: Jeyan Thiyagalingam, Gregor von Laszewski, Junqi Yin, Murali Emani, Juri Papay, Gregg Barrett, Piotr Luszczek, Aristeidis Tsaris, Christine R. Kirkpatrick, Feiyi Wang, Tom Gibbs, Venkatram Vishwanath, Mallikarjun Shankar, Geoffrey C. Fox, and Tony Hey
Published: 2022
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33. AI Benchmarking for Science: Efforts from the MLCommons Science Working Group

Author: Thiyagalingam, Jeyan, von Laszewski, Gregor, Yin, Junqi, Emani, Murali, Papay, Juri, Barrett, Gregg, Luszczek, Piotr, Tsaris, Aristeidis, Kirkpatrick, Christine, Wang, Feiyi, Gibbs, Tom, Vishwanath, Venkatram, Shankar, Mallikarjun, Fox, Geoffrey, Hey, Tony, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Anzt, Hartwig, editor, Bienz, Amanda, editor, Luszczek, Piotr, editor, and Baboulin, Marc, editor
Published: 2022
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34. Novel deep learning methods for track reconstruction

Author: Farrell, Steven, Calafiura, Paolo, Mudigonda, Mayur, Prabhat, Anderson, Dustin, Vlimant, Jean-Roch, Zheng, Stephan, Bendavid, Josh, Spiropulu, Maria, Cerati, Giuseppe, Gray, Lindsey, Kowalkowski, Jim, Spentzouris, Panagiotis, and Tsaris, Aristeidis
Subjects: High Energy Physics - Experiment, Physics - Data Analysis, Statistics and Probability
Abstract: For the past year, the HEP.TrkX project has been investigating machine learning solutions to LHC particle track reconstruction problems. A variety of models were studied that drew inspiration from computer vision applications and operated on an image-like representation of tracking detector data. While these approaches have shown some promise, image-based methods face challenges in scaling up to realistic HL-LHC data due to high dimensionality and sparsity. In contrast, models that can operate on the spacepoint representation of track measurements ("hits") can exploit the structure of the data to solve tasks efficiently. In this paper we will show two sets of new deep learning models for reconstructing tracks using space-point data arranged as sequences or connected graphs. In the first set of models, Recurrent Neural Networks (RNNs) are used to extrapolate, build, and evaluate track candidates akin to Kalman Filter algorithms. Such models can express their own uncertainty when trained with an appropriate likelihood loss function. The second set of models use Graph Neural Networks (GNNs) for the tasks of hit classification and segment classification. These models read a graph of connected hits and compute features on the nodes and edges. They adaptively learn which hit connections are important and which are spurious. The models are scaleable with simple architecture and relatively few parameters. Results for all models will be presented on ACTS generic detector simulated data., Comment: CTD 2018 proceedings
Published: 2018

35. The DUNE Far Detector Interim Design Report, Volume 3: Dual-Phase Module

Author: DUNE Collaboration, Abi, B., Acciarri, R., Acero, M. A., Adamowski, M., Adams, C., Adams, D., Adamson, P., Adinolfi, M., Ahmad, Z., Albright, C. H., Soplin, L. Aliaga, Alion, T., Monsalve, S. Alonso, Alrashed, M., Alt, C., Anderson, J., Anderson, K., Andreopoulos, C., Andrews, M. P., Andrews, R. A., Ankowski, A., Anthony, J., Antonello, M., Antonova, M., Antusch, S., Fernandez, A. Aranda, Ariga, A., Ariga, T., Sierra, D. Aristizabal, Diaz, E. Arrieta, Asaadi, J., Ascencio, M., Asner, D., Athar, M. S., Auger, M., Aurisano, A., Aushev, V., Autiero, D., Azfar, F., Back, A., Back, H., Back, J., Backhouse, C., Baesso, P., Bagby, L., Bai, X., Baird, M., Balantekin, B., Balasubramanian, S., Baller, B., Ballett, P., Balleyguier, L., Bambah, B., Band, H., Bansal, M., Bansal, S., Barenboim, G., Barker, G. J., Barnes, C., Barr, G., Monarca, J. Barranco, Barros, N., Barrow, J., Bashyal, A., Basque, V., Bass, M., Bay, F., Bays, K., Bazo, J. L., Beacom, J. F., Bechetoille, E., Behera, B. R., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Belver, D., Benekos, N., Benetti, P. A., Bercellie, A., Berman, E., Bernardini, P., Berner, R., Berns, H. G., Bernstein, R. H., Bertolucci, S., Betancourt, M., Bhatnagar, V., Bhattacharjee, M., Bhuyan, B., Biagi, S., Bian, J., Biery, K., Bilki, B., Bishai, M., Bitadze, A., Blackburn, T., Blake, A., Siffert, B. Blanco, Blaszczyk, F., Blaufuss, E., Blazey, G. C., Blennow, M., Blucher, E., Bocean, V., Boffelli, F., Boissevain, J., Bolognesi, S., Bolton, T., Bonesini, M., Boone, T., Booth, A., Booth, C., Bordoni, S., Borkum, A., Boschi, T., Bour, P., Bourguille, B., Boyd, S. B., Boyden, D., Bracinik, J., Brailsford, D., Brandt, A., Bremer, J., Brice, S. J., Bromberg, C., Brooijmans, G., Brooke, J., Brown, G., Buchanan, N., Budd, H., de Holanda, P. C., Cai, T., Caiulo, D., Calafiura, P., Calatayud, A., Calcutt, J., Callahan, C., Calligarich, E., Calvo, E., Camilleri, L., Caminata, A., Campanelli, M., Cancelo, G., Cankocak, K., Cantini, C., Caratelli, D., Carlus, B., Carneiro, M., Terrazas, I. Caro, Carroll, T. J., Carvallo, M. P., Cascella, M., Castromonte, C., Catano-Mur, E., Cavalli-Sforza, M., Cavanna, F., Cazzato, E., Centro, S., Cerati, G., Cervelli, A., Villanueva, A. Cervera, Cervi, T., Chalifour, M., Chappuis, A., Chatterjee, A., Chattopadhyay, S., Chaves, J., Chen, H., Chen, M. -C., Chen, S., Cherdack, D., Chi, C. -Y., Childress, S., Cho, K., Choubey, S., Choudhary, B. C., Christensen, A., Christian, D., Christodoulou, G., Christofferson, C. -A., Church, E., Clarke, P., Coan, T. E., Cocco, A., Collin, G. H., Conley, E., Conrad, J. M., Convery, M., Corey, R., Corwin, L., Cotte, P., Cremonesi, L., Crespo-Anadón, J. I., Prats, J. Creus, Cristaldo, E., Crivelli, P., Cronin-Hennessy, D., Crowley, C., Cuesta, C., Curioni, A., Cussans, D., Dabrowski, M., Dale, D., Da Motta, H., Davenne, T., Davenport, E., Davies, G. S., Davies, J., Davini, S., Dawson, J., De, K., Decowski, M. P., Neto, P. Dedin, Astiz, I. de Icaza, Delbart, A., Delepine, D., Delgado, M., Dell, A., Neto, J. de Mello, DeMuth, D., Deng, Z., Dennis, S., Densham, C., De Bonis, I., De Gouvêa, A., De Jong, P., De Lurgio, P., De Rijck, S., De Roeck, A., de Vries, J. J., Dharmapalan, R., Dhingra, N., Diamantopoulou, M., Diaz, F., Díaz, J. S., Bautista, G. Diaz, Ding, P., Distefano, C., Diwan, M., Di Domizio, S., Di Giulio, L., Di Luise, S., Djurcic, Z., Doizon, F., Dokania, N., Dolinski, M. J., Dong, R., Anjos, J. dos, Douglas, D., Drake, G., Duchesneau, D., Duffy, K., Dung, B., Dutta, D., Duvernois, M., Duyang, H., Dvornikov, O., Dwyer, D. A., Dye, S., Dyshkant, A. S., Dytman, S., Eads, M., Eberly, B., Edmunds, D., Eisch, J., Elagin, A., Elliott, S., Ellsworth, W., Elnimr, M., Emery, S., Eno, S., Ereditato, A., Escobar, C. O., Sanchez, L. Escudero, Evans, J. J., Ezeribe, A., Fahey, K., Falcone, A., Falk, L., Farbin, A., Farnese, C., Farzan, Y., Fasoli, M., Fava, A., Felix, J., Fernandez-Martinez, E., Menendez, P. Fernandez, Ferraro, F., Feyzi, F., Fields, L., Filkins, A., Filthaut, F., Finch, A., Fischer, O., Fitton, M., Fitzpatrick, R., Flanagan, W., Fleming, B. T., Flight, R., Forest, T., Fowler, J., Fox, W., Franc, J., Francis, K., Franchini, P., Franco, D., Freeman, J., Freestone, J., Fried, J., Friedland, A., Fuess, S., Furic, I., Furmanski, A., Gago, A. M., Gallagher, H., Gallego-Ros, A., Galymov, V., Gamberini, E., Gambetta, S., Gamble, T., Gandhi, R., Gandrajula, R., Gao, S., Garcia-Gamez, D., Gardiner, S., Gastler, D., Gehrlein, J., Gelli, B., Gendotti, A., Ghorbani-Moghaddam, Z., Ghosh, A., Gibin, D., Gil-Botella, I., Girerd, C., Giri, A. K., Glavin, S., Goeldi, D., Gogota, O., Gold, M., Gollapinni, S., Gollwitzer, K., Gomes, R. A., Gomez, L., Bermeo, L. V. Gomez, Cadenas, J. J. Gomez, Gong, H., Gonnella, F., Gonzalez-Cuevas, J. A., Goodman, M., Goodwin, O., Gorbunov, D., Goswami, S., Goudzovski, E., Grace, C., Graf, N., Graham, M., Gramellini, E., Gran, R., Grant, A., Grant, C., Grant, N., Greco, V., Green, S., Greenlee, H., Greenler, L., Greenwood, M., Greer, J., Griffith, W. C., Groh, M., Grudzinski, J., Grzelak, K., Guanghua, G., Guardincerri, E., Guarino, V., Guedes, G. P., Guenette, R., Guglielmi, A., Guo, B., Gupta, S., Gupta, V., Guthikonda, K. K., Gutierrez, R., Guzowski, P., Guzzo, M. M., Habig, A., Hackenburg, R. W., Hackenburg, A., Hackett, B., Hadavand, H., Haenni, R., Hahn, A., Haigh, J., Haines, T., Haiston, J., Hamernik, T., Hamilton, P., Han, J., Handler, T., Hans, S., Harris, D. A., Hartnell, J., Hasegawa, T., Hatcher, R., Hatzikoutelis, A., Hays, S., Hazen, E., Headley, M., Heavey, A., Heegerv, K., Heise, J., Hennessy, K., Henry, S., Hernandez, A., Hernandez-Garcia, J., Herner, K., Hewes, V, Hignight, J., Higuera, A., Hill, T., Hillier, S., Himmel, A., Hohl, C., Holin, A., Hoppe, E., Horikawa, S., Horton-Smith, G., Hostert, M., Hourlier, A., Howard, B., Howell, R., Huang, J., Hugon, J., Hurh, P., Hylen, J., Illingworth, R., Insler, J., Introzzi, G., Ioannisian, A., Izmaylov, A., Jaffe, D. E., James, C., James, E., Jang, C. -H., Jediny, F., Jeong, Y. S., Jhingan, A., Ji, W., Jipa, A., Jiménez, S., Johnson, C., Johnson, M., Johnson, R., Johnstone, J., Jones, B., Jones, S., Joshi, J., Jostlein, H., Jung, C. K., Junk, T., Kaboth, A., Kadenko, I., Kamiya, F., Kamyshkov, Y., Karagiorgi, G., Karasavvas, D., Karyotakis, Y., Kasai, S., Kasetti, S., Kaur, K., Kayser, B., Kazaryan, N., Kearns, E., Keener, P., Kemp, E., Kendziora, C., Ketchum, W., Kettell, S. H., Khabibullin, M., Khotjantsev, A., Kim, D., Kirby, B., Kirby, M., Klein, J., Ko, Y. -J., Kobilarcik, T., Kocaman, B., Koerner, L. W., Kohn, S., Koizumi, G., Koller, P., Kopylov, A., Kordosky, M., Kormos, L., Kosc, T., Kose, U., Kostelecký, V. A., Kothekar, K., Kramer, M., Krennrich, F., Kreslo, I., Kriesel, K., Kropp, W., Kudenko, Y., Kudryavtsev, V. A., Kulagin, S., Kumar, J., Kumar, L., Kumar, A., Kumbhare, S., Kuruppu, C., Kus, V., Kutter, T., LaZur, R., Lande, K., Lane, C., Lang, K., Langford, T., Lanni, F., Lasorak, P., Last, D., Lastoria, C., Laundrie, A., Lazanu, I., Le, T., Learned, J., Lebrun, P., Lee, D., Miotto, G. Lehmann, de Oliveira, M. A. Leigui, Li, Q., Li, S., Li, S. W., Li, X., Li, Y., Li, Z., Liao, H. -Y., Lin, S. -K., Lin, C-J. S., Linehan, R., Linhart, V., Link, J., Liptak, Z., Lissauer, D., Littenberg, L., Littlejohn, B., Liu, J., Liu, T., LoMonaco, L., LoSecco, J. M., Lockwitz, S., Lockyer, N., Loew, T., Lokajicek, M., Long, K., Loo, K., Lopez, J. P., Lorca, D., Lord, T., Losada, M., Louis, W. C., Luethi, M., Luk, K. -B., Lundin, T., Luo, X., Lurkin, N., Lux, T., Luzio, V. P., Lykken, J., Maalampi, J., MacLellan, R., Machado, A. A., Machado, P., Macias, C. T., Macier, J., Madigan, P., Magill, S., Mahler, G., Mahn, K., Malek, M., Maloney, J. A., Mammoliti, F., Mandal, S. K., Mandrioli, G., Manenti, L., Manly, S., Mann, A., Marchionni, A., Marciano, W., Marcocci, S., Marfatia, D., Mariani, C., Maricic, J., Marinho, F., Marino, A. D., Marshak, M., Marshall, C., Marshall, J., Marteau, J., Martin-Albo, J., Martinez, D., Martinez, N., Martinez, H., Mason, K., Mastbaum, A., Masud, M., Mathez, H., Matsuno, S., Matthews, J., Mauger, C., Mauri, N., Mavrokoridis, K., Mazza, R., Mazzacane, A., Mazzucato, E., McCauley, N., McCluskey, E., McConkey, N., McDonald, K., McFarland, K. S., McGivern, C., McGowan, A., McGrew, C., McKeown, R., McNab, A., McNulty, D., McTaggart, R., Meddage, V., Mefodiev, A., Mehta, P., Mei, D., Mena, O., Menary, S., Mendez, H., Mendez, D. P., Menegolli, A., Meng, G., Messier, M., Metcalf, W., Mewes, M., Meyer, H., Miao, T., Migenda, J., Milincic, R., Miller, J., Miller, W., Mills, J., Milne, C., Mineev, O., Miranda, O., Mishra, C. S., Mishra, S. R., Mislivec, A., Mitrica, B., Mladenov, D., Mocioiu, I., Moffat, K., Moggi, N., Mohanta, R., Mokhov, N., Molina, J., Bueno, L. Molina, Montanari, A., Montanari, C., Montanari, D., Zetina, L. Montano, Moon, J., Mooney, M., Moore, C., Moreno, D., Morgan, B., Moroni, G. F., Morris, C., Morse, W., Mossey, C., Moura, C. A., Mousseau, J., Mualem, L., Muether, M., Mufson, S., Muheim, F., Muramatsu, H., Murphy, S., Musser, J., Nachtman, J., Nalbandyan, M., Nandakumar, R., Naples, D., Narita, S., Navarro, G., Navarro, J., Navas-Nicolás, D., Nayak, N., Nebot-Guinot, M., Needham, M., Negishi, K., Nelson, J., Nessi, M., Newbold, D., Newcomer, M., Nichol, R., Nicholls, T. C., Niner, E., Norman, A., Norris, B., Norris, J., Novella, P., Nowak, E., Nowak, J., Nunes, M. S., O'Keeffe, H., Oberling, M., Del Campo, A. Olivares, Olivier, A., Onel, Y., Onishchuk, Y., Ovsjannikova, T., Ozturk, S., Pagani, L., Pakvasa, S., Palamara, O., Paley, J., Pallavicini, M., Palomares, C., Palomino, J., Pantic, E., Paolo, A., Paolone, V., Papadimitriou, V., Papaleo, R., Paramesvaran, S., Park, J., Parke, S., Parsa, Z., Pascoli, S., Pasternak, J., Pater, J., Patrizii, L., Patterson, R. B., Patton, S. 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L., Ratoff, P., Ravat, S., Ravinez, O., Razafinime, H., Rebel, B., Redondo, D., Regenfus, C., Reggiani-Guzzo, M., Rehak, T., Reichenbacher, J., Reitzner, D., Reno, M. H., Renshaw, A., Rescia, S., Resnati, F., Reynolds, A., Riccobene, G., Rice, L. C. J., Rielage, K., Riesselmann, K., Rigaut, Y. -A., Rivera, D., Rochester, L., Roda, M., Rodrigues, P., Alonso, M. J. Rodriguez, Roe, B., Roeth, A. J., Roser, R. M., Ross-Lonergan, M., Rossella, M., Rout, J., Roy, S., Rubbia, A., Rubbia, C., Rucinski, R., Russell, B., Russell, J., Ruterbories, D., Vagins, M. R., Saakyan, R., Sahu, N., Sala, P., Salukvadze, G., Samios, N., Sanchez, F., Sanchez, M. C., Sandoval, C., Sands, B., Sankar, S. U., Santana, S., Santos, L. M., Santucci, G., Saoulidou, N., Sapienza, P., Sarasty, C., Sarcevic, I., Savage, G., Scaramelli, A., Scarpelli, A., Schaffer, T., Schellman, H., Schlabach, P., Schloesser, C. M., Schmitz, D. 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P., Zennamo, J., Zhang, C., Zhao, M., Zhou, Y. -L., Zhu, G., Zimmerman, E. D., Zito, M., Zucchelli, S., Zuklin, J., Zutshi, V., and Zwaska, R.
Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract: The DUNE IDR describes the proposed physics program and technical designs of the DUNE far detector modules in preparation for the full TDR to be published in 2019. It is intended as an intermediate milestone on the path to a full TDR, justifying the technical choices that flow down from the high-level physics goals through requirements at all levels of the Project. These design choices will enable the DUNE experiment to make the ground-breaking discoveries that will help to answer fundamental physics questions. Volume 3 describes the dual-phase module's subsystems, the technical coordination required for its design, construction, installation, and integration, and its organizational structure., Comment: 280 pages, 109 figures. arXiv admin note: text overlap with arXiv:1807.10327
Published: 2018

36. The DUNE Far Detector Interim Design Report Volume 1: Physics, Technology and Strategies

Author: DUNE Collaboration, Abi, B., Acciarri, R., Acero, M. A., Adamowski, M., Adams, C., Adams, D., Adamson, P., Adinolfi, M., Ahmad, Z., Albright, C. H., Soplin, L. Aliaga, Alion, T., Monsalve, S. Alonso, Alrashed, M., Alt, C., Anderson, J., Anderson, K., Andreopoulos, C., Andrews, M. P., Andrews, R. A., Ankowski, A., Anthony, J., Antonello, M., Antonova, M., Antusch, S., Fernandez, A. Aranda, Ariga, A., Ariga, T., Sierra, D. Aristizabal, Diaz, E. Arrieta, Asaadi, J., Ascencio, M., Asner, D., Athar, M. S., Auger, M., Aurisano, A., Aushev, V., Autiero, D., Azfar, F., Back, A., Back, H., Back, J., Backhouse, C., Baesso, P., Bagby, L., Bai, X., Baird, M., Balantekin, B., Balasubramanian, S., Baller, B., Ballett, P., Balleyguier, L., Bambah, B., Band, H., Bansal, M., Bansal, S., Barenboim, G., Barker, G. J., Barnes, C., Barr, G., Monarca, J. Barranco, Barros, N., Barrow, J., Bashyal, A., Basque, V., Bass, M., Bay, F., Bays, K., Bazo, J. L., Beacom, J. F., Bechetoille, E., Behera, B. R., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Belver, D., Benekos, N., Benetti, P. A., Bercellie, A., Berman, E., Bernardini, P., Berner, R., Berns, H. G., Bernstein, R. H., Bertolucci, S., Betancourt, M., Bhatnagar, V., Bhattacharjee, M., Bhuyan, B., Biagi, S., Bian, J., Biery, K., Bilki, B., Bishai, M., Bitadze, A., Blackburn, T., Blake, A., Siffert, B. Blanco, Blaszczyk, F., Blaufuss, E., Blazey, G. C., Blennow, M., Blucher, E., Bocean, V., Boffelli, F., Boissevain, J., Bolognesi, S., Bolton, T., Bonesini, M., Boone, T., Booth, A., Booth, C., Bordoni, S., Borkum, A., Boschi, T., Bour, P., Bourguille, B., Boyd, S. B., Boyden, D., Bracinik, J., Brailsford, D., Brandt, A., Bremer, J., Brice, S. J., Bromberg, C., Brooijmans, G., Brooke, J., Brown, G., Buchanan, N., Budd, H., de Holanda, P. C., Cai, T., Caiulo, D., Calafiura, P., Calatayud, A., Calcutt, J., Callahan, C., Calligarich, E., Calvo, E., Camilleri, L., Caminata, A., Campanelli, M., Cancelo, G., Cankocak, K., Cantini, C., Caratelli, D., Carlus, B., Carneiro, M., Terrazas, I. Caro, Carroll, T. J., Carvallo, M. P., Cascella, M., Castromonte, C., Catano-Mur, E., Cavalli-Sforza, M., Cavanna, F., Cazzato, E., Centro, S., Cerati, G., Cervelli, A., Villanueva, A. Cervera, Cervi, T., Chalifour, M., Chappuis, A., Chatterjee, A., Chattopadhyay, S., Chaves, J., Chen, H., Chen, M. -C., Chen, S., Cherdack, D., Chi, C. -Y., Childress, S., Cho, K., Choubey, S., Choudhary, B. C., Christensen, A., Christian, D., Christodoulou, G., Christofferson, C. -A., Church, E., Clarke, P., Coan, T. E., Cocco, A., Collin, G. H., Conley, E., Conrad, J. M., Convery, M., Corey, R., Corwin, L., Cotte, P., Cremonesi, L., Crespo-Anadón, J. I., Prats, J. 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M., Gallagher, H., Gallego-Ros, A., Galymov, V., Gamberini, E., Gambetta, S., Gamble, T., Gandhi, R., Gandrajula, R., Gao, S., Garcia-Gamez, D., Gardiner, S., Gastler, D., Gehrlein, J., Gelli, B., Gendotti, A., Ghorbani-Moghaddam, Z., Ghosh, A., Gibin, D., Gil-Botella, I., Girerd, C., Giri, A. K., Glavin, S., Goeldi, D., Gogota, O., Gold, M., Gollapinni, S., Gollwitzer, K., Gomes, R. A., Gomez, L., Bermeo, L. V. Gomez, Cadenas, J. J. Gomez, Gong, H., Gonnella, F., Gonzalez-Cuevas, J. A., Goodman, M., Goodwin, O., Gorbunov, D., Goswami, S., Goudzovski, E., Grace, C., Graf, N., Graham, M., Gramellini, E., Gran, R., Grant, A., Grant, C., Grant, N., Greco, V., Green, S., Greenlee, H., Greenler, L., Greenwood, M., Greer, J., Griffith, W. C., Groh, M., Grudzinski, J., Grzelak, K., Guanghua, G., Guardincerri, E., Guarino, V., Guedes, G. P., Guenette, R., Guglielmi, A., Guo, B., Gupta, S., Gupta, V., Guthikonda, K. K., Gutierrez, R., Guzowski, P., Guzzo, M. M., Habig, A., Hackenburg, R. 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W., Li, X., Li, Y., Li, Z., Liao, H. -Y., Lin, S. -K., Lin, C-J. S., Linehan, R., Linhart, V., Link, J., Liptak, Z., Lissauer, D., Littenberg, L., Littlejohn, B., Liu, J., Liu, T., LoMonaco, L., LoSecco, J. M., Lockwitz, S., Lockyer, N., Loew, T., Lokajicek, M., Long, K., Loo, K., Lopez, J. P., Lorca, D., Lord, T., Losada, M., Louis, W. C., Luethi, M., Luk, K. -B., Lundin, T., Luo, X., Lurkin, N., Lux, T., Luzio, V. P., Lykken, J., Maalampi, J., MacLellan, R., Machado, A. A., Machado, P., Macias, C. T., Macier, J., Madigan, P., Magill, S., Mahler, G., Mahn, K., Malek, M., Maloney, J. A., Mammoliti, F., Mandal, S. K., Mandrioli, G., Manenti, L., Manly, S., Mann, A., Marchionni, A., Marciano, W., Marcocci, S., Marfatia, D., Mariani, C., Maricic, J., Marinho, F., Marino, A. D., Marshak, M., Marshall, C., Marshall, J., Marteau, J., Martin-Albo, J., Martinez, D., Martinez, N., Martinez, H., Mason, K., Mastbaum, A., Masud, M., Mathez, H., Matsuno, S., Matthews, J., Mauger, C., Mauri, N., Mavrokoridis, K., Mazza, R., Mazzacane, A., Mazzucato, E., McCauley, N., McCluskey, E., McConkey, N., McDonald, K., McFarland, K. S., McGivern, C., McGowan, A., McGrew, C., McKeown, R., McNab, A., McNulty, D., McTaggart, R., Meddage, V., Mefodiev, A., Mehta, P., Mei, D., Mena, O., Menary, S., Mendez, H., Mendez, D. P., Menegolli, A., Meng, G., Messier, M., Metcalf, W., Mewes, M., Meyer, H., Miao, T., Migenda, J., Milincic, R., Miller, J., Miller, W., Mills, J., Milne, C., Mineev, O., Miranda, O., Mishra, C. S., Mishra, S. R., Mislivec, A., Mitrica, B., Mladenov, D., Mocioiu, I., Moffat, K., Moggi, N., Mohanta, R., Mokhov, N., Molina, J., Bueno, L. Molina, Montanari, A., Montanari, C., Montanari, D., Zetina, L. Montano, Moon, J., Mooney, M., Moore, C., Moreno, D., Morgan, B., Moroni, G. F., Morris, C., Morse, W., Mossey, C., Moura, C. A., Mousseau, J., Mualem, L., Muether, M., Mufson, S., Muheim, F., Muramatsu, H., Murphy, S., Musser, J., Nachtman, J., Nalbandyan, M., Nandakumar, R., Naples, D., Narita, S., Navarro, G., Navarro, J., Navas-Nicolás, D., Nayak, N., Nebot-Guinot, M., Needham, M., Negishi, K., Nelson, J., Nessi, M., Newbold, D., Newcomer, M., Nichol, R., Nicholls, T. C., Niner, E., Norman, A., Norris, B., Norris, J., Novella, P., Nowak, E., Nowak, J., Nunes, M. S., O'Keeffe, H., Oberling, M., Del Campo, A. Olivares, Olivier, A., Onel, Y., Onishchuk, Y., Ovsjannikova, T., Ozturk, S., Pagani, L., Pakvasa, S., Palamara, O., Paley, J., Pallavicini, M., Palomares, C., Palomino, J., Pantic, E., Paolo, A., Paolone, V., Papadimitriou, V., Papaleo, R., Paramesvaran, S., Park, J., Parke, S., Parsa, Z., Pascoli, S., Pasternak, J., Pater, J., Patrizii, L., Patterson, R. B., Patton, S. J., Patzak, T., Paudel, A., Paulos, B., Paulucci, L., Pavlovic, Z., Pawloski, G., Payam, P., Payne, D., Pec, V., Peeters, S. J. M., Pennacchio, E., Penzo, A., Perdue, G. N., Peres, O. l. G., Periale, L., Petridis, K., Petrillo, G., Petti, R., Picchi, P., Pickering, L., Pietropaolo, F., Pillow, J., Plonski, P., Plunkett, R., Poling, R., Pons, X., Poonthottathil, N., Popovic, M., Pordes, R., Pordes, S., Potekhin, M., Potenza, R., Potukuchi, B., Poudel, S., Pozimski, J., Pozzato, M., Prakasj, T., Preece, R., Prokofiev, O., Pruthi, N., Przewlocki, P., Psihas, F., PugnËre, D., Pushka, D., Qi, K., Qian, X., Raaf, J. L., Raboanary, R., Radeka, V., Rademacker, J., Radescu, V., Radics, B., Radovic, A., Rafique, A., Rajaoalisoa, M., Rakhno, I., Rakotondramanana, H. T., Rakotondravohitra, L., Ramachers, Y. A., Rameika, R. A., Delgado, M. A. Ramirez, Ramsey, J., Ramson, B. J., Rappoldi, A., Raselli, G. L., Ratoff, P., Ravat, S., Ravinez, O., Razafinime, H., Rebel, B., Redondo, D., Regenfus, C., Reggiani-Guzzo, M., Rehak, T., Reichenbacher, J., Reitzner, D., Reno, M. H., Renshaw, A., Rescia, S., Resnati, F., Reynolds, A., Riccobene, G., Rice, L. C. J., Rielage, K., Riesselmann, K., Rigaut, Y. -A., Rivera, D., Rochester, L., Roda, M., Rodrigues, P., Alonso, M. J. Rodriguez, Roe, B., Roeth, A. J., Roser, R. M., Ross-Lonergan, M., Rossella, M., Rout, J., Roy, S., Rubbia, A., Rubbia, C., Rucinski, R., Russell, B., Russell, J., Ruterbories, D., Vagins, M. R., Saakyan, R., Sahu, N., Sala, P., Salukvadze, G., Samios, N., Sanchez, F., Sanchez, M. C., Sandoval, C., Sands, B., Sankar, S. U., Santana, S., Santos, L. M., Santucci, G., Saoulidou, N., Sapienza, P., Sarasty, C., Sarcevic, I., Savage, G., Scaramelli, A., Scarpelli, A., Schaffer, T., Schellman, H., Schlabach, P., Schloesser, C. M., Schmitz, D. W., Schneps, J., Scholberg, K., Schukraft, A., Segreto, E., Sehrawat, S., Sensenig, J., Seong, I., Sepulveda-Quiroz, J. A., Sergi, A., Sergiampietri, F., Sessumes, D., Sexton, K., Sexton-Kennedy, L., Sgalaberna, D., Shaevitz, M. H., Shafaq, S., Shahi, J. S., Shahsavarani, S., Shanahan, P., Sharma, H. R., Sharma, R., Sharma, R. K., Shaw, T., Shin, S., Shoemaker, I., Shooltz, D., Shrock, R., Simos, N., Sinclair, J., Sinev, G., Singh, V., Singh, J., Singh, I., Sipos, R., Sippach, F. W., Sirri, G., Siyeon, K., Smargianaki, D., Smith, A., Smith, E., Smith, P., Smolik, J., Smy, M., Snider, E. L., Snopok, P., Sobczyk, J., Sobel, H., Soderberg, M., Salinas, C. J. Solano, Söldner-Rembold, S., Solomey, N., Sondheim, W., Sorel, M., Soto-Oton, J. A., Sousa, A., Soustruznik, K., Spagliardi, F., Spanu, M., Spitz, J., Spooner, N. J. C., Staley, R., Stancari, M., Stanco, L., Stefanik, A., Steiner, H. 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P., Zennamo, J., Zhang, C., Zhao, M., Zhou, Y. -L., Zhu, G., Zimmerman, E. D., Zito, M., Zucchelli, S., Zuklin, J., Zutshi, V., and Zwaska, R.
Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract: The DUNE IDR describes the proposed physics program and technical designs of the DUNE Far Detector modules in preparation for the full TDR to be published in 2019. It is intended as an intermediate milestone on the path to a full TDR, justifying the technical choices that flow down from the high-level physics goals through requirements at all levels of the Project. These design choices will enable the DUNE experiment to make the ground-breaking discoveries that will help to answer fundamental physics questions. Volume 1 contains an executive summary that describes the general aims of this document. The remainder of this first volume provides a more detailed description of the DUNE physics program that drives the choice of detector technologies. It also includes concise outlines of two overarching systems that have not yet evolved to consortium structures: computing and calibration. Volumes 2 and 3 of this IDR describe, for the single-phase and dual-phase technologies, respectively, each detector module's subsystems, the technical coordination required for its design, construction, installation, and integration, and its organizational structure., Comment: 83 pages, 11 figures
Published: 2018

37. The DUNE Far Detector Interim Design Report, Volume 2: Single-Phase Module

Author: DUNE Collaboration, Abi, B., Acciarri, R., Acero, M. A., Adamowski, M., Adams, C., Adams, D., Adamson, P., Adinolfi, M., Ahmad, Z., Albright, C. H., Soplin, L. Aliaga, Alion, T., Monsalve, S. Alonso, Alrashed, M., Alt, C., Anderson, J., Anderson, K., Andreopoulos, C., Andrews, M. P., Andrews, R. A., Ankowski, A., Anthony, J., Antonello, M., Antonova, M., Antusch, S., Fernandez, A. Aranda, Ariga, A., Ariga, T., Sierra, D. Aristizabal, Diaz, E. Arrieta, Asaadi, J., Ascencio, M., Asner, D., Athar, M. S., Auger, M., Aurisano, A., Aushev, V., Autiero, D., Azfar, F., Back, A., Back, H., Back, J., Backhouse, C., Baesso, P., Bagby, L., Bai, X., Baird, M., Balantekin, B., Balasubramanian, S., Baller, B., Ballett, P., Balleyguier, L., Bambah, B., Band, H., Bansal, M., Bansal, S., Barenboim, G., Barker, G. J., Barnes, C., Barr, G., Monarca, J. Barranco, Barros, N., Barrow, J., Bashyal, A., Basque, V., Bass, M., Bay, F., Bays, K., Bazo, J. L., Beacom, J. F., Bechetoille, E., Behera, B. R., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Belver, D., Benekos, N., Benetti, P. A., Bercellie, A., Berman, E., Bernardini, P., Berner, R., Berns, H. G., Bernstein, R. H., Bertolucci, S., Betancourt, M., Bhatnagar, V., Bhattacharjee, M., Bhuyan, B., Biagi, S., Bian, J., Biery, K., Bilki, B., Bishai, M., Bitadze, A., Blackburn, T., Blake, A., Siffert, B. Blanco, Blaszczyk, F., Blaufuss, E., Blazey, G. C., Blennow, M., Blucher, E., Bocean, V., Boffelli, F., Boissevain, J., Bolognesi, S., Bolton, T., Bonesini, M., Boone, T., Booth, A., Booth, C., Bordoni, S., Borkum, A., Boschi, T., Bour, P., Bourguille, B., Boyd, S. B., Boyden, D., Bracinik, J., Brailsford, D., Brandt, A., Bremer, J., Brice, S. J., Bromberg, C., Brooijmans, G., Brooke, J., Brown, G., Buchanan, N., Budd, H., de Holanda, P. C., Cai, T., Caiulo, D., Calafiura, P., Calatayud, A., Calcutt, J., Callahan, C., Calligarich, E., Calvo, E., Camilleri, L., Caminata, A., Campanelli, M., Cancelo, G., Cankocak, K., Cantini, C., Caratelli, D., Carlus, B., Carneiro, M., Terrazas, I. Caro, Carroll, T. J., Carvallo, M. P., Cascella, M., Castromonte, C., Catano-Mur, E., Cavalli-Sforza, M., Cavanna, F., Cazzato, E., Centro, S., Cerati, G., Cervelli, A., Villanueva, A. Cervera, Cervi, T., Chalifour, M., Chappuis, A., Chatterjee, A., Chattopadhyay, S., Chaves, J., Chen, H., Chen, M. -C., Chen, S., Cherdack, D., Chi, C. -Y., Childress, S., Cho, K., Choubey, S., Choudhary, B. C., Christensen, A., Christian, D., Christodoulou, G., Christofferson, C. -A., Church, E., Clarke, P., Coan, T. E., Cocco, A., Collin, G. H., Conley, E., Conrad, J. M., Convery, M., Corey, R., Corwin, L., Cotte, P., Cremonesi, L., Crespo-Anadón, J. I., Prats, J. 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M., Gallagher, H., Gallego-Ros, A., Galymov, V., Gamberini, E., Gambetta, S., Gamble, T., Gandhi, R., Gandrajula, R., Gao, S., Garcia-Gamez, D., Gardiner, S., Gastler, D., Gehrlein, J., Gelli, B., Gendotti, A., Ghorbani-Moghaddam, Z., Ghosh, A., Gibin, D., Gil-Botella, I., Girerd, C., Giri, A. K., Glavin, S., Goeldi, D., Gogota, O., Gold, M., Gollapinni, S., Gollwitzer, K., Gomes, R. A., Gomez, L., Bermeo, L. V. Gomez, Cadenas, J. J. Gomez, Gong, H., Gonnella, F., Gonzalez-Cuevas, J. A., Goodman, M., Goodwin, O., Gorbunov, D., Goswami, S., Goudzovski, E., Grace, C., Graf, N., Graham, M., Gramellini, E., Gran, R., Grant, A., Grant, C., Grant, N., Greco, V., Green, S., Greenlee, H., Greenler, L., Greenwood, M., Greer, J., Griffith, W. C., Groh, M., Grudzinski, J., Grzelak, K., Guanghua, G., Guardincerri, E., Guarino, V., Guedes, G. P., Guenette, R., Guglielmi, A., Guo, B., Gupta, S., Gupta, V., Guthikonda, K. K., Gutierrez, R., Guzowski, P., Guzzo, M. M., Habig, A., Hackenburg, R. W., Hackenburg, A., Hackett, B., Hadavand, H., Haenni, R., Hahn, A., Haigh, J., Haines, T., Haiston, J., Hamernik, T., Hamilton, P., Han, J., Handler, T., Hans, S., Harris, D. A., Hartnell, J., Hasegawa, T., Hatcher, R., Hatzikoutelis, A., Hays, S., Hazen, E., Headley, M., Heavey, A., Heegerv, K., Heise, J., Hennessy, K., Henry, S., Hernandez, A., Hernandez-Garcia, J., Herner, K., Hewes, V, Hignight, J., Higuera, A., Hill, T., Hillier, S., Himmel, A., Hohl, C., Holin, A., Hoppe, E., Horikawa, S., Horton-Smith, G., Hostert, M., Hourlier, A., Howard, B., Howell, R., Huang, J., Hugon, J., Hurh, P., Hylen, J., Illingworth, R., Insler, J., Introzzi, G., Ioannisian, A., Izmaylov, A., Jaffe, D. E., James, C., James, E., Jang, C. -H., Jediny, F., Jeong, Y. S., Jhingan, A., Ji, W., Jipa, A., Jiménez, S., Johnson, C., Johnson, M., Johnson, R., Johnstone, J., Jones, B., Jones, S., Joshi, J., Jostlein, H., Jung, C. 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W., Li, X., Li, Y., Li, Z., Liao, H. -Y., Lin, S. -K., Lin, C-J. S., Linehan, R., Linhart, V., Link, J., Liptak, Z., Lissauer, D., Littenberg, L., Littlejohn, B., Liu, J., Liu, T., LoMonaco, L., LoSecco, J. M., Lockwitz, S., Lockyer, N., Loew, T., Lokajicek, M., Long, K., Loo, K., Lopez, J. P., Lorca, D., Lord, T., Losada, M., Louis, W. C., Luethi, M., Luk, K. -B., Lundin, T., Luo, X., Lurkin, N., Lux, T., Luzio, V. P., Lykken, J., Maalampi, J., MacLellan, R., Machado, A. A., Machado, P., Macias, C. T., Macier, J., Madigan, P., Magill, S., Mahler, G., Mahn, K., Malek, M., Maloney, J. A., Mammoliti, F., Mandal, S. K., Mandrioli, G., Manenti, L., Manly, S., Mann, A., Marchionni, A., Marciano, W., Marcocci, S., Marfatia, D., Mariani, C., Maricic, J., Marinho, F., Marino, A. 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J., Patzak, T., Paudel, A., Paulos, B., Paulucci, L., Pavlovic, Z., Pawloski, G., Payam, P., Payne, D., Pec, V., Peeters, S. J. M., Pennacchio, E., Penzo, A., Perdue, G. N., Peres, O. l. G., Periale, L., Petridis, K., Petrillo, G., Petti, R., Picchi, P., Pickering, L., Pietropaolo, F., Pillow, J., Plonski, P., Plunkett, R., Poling, R., Pons, X., Poonthottathil, N., Popovic, M., Pordes, R., Pordes, S., Potekhin, M., Potenza, R., Potukuchi, B., Poudel, S., Pozimski, J., Pozzato, M., Prakasj, T., Preece, R., Prokofiev, O., Pruthi, N., Przewlocki, P., Psihas, F., PugnËre, D., Pushka, D., Qi, K., Qian, X., Raaf, J. L., Raboanary, R., Radeka, V., Rademacker, J., Radescu, V., Radics, B., Radovic, A., Rafique, A., Rajaoalisoa, M., Rakhno, I., Rakotondramanana, H. T., Rakotondravohitra, L., Ramachers, Y. A., Rameika, R. A., Delgado, M. A. Ramirez, Ramsey, J., Ramson, B. J., Rappoldi, A., Raselli, G. L., Ratoff, P., Ravat, S., Ravinez, O., Razafinime, H., Rebel, B., Redondo, D., Regenfus, C., Reggiani-Guzzo, M., Rehak, T., Reichenbacher, J., Reitzner, D., Reno, M. H., Renshaw, A., Rescia, S., Resnati, F., Reynolds, A., Riccobene, G., Rice, L. C. J., Rielage, K., Riesselmann, K., Rigaut, Y. -A., Rivera, D., Rochester, L., Roda, M., Rodrigues, P., Alonso, M. J. Rodriguez, Roe, B., Roeth, A. J., Roser, R. M., Ross-Lonergan, M., Rossella, M., Rout, J., Roy, S., Rubbia, A., Rubbia, C., Rucinski, R., Russell, B., Russell, J., Ruterbories, D., Vagins, M. R., Saakyan, R., Sahu, N., Sala, P., Salukvadze, G., Samios, N., Sanchez, F., Sanchez, M. C., Sandoval, C., Sands, B., Sankar, S. U., Santana, S., Santos, L. M., Santucci, G., Saoulidou, N., Sapienza, P., Sarasty, C., Sarcevic, I., Savage, G., Scaramelli, A., Scarpelli, A., Schaffer, T., Schellman, H., Schlabach, P., Schloesser, C. M., Schmitz, D. W., Schneps, J., Scholberg, K., Schukraft, A., Segreto, E., Sehrawat, S., Sensenig, J., Seong, I., Sepulveda-Quiroz, J. A., Sergi, A., Sergiampietri, F., Sessumes, D., Sexton, K., Sexton-Kennedy, L., Sgalaberna, D., Shaevitz, M. H., Shafaq, S., Shahi, J. S., Shahsavarani, S., Shanahan, P., Sharma, H. R., Sharma, R., Sharma, R. K., Shaw, T., Shin, S., Shoemaker, I., Shooltz, D., Shrock, R., Simos, N., Sinclair, J., Sinev, G., Singh, V., Singh, J., Singh, I., Sipos, R., Sippach, F. W., Sirri, G., Siyeon, K., Smargianaki, D., Smith, A., Smith, E., Smith, P., Smolik, J., Smy, M., Snider, E. L., Snopok, P., Sobczyk, J., Sobel, H., Soderberg, M., Salinas, C. J. Solano, Söldner-Rembold, S., Solomey, N., Sondheim, W., Sorel, M., Soto-Oton, J. A., Sousa, A., Soustruznik, K., Spagliardi, F., Spanu, M., Spitz, J., Spooner, N. J. C., Staley, R., Stancari, M., Stanco, L., Stefanik, A., Steiner, H. M., Stewart, J., Stock, J., Stocker, F., Stoica, S., Stone, J., Strait, J., Strait, M., Strauss, T., Striganov, S., Stuart, A., Sullivan, G., Sultana, M., Sun, Y., Surdo, A., Susic, V., Suter, L., Sutera, C. M., Svoboda, R., Szczerbinska, B., Szelc, A. M., Tagg, N., Talaga, R., Tanaka, H., Oregui, B. Tapia, Tariq, S., Tatar, E., Tayloe, R., Tenti, M., Terao, K., Ternes, C. A., Terranova, F., Testera, G., Thea, A., Thompson, L. F., Thompson, J., Thorn, C., Timilsina, A., Timm, S. C., Todd, J., Tonazzo, A., Tope, T., Torbunov, D., Torti, M., Tórtola, M., Tortorici, F., Toups, M., Touramanis, C., Trevor, J., Tripathi, M., Tromeur, W., Tropin, I., Trzaska, W. H., Tsai, Y. -T., Tsang, K. V., Tsaris, A., Tufanli, S., Tull, C., Turner, J., Tzanov, M., Tziaferi, E., Uchida, Y., Urheim, J., Usher, T., Valdiviesso, G. A., Valencia, E., Valerio, L., Vallari, Z., Valle, J. W. F., Van Berg, R., Van de Water, R., Varanini, F., Varner, G., Vasel, J., Vasseur, G., Vaziri, K., Velev, G., Ventura, S., Verdugo, A., Vermeulen, M., Vernon, E., Verzocchi, M., Viant, T., Vignoli, C., Vihonen, S., Vilela, C., Viren, B., Vokac, P., Vrba, T., Wachala, T., Wahl, D., Wallbank, M., Wang, H., Wang, J., Wang, T. -C., Wang, B., Wang, Y., Wang, Z., Warburton, K., Warner, D., Wascko, M. O., Waters, D., Watson, A., Weber, A., Weber, M., Wei, H., Wei, W., Weinstein, A., Wenman, D., Wetstein, M., While, M., White, A., Whitehead, L. H., Whittington, D., Wierman, K., Wilking, M., Wilkinson, C., Willhite, J., Williams, Z., Wilson, R. J., Wilson, P., Wittich, P., Wolcott, J., Wongjirad, T., Wood, K., Wood, L., Worcester, E., Worcester, M., Wu, S., Wu, W., Xu, W., Yanagisawa, C., Yang, S., Yang, T., Yang, G., Ye, J., Yeh, M., Yershov, N., Yonehara, K., Yoshimura, L., Yu, B., Yu, J., Zalesak, J., Zambelli, L., Zamorano, B., Zani, A., Zaremba, K., Zazueta, L., Zeller, G. P., Zennamo, J., Zhang, C., Zhao, M., Zhou, Y. -L., Zhu, G., Zimmerman, E. D., Zito, M., Zucchelli, S., Zuklin, J., Zutshi, V., and Zwaska, R.
Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract: The DUNE IDR describes the proposed physics program and technical designs of the DUNE far detector modules in preparation for the full TDR to be published in 2019. It is intended as an intermediate milestone on the path to a full TDR, justifying the technical choices that flow down from the high-level physics goals through requirements at all levels of the Project. These design choices will enable the DUNE experiment to make the ground-breaking discoveries that will help to answer fundamental physics questions. Volume 2 describes the single-phase module's subsystems, the technical coordination required for its design, construction, installation, and integration, and its organizational structure., Comment: 324 pages, 130 figures. arXiv admin note: text overlap with arXiv:1807.10340
Published: 2018

38. New constraints on oscillation parameters from $\nu_e$ appearance and $\nu_\mu$ disappearance in the NOvA experiment

Author: Acero, M. A., Adamson, P., Alion, L. Aliaga T., Allakhverdian, V., Antoshkin, N. Anfimov A., Arrieta-Diaz, E., Back, A. Aurisano A., Backhouse, C., Balashov, M. Baird N., Bambah, B. A., Behera, K. Bays B., Bending, S., Bhatnagar, R. Bernstein V., Bhuyan, B., Blackburn, J. Bian T., Blair, J., Bour, A. Bolshakova P., Bromberg, C., Buchanan, J. Brown N., Butkevich, A., Campbell, V. Bychkov M., Carroll, T. J., Cedeno, E. Catano-Mur A., Childress, S., Chowdhury, B. C. Choudhary B., Coan, T. E., Cooper, M. Colo J., Corwin, L., Cronin-Hennessy, L. Cremonesi D., Davies, G. S., De Rijck, J. P. Davies S., Derwent, P. F., Ding, R. Dharmapalan P., Djurcic, Z., Dung, E. C. Dukes P., Duyang, H., Ehrlich, S. Edayath R., Feldman, G. J., Gallagher, M. J. Frank H. R., Gandrajula, R., Germani, F. Gao S., Giri, A., Goodman, R. A. Gomes M. C., Grichine, V., Group, M. Groh R., Grover, D., Habig, B. Guo A., Hakl, F., Hatcher, J. Hartnell R., Hatzikoutelis, A., Himmel, K. Heller A., Holin, A., Huang, B. Howard J., Hylen, J., Judah, F. Jediny M., Kakorin, I., Kaplan, D. Kalra D. M., Keloth, R., Koerner, O. Klimov L. W., Kolupaeva, L., Kourbanis, S. Kotelnikov I., Kreymer, A., Kumar, Ch. Kulenberg A., Kuruppu, C., Lackey, V. Kus T., Lang, K., Lokajicek, S. Lin M., Lozier, J., Maan, S. Luchuk K., Magill, S., Marshak, W. A. Mann M. L., Matveev, V., Messier, D. P. Méndez M. D., Meyer, H., Miller, T. Miao W. H., Mishra, S. R., Mohanta, A. Mislivec R., Moren, A., Muether, L. Mualem M., Mufson, S., Musser, R. Murphy J., Naples, D., Nelson, N. Nayak J. K., Nichol, R., Norman, E. Niner A., Nosek, T., Olshevskiy, Y. Oksuzian A., Olson, T., Patterson, J. Paley R. B., Pawloski, G., Petrova, D. Pershey O., Petti, R., Plunkett, S. Phan-Budd R. K., Potukuchi, B., Psihas, C. Principato F., Radovic, A., Rebel, R. A. Rameika B., Rojas, P., Sachdev, V. Ryabov K., Samoylov, O., Sepulveda-Quiroz, M. C. Sanchez J., Shanahan, P., Singh, A. Sheshukov P., Singh, V., Smolik, E. Smith J., Snopok, P., Song, N. Solomey E., Sousa, A., Strait, K. Soustruznik M., Suter, L., Tas, R. L. Talaga P., Thayyullathil, R. B., Tiras, J. Thomas E., Tognini, S. C., Tripathi, D. Torbunov J., Tsaris, A., Urheim, Y. Torun J., Vahle, P., Vinton, J. Vasel L., Vokac, P., Vrba, A. Vold T., Wang, B., Wetstein, T. K. Warburton M., Whittington, D., Wolcott, S. G. Wojcicki J., Yang, S., Zalesak, S. Yu J., Zamorano, B., and Zwaska, R.
Subjects: High Energy Physics - Experiment
Abstract: We present updated results from the NOvA experiment for $\nu_\mu\rightarrow\nu_\mu$ and $\nu_\mu\rightarrow\nu_e$ oscillations from an exposure of $8.85\times10^{20}$ protons on target, which represents an increase of 46% compared to our previous publication. The results utilize significant improvements in both the simulations and analysis of the data. A joint fit to the data for $\nu_\mu$ disappearance and $\nu_e$ appearance gives the best fit point as normal mass hierarchy, $\Delta m^2_{32} = 2.44\times 10^{-3}{{\rm eV}^2}/c^4$, $\sin^2\theta_{23} = 0.56$, and $\delta_{CP} = 1.21\pi$. The 68.3% confidence intervals in the normal mass hierarchy are $\Delta m^2_{32} \in [2.37,2.52]\times 10^{-3}{{\rm eV}^2}/c^4$, $\sin^2\theta_{23} \in [0.43,0.51] \cup [0.52,0.60]$, and $\delta_{CP} \in [0,0.12\pi] \cup [0.91\pi,2\pi]$. The inverted mass hierarchy is disfavored at the 95% confidence level for all choices of the other oscillation parameters.
Published: 2018
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39. Language models for the prediction of SARS-CoV-2 inhibitors.

Author: Andrew E. Blanchard, John Gounley, Debsindhu Bhowmik, Mayanka Chandra Shekar, Isaac Lyngaas, Shang Gao 0008, Junqi Yin, Aristeidis Tsaris, Feiyi Wang, and Jens Glaser
Published: 2022
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40. Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action.

Author: Anda Trifan, Defne Gorgun, Michael Salim, Zongyi Li, Alexander Brace, Maxim Zvyagin, Heng Ma, Austin Clyde, David Clark, David J. Hardy, Tom Burnley, Lei Huang 0019, John D. McCalpin, Murali Emani, Hyenseung Yoo, Junqi Yin, Aristeidis Tsaris, Vishal Subbiah, Tanveer Raza, Jessica Liu, Noah Trebesch, Geoffrey Wells, Venkatesh Mysore, Tom Gibbs, James C. Phillips, S. Chakra Chennubhotla, Ian T. Foster, Rick Stevens, Anima Anandkumar, Venkatram Vishwanath, John E. Stone, Emad Tajkhorshid, Sarah A. Harris, and Arvind Ramanathan
Published: 2022
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41. Model Assumptions and Data Characteristics: Impacts on Domain Adaptation in Building Segmentation.

Author: Philipe A. Dias, Yuxin Tian, Shawn D. Newsam, Aristeidis Tsaris, Jacob D. Hinkle, and Dalton D. Lunga
Published: 2022
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42. Ultra-Long Sequence Distributed Transformer.

Author: Xiao Wang 0004, Isaac Lyngaas, Aristeidis Tsaris, Peng Chen 0035, Sajal Dash, Mayanka Chandra Shekar, Tao Luo 0014, Hong-Jun Yoon, Mohamed Wahib, and John Gounley
Published: 2023
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43. Progress of the Charged Pion Semi-Inclusive Neutrino Charged Current Cross Section in NOvA

Author: Tsaris, Aristeidis
Subjects: High Energy Physics - Experiment, Physics - Instrumentation and Detectors
Abstract: The NOvA experiment is a long-baseline neutrino oscillation experiment designed to measure the rates of electron neutrino appearance and muon neutrino disappearance. The NOvA near detector is located at Fermilab, 800 m from the primary target and provides an excellent platform to measure and study neutrino-nucleus interactions. We present the status of the measurement of the double differential cross section with respect to muon kinematics for interactions involving charged pions in the final state, $\nu_{\mu} + N \rightarrow \mu + \pi^{+/-} + X$. We have derived a convolutional neural network-based approach for the identification of neutrino interactions with the specific final state topology. We present event classification efficiency studies using this particle identification and classification methodology, along with systematic uncertainties and prospects for the measurement., Comment: Talk presented at the APS Division of Particles and Fields Meeting (DPF 2017), July 31-August 4, 2017, Fermilab. C170731
Published: 2017

44. The Single-Phase ProtoDUNE Technical Design Report

Author: Abi, B., Acciarri, R., Acero, M. A., Adamowski, M., Adams, C., Adams, D. L., Adamson, P., Adinolfi, M., Ahmad, Z., Albright, C. H., Alion, T., Anderson, J., Anderson, K., Andreopoulos, C., Andrews, M. P., Andrews, R. A., Anjos, J. dos, Ankowski, A., Anthony, J., Antonello, M., Fernandez, A. Aranda, Ariga, A., Ariga, T., Diaz, E. Arrieta, Asaadi, J., Ascencio, M., Asner, D. M., Athar, M. S., Auger, M., Aurisano, A., Aushev, V., Autiero, D., Azfar, F., Back, J. J., Back, H. O., Backhouse, C., Bai, X., Baird, M., Balantekin, A. B., Balasubramanian, S., Baller, B., Ballett, P., Bambah, B., Band, H., Bansal, M., Bansal, S., Barker, G. J., Barr, G., Monarca, J. Barranco, Barros, N., Bashyal, A., Bass, M., Bay, F., Bazo, J. L., Beacom, J. F., Behera, B. R., Bellettini, G., Bellini, V., Beltramello, O., Benekos, N., Benetti, P. A., Bercellie, A., Berman, E., Berns, H., Bernstein, R., Bertolucci, S., Bhatnagar, V., Bhuyan, B., Bian, J., Biery, K., Bishai, M., Bitadze, A., Blackburn, T., Blake, A., Blaszczyk, F. d. M., Blaufuss, E., Blazey, G. C., Blennow, M., Blucher, E., Bocean, V., Boffelli, F., Boissevain, J., Bolognesi, S., Bolton, T., Bonesini, M., Boone, T., Booth, C., Bordoni, S., Bour, P., Bourguille, B., Boyd, S. B., Boyden, D., Brailsford, D., Brandt, A., Bremer, J., Brice, S. J., Bromberg, C., Brooijmans, G., Brown, G., Brunetti, G., Buchanan, N., Budd, H., Calafiura, P., Calatayud, A., Calligarich, E., Calvo, E., Camilleri, L., Campanelli, M., Cantini, C., Carls, B., Cascella, M., Catano-Mur, E., Cavaili-Sforza, M., Cavanna, F., Centro, S., Villanueva, A. Cervera, Cervi, T., Chalifour, M., Chappuis, A., Chatterjee, A., Chattopadhyay, S., Chaussard, L., Chembra, S., Chen, H., Chen, M., Cherdack, D., Chi, C., Childress, S., Choubey, S., Choudhary, B. 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A., Mousseau, J., Mualem, L., Muether, M., Mufson, S., Murphy, S., Musser, J., Nakajima, Y., Naples, D., Navarro, J., Navas-Nicolás, D., Nelson, J., Nessi, M., Newbold, D., Newcomer, M., Nguyen, K. T. T., Nichol, R., Nicholls, T. C., Niner, E., Norman, A., Norris, B., Novakova, P., Novella, P., Nowak, E., Nowak, J., Nunes, M. S., O'Keeffe, H., Del Campo, A. Olivares, Oliveira, R., Olivier, A., Onishchuk, Y., Ovsjannikova, T., Pakvasa, S., Palamara, O., Paley, J., Palomares, C., Pantic, E., Paolone, V., Papadimitriou, V., Paramesvaran, S., Park, J., Parke, S., Parsa, Z., Pascoli, S., Pasternak, J., Pater, J., Patterson, R. B., Patton, S. J., Patzak, T., Paulos, B., Paulucci, L., Pavlovic, Z., Pawloski, G., Payne, D., Peeters, S. J. M., Pennacchio, E., Perdue, G. N., Peres, O. L. G., Periale, L., Perkin, J. D., Petridis, K., Petti, R., Petukhov, A., Picchi, P., Pietropaolo, F., Plonski, P., Plunkett, R., Poling, R., Popovic, M., Pordes, R., Pordes, S., Potekhin, M., Potenza, R., Potukuchi, B., Poudel, S., Pozimski, J., Prokofiev, O., Pruthi, N., Przewlocki, P., Pumulo, N., Pushka, D., Qian, X., Raaf, J. L., Raboanary, R., Radeka, V., Rademacker, J., Radics, B., Radovic, A., Rakhno, I., Rakotondramanana, H. T., Rakotondravohitra, L., Ramachers, Y. A., Rameika, R., Ramsey, J., Rappoldi, A., Raselli, G., Ratoff, P., Rebel, B., Regenfus, C., Reichenbacher, J., Reitzner, S. D., Remoto, A., Renshaw, A., Rescia, S., Rice, L., Rielage, K., Riesselmann, K., Rivera, D., Robinson, M., Rochester, L., Rodrigues, O. B., Rodrigues, P., Roe, B., Roser, R. M., Ross-Lonergan, M., Rossella, M., Rout, J., Roy, S., Rubbia, A., Rubbia, C., Rucinski, R., von Rohr, C. Rudolph, Russell, B., Ruterbories, D., Saakyan, R., Sachdev, K., Sahu, N., Sala, P., Samios, N., Sanchez, F., Sanchez, M. C., Sands, W. R., Santana, S., Santorelli, R., Santos, L. M., Santucci, G., Saoulidou, N., Savage, G., Scaramelli, A., Scarpelli, A., Schaffer, T., Schellman, H., Schlabach, P., Schmitz, D., Schneps, J., Scholberg, K., Schukraft, A., Segreto, E., Sehrawat, S., Sepulveda-Quiroz, J. A., Sergiampietri, F., Sexton, K., Sexton-Kennedy, L., Shaevitz, M. H., Shahi, J., Shahsavarani, S., Shanahan, P., Sharma, R., Sharma, R. K., Shaw, T., Shooltz, D., Shrock, R., Simos, N., Sinclair, J., Sinev, G., Singh, I., Singh, J., Singh, V., Sippach, F. W., Siyeon, K., Smith, A., Smith, P., Smolik, J., Smy, M., Snider, E., Snopok, P., Sobczyk, J., Sobel, H., Soderberg, M., Solomey, N., Sondheim, W., Sorel, M., Sousa, A., Soustruznik, K., Spanu, M., Spitz, J., Spooner, N. J. C., Stancari, M., Stefan, D., Stefanik, A., Steiner, H. M., Stewart, J., Stock, J., Stoica, S., Stone, J., Strait, J., Strait, M., Strauss, T., Striganov, S., Sulej, R., Sullivan, G., Sun, Y., Suter, L., Sutera, C. M., Svoboda, R., Szczerbinska, B., Szelc, A. M., Söldner-Rembold, S., Talaga, R., Tariq, S., Tatar, E., Tayloe, R., Terao, K., Thiesse, M., Thompson, L. F., Thomson, M., Thorn, C., Thorpe, M., Tian, X., Tiedt, D., Timm, S. C., Todd, J., Tonazzo, A., Tope, T., Torres, F. R., Torti, M., Tortola, M., Tortorici, F., Toups, M., Touramanis, C., Trevor, J., Tripathi, M., Tropin, I., Trzaska, W. H., Tsai, Y., Tsang, K. V., Tsaris, A., Tsenov, R., Tufanli, S., Tull, C., Turner, J., Tzanov, M., Tziaferi, E., Uchida, Y., Sankar, S. Uma, Urheim, J., Usher, T., Vagins, M. R., Vahle, P., Valdiviesso, G. A., Valerio, L., Vallari, Z., Valle, J., Van Berg, R., Van de Water, R., Varanini, F., Varner, G., Vasseur, G., Vaziri, K., Velev, G., Ventura, S., Verdugo, A., Vermeulen, M. A., Vernon, E., Viant, T., Vieira, T. V., Vignoli, C., Vihonen, S., Vilela, C., Viren, B., Vokac, P., Vrba, T., Wachala, T., Wahl, D., Wallbank, M., Wang, B., Wang, H., Wang, T., Warburton, T. K., Warner, D., Wascko, M., Waters, D., Weber, A., Weber, M., Wei, W., Weinstein, A., Wenman, D., Wetstein, M., White, A., Whitehead, L. H., Whittington, D., Wilking, M. J., Willhite, J., Wilson, P., Wilson, R. J., Wittich, P., Wolcott, J., Wong, H. H., Wongjirad, T., Wood, K., Wood, L. S., Worcester, E., Worcester, M., Wu, S., Xu, W., Yanagisawa, C., Yang, S., Yang, T., Ye, J., Yeh, M., Yershov, N., Yonehara, K., Yu, B., Yu, J., Zalesak, J., Zambelli, L., Zamorano, B., Zang, L., Zani, A., Zaremba, K., Zeller, G. P., Zhang, C., Zhou, Y., Zimmerman, E. D., Zito, M., Zuklin, J., Zutshi, V., and Zwaska, R.
Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract: ProtoDUNE-SP is the single-phase DUNE Far Detector prototype that is under construction and will be operated at the CERN Neutrino Platform (NP) starting in 2018. ProtoDUNE-SP, a crucial part of the DUNE effort towards the construction of the first DUNE 10-kt fiducial mass far detector module (17 kt total LAr mass), is a significant experiment in its own right. With a total liquid argon (LAr) mass of 0.77 kt, it represents the largest monolithic single-phase LArTPC detector to be built to date. It's technical design is given in this report., Comment: 165 pages, fix references, author list and minor numbers
Published: 2017

45. Search for active-sterile neutrino mixing using neutral-current interactions in NOvA

Author: NOvA Collaboration, Adamson, P., Aliaga, L., Ambrose, D., Anfimov, N., Antoshkin, A., Arrieta-Diaz, E., Augsten, K., Aurisano, A., Backhouse, C., Baird, M., Bambah, B. A., Bays, K., Behera, B., Bending, S., Bernstein, R., Bhatnagar, V., Bhuyan, B., Bian, J., Blackburn, T., Bolshakova, A., Bromberg, C., Brown, J., Brunetti, G., Buchanan, N., Butkevich, A., Bychkov, V., Campbell, M., Catano-Mur, E., Childress, S., Choudhary, B. C., Chowdhury, B., Coan, T. E., Coelho, J. A. B., Colo, M., Cooper, J., Corwin, L., Cremonesi, L., Cronin-Hennessy, D., Davies, G. S., Davies, J. P., Derwent, P. F., Dharmapalan, R., Ding, P., Djurcic, Z., Dukes, E. C., Duyang, H., Edayath, S., Ehrlich, R., Feldman, G. J., Frank, M. J., Gabrielyan, M., Gallagher, H. R., Germani, S., Ghosh, T., Giri, A., Gomes, R. A., Goodman, M. C., Grichine, V., Groh, M., Group, R., Grover, D., Guo, B., Habig, A., Hartnell, J., Hatcher, R., Hatzikoutelis, A., Heller, K., Himmel, A., Holin, A., Howard, B., Hylen, J., Jediny, F., Judah, M., Kafka, G. K., Kalra, D., Kasahara, S. M. S., Kasetti, S., Keloth, R., Kolupaeva, L., Kotelnikov, S., Kourbanis, I., Kreymer, A., Kumar, A., Kurbanov, S., Lackey, T., Lang, K., Lee, W. M., Lin, S., Lokajicek, M., Lozier, J., Luchuk, S., Maan, K., Magill, S., Mann, W. A., Marshak, M. L., Matera, K., Matveev, V., Méndez, D. P., Messier, M. D., Meyer, H., Miao, T., Miller, W. H., Mishra, S. R., Mohanta, R., Moren, A., Mualem, L., Muether, M., Mufson, S., Murphy, R., Musser, J., Nelson, J. K., Nichol, R., Niner, E., Norman, A., Nosek, T., Oksuzian, Y., Olshevskiy, A., Olson, T., Paley, J., Patterson, R. B., Pawloski, G., Pershey, D., Petrova, O., Petti, R., Phan-Budd, S., Plunkett, R. K., Poling, R., Potukuchi, B., Principato, C., Psihas, F., Radovic, A., Rameika, R. A., Rebel, B., Reed, B., Rocco, D., Rojas, P., Ryabov, V., Sachdev, K., Sail, P., Samoylov, O., Sanchez, M. C., Schroeter, R., Sepulveda-Quiroz, J., Shanahan, P., Sheshukov, A., Singh, J., Singh, P., Singh, V., Smolik, J., Solomey, N., Song, E., Sousa, A., Soustruznik, K., Strait, M., Suter, L., Talaga, R. L., Tas, P., Thayyullathil, R. B., Thomas, J., Tian, X., Tognini, S. C., Tripathi, J., Tsaris, A., Urheim, J., Vahle, P., Vasel, J., Vinton, L., Vold, A., Vrba, T., Wang, B., Wetstein, M., Whittington, D., Wojcicki, S. G., Wolcott, J., Yadav, N., Yang, S., Zalesak, J., Zamorano, B., and Zwaska, R.
Subjects: High Energy Physics - Experiment
Abstract: We report results from the first search for sterile neutrinos mixing with active neutrinos through a reduction in the rate of neutral-current interactions over a baseline of 810\,km between the NOvA detectors. Analyzing a 14-kton detector equivalent exposure of 6.05$\times$10$^{20}$ protons-on-target in the NuMI beam at Fermilab, we observe 95 neutral-current candidates at the Far Detector compared with $83.5 \pm 9.7 \mbox{(stat.)} \pm 9.4 \mbox{(syst.)}$ events predicted assuming mixing only occurs between active neutrino species. No evidence for $\nu_{\mu} \rightarrow \nu_{s}$ transitions is found. Interpreting these results within a 3+1 model, we place constraints on the mixing angles $\theta_{24}<20.8^{\circ}$ and $\theta_{34}<31.2^{\circ}$ at the 90% C.L. for $0.05~eV^2\leq \Delta m^2_{41}\leq 0.5~eV^2$, the range of mass splittings that produce no significant oscillations over the Near Detector baseline., Comment: 8 pages, 4 figures
Published: 2017
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46. Constraints on Oscillation Parameters from $\nu_e$ Appearance and $\nu_\mu$ Disappearance in NOvA

Author: The NOvA Collaboration, Adamson, P., Aliaga, L., Ambrose, D., Anfimov, N., Antoshkin, A., Arrieta-Diaz, E., Augsten, K., Aurisano, A., Backhouse, C., Baird, M., Bambah, B. A., Bays, K., Behera, B., Bending, S., Bernstein, R., Bhatnagar, V., Bhuyan, B., Bian, J., Blackburn, T., Bolshakova, A., Bromberg, C., Brown, J., Brunetti, G., Buchanan, N., Butkevich, A., Bychkov, V., Campbell, M., Catano-Mur, E., Childress, S., Choudhary, B. C., Chowdhury, B., Coan, T. E., Coelho, J. A. B., Colo, M., Cooper, J., Corwin, L., Cremonesi, L., Cronin-Hennessy, D., Davies, G. S., Davies, J. P., Derwent, P. F., Dharmapalan, R., Ding, P., Djurcic, Z., Dukes, E. C., Duyang, H., Edayath, S., Ehrlich, R., Feldman, G. J., Frank, M. J., Gabrielyan, M., Gallagher, H. R., Germani, S., Ghosh, T., Giri, A., Gomes, R. A., Goodman, M. C., Grichine, V., Group, R., Grover, D., Guo, B., Habig, A., Hartnell, J., Hatcher, R., Hatzikoutelis, A., Heller, K., Himmel, A., Holin, A., Hylen, J., Jediny, F., Judah, M., Kafka, G. K., Kalra, D., Kasahara, S. M. S., Kasetti, S., Keloth, R., Kolupaeva, L., Kotelnikov, S., Kourbanis, I., Kreymer, A., Kumar, A., Kurbanov, S., Lang, K., Lee, W. M., Lin, S., Liu, J., Lokajicek, M., Lozier, J., Luchuk, S., Maan, K., Magill, S., Mann, W. A., Marshak, M. L., Matera, K., Matveev, V., Méndez, D. P., Messier, M. D., Meyer, H., Miao, T., Miller, W. H., Mishra, S. R., Mohanta, R., Moren, A., Mualem, L., Muether, M., Mufson, S., Murphy, R., Musser, J., Nelson, J. K., Nichol, R., Niner, E., Norman, A., Nosek, T., Oksuzian, Y., Olshevskiy, A., Olson, T., Paley, J., Pandey, P., Patterson, R. B., Pawloski, G., Pershey, D., Petrova, O., Petti, R., Phan-Budd, S., Plunkett, R. K., Poling, R., Potukuchi, B., Principato, C., Psihas, F., Radovic, A., Rameika, R. A., Rebel, B., Reed, B., Rocco, D., Rojas, P., Ryabov, V., Sachdev, K., Sail, P., Samoylov, O., Sanchez, M. C., Schroeter, R., Sepulveda-Quiroz, J., Shanahan, P., Sheshukov, A., Singh, J., Singh, P., Singh, V., Smolik, J., Solomey, N., Song, E., Sousa, A., Soustruznik, K., Strait, M., Suter, L., Talaga, R. L., Tamsett, M. C., Tas, P., Thayyullathil, R. B., Thomas, J., Tian, X., Tognini, S. C., Tripathi, J., Tsaris, A., Urheim, J., Vahle, P., Vasel, J., Vinton, L., Vold, A., Vrba, T., Wang, B., Wetstein, M., Whittington, D., Wojcicki, S. G., Wolcott, J., Yadav, N., Yang, S., Zalesak, J., Zamorano, B., and Zwaska, R.
Subjects: High Energy Physics - Experiment
Abstract: Results are reported from an improved measurement of $\nu_\mu \rightarrow \nu_e$ transitions by the NOvA experiment. Using an exposure equivalent to $6.05\times10^{20}$ protons-on-target 33 $\nu_e$ candidates were observed with a background of $8.2\pm0.8$ (syst.). Combined with the latest NOvA $\nu_\mu$ disappearance data and external constraints from reactor experiments on $\sin^22\theta_{13}$, the hypothesis of inverted mass hierarchy with $\theta_{23}$ in the lower octant is disfavored at greater than $93\%$ C.L. for all values of $\delta_{CP}$., Comment: 6 pages, 4 figures
Published: 2017
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47. Measurement of the beam asymmetry $\Sigma$ for $\pi^0$ and $\eta$ photoproduction on the proton at $E_\gamma = 9$ GeV

Author: GlueX Collaboration, Ghoul, H. Al, Anassontzis, E. G., Austregesilo, A., Barbosa, F., Barnes, A., Beattie, T. D., Bennett, D. W., Berdnikov, V. V., Black, T., Boeglin, W., Briscoe, W. J., Brooks, W. K., Cannon, B. E., Chernyshov, O., Chudakov, E., Crede, V., Dalton, M. M., Deur, A., Dobbs, S., Dolgolenko, A., Dugger, M., Dzhygadlo, R., Egiyan, H., Eugenio, P., Fanelli, C., Foda, A. M., Frye, J., Furletov, S., Gan, L., Gasparian, A., Gerasimov, A., Gevorgyan, N., Goetzen, K., Goryachev, V. S., Guo, L., Hakobyan, H., Hardin, J., Henderson, A., Huber, G. M., Ireland, D. G., Ito, M. M., Jarvis, N. S., Jones, R. T., Kakoyan, V., Kamel, M., Klein, F. J., Kliemt, R., Kourkoumeli, C., Kuleshov, S., Kuznetsov, I., Lara, M., Larin, I., Lawrence, D., Levine, W. I., Livingston, K., Lolos, G. J., Lyubovitskij, V., Mack, D., Mattione, P. T., Matveev, V., McCaughan, M., McCracken, M., McGinley, W., McIntyre, J., Mendez, R., Meyer, C. A., Miskimen, R., Mitchell, R. E., Mokaya, F., Moriya, K., Nerling, F., Nigmatkulov, G., Ochoa, N., Ostrovidov, A. I., Papandreou, Z., Patsyuk, M., Pedroni, R., Pennington, M. R., Pentchev, L., Peters, K. J., Pooser, E., Pratt, B., Qiang, Y., Reinhold, J., Ritchie, B. G., Robison, L., Romanov, D., Salgado, C., Schumacher, R. A., Schwarz, C., Schwiening, J., Semenov, A. Yu., Semenova, I. A., Seth, K. K., Shepherd, M. R., Smith, E. S., Sober, D. I., Somov, A., Somov, S., Soto, O., Sparks, N., Staib, M. J., Stevens, J. R., Strakovsky, I. I., Subedi, A., Tarasov, V., Taylor, S., Teymurazyan, A., Tolstukhin, I., Tomaradze, A., Toro, A., Tsaris, A., Vasileiadis, G., Vega, I., Walford, N. K., Werthmuller, D., Whitlatch, T., Williams, M., Wolin, E., Xiao, T., Zarling, J., Zhang, Z., Zihlmann, B., Mathieu, V., and Nys, J.
Subjects: Nuclear Experiment
Abstract: We report measurements of the photon beam asymmetry $\Sigma$ for the reactions $\vec{\gamma}p\to p\pi^0$ and $\vec{\gamma}p\to p\eta $ from the GlueX experiment using a 9 GeV linearly-polarized, tagged photon beam incident on a liquid hydrogen target in Jefferson Lab's Hall D. The asymmetries, measured as a function of the proton momentum transfer, possess greater precision than previous $\pi^0$ measurements and are the first $\eta$ measurements in this energy regime. The results are compared with theoretical predictions based on $t$-channel, quasi-particle exchange and constrain the axial-vector component of the neutral meson production mechanism in these models., Comment: 6 pages, 6 figures, Published in Physical Review C: Rapid Communication
Published: 2017
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48. Measurement of the neutrino mixing angle $\theta_{23}$ in NOvA

Author: The NOvA Collaboration, Adamson, P., Aliaga, L., Ambrose, D., Anfimov, N., Antoshkin, A., Arrieta-Diaz, E., Augsten, K., Aurisano, A., Backhouse, C., Baird, M., Bambah, B. A., Bays, K., Behera, B., Bending, S., Bernstein, R., Bhatnagar, V., Bhuyan, B., Bian, J., Blackburn, T., Bolshakova, A., Bromberg, C., Brown, J., Brunetti, G., Buchanan, N., Butkevich, A., Bychkov, V., Campbell, M., Catano-Mur, E., Childress, S., Choudhary, B. C., Chowdhury, B., Coan, T. E., Coelho, J. A. B., Colo, M., Cooper, J., Corwin, L., Cremonesi, L., Cronin-Hennessy, D., Davies, G. S., Davies, J. P., Derwent, P. F., Desai, S., Dharmapalan, R., Ding, P., Djurcic, Z., Dukes, E. C., Duyang, H., Edayath, S., Ehrlich, R., Feldman, G. J., Gabrielyan, M., Gallagher, H. R., Germani, S., Ghosh, T., Giri, A., Gomes, R. A., Goodman, M. C., Grichine, V., Group, R., Grover, D., Guo, B., Habig, A., Hartnell, J., Hatcher, R., Hatzikoutelis, A., Heller, K., Himmel, A., Holin, A., Hylen, J., Jediny, F., Judah, M., Kafka, G. K., Kalra, D., Kasahara, S. M. S., Kasetti, S., Keloth, R., Kolupaeva, L., Kotelnikov, S., Kourbanis, I., Kreymer, A., Kumar, A., Kurbanov, S., Lang, K., Lee, W. M., Lin, S., Liu, J., Lokajicek, M., Lozier, J., Luchuk, S., Maan, K., Magill, S., Mann, W. A., Marshak, M. L., Matera, K., Matveev, V., Méndez, D. P., Messier, M. D., Meyer, H., Miao, T., Miller, W. H., Mishra, S. R., Mohanta, R., Moren, A., Mualem, L., Muether, M., Mufson, S., Murphy, R., Musser, J., Nelson, J. K., Nichol, R., Niner, E., Norman, A., Nosek, T., Oksuzian, Y., Olshevskiy, A., Olson, T., Paley, J., Pandey, P., Patterson, R. B., Pawloski, G., Pershey, D., Petrova, O., Petti, R., Phan-Budd, S., Plunkett, R. K., Poling, R., Potukuchi, B., Principato, C., Psihas, F., Radovic, A., Rameika, R. A., Rebel, B., Reed, B., Rocco, D., Rojas, P., Ryabov, V., Sachdev, K., Sail, P., Samoylov, O., Sanchez, M. C., Schroeter, R., Sepulveda-Quiroz, J., Shanahan, P., Sheshukov, A., Singh, J., Singh, P., Singh, V., Smolik, J., Solomey, N., Song, E., Sousa, A., Soustruznik, K., Strait, M., Suter, L., Talaga, R. L., Tamsett, M. C., Tas, P., Thayyullathil, R. B., Thomas, J., Tian, X., Tognini, S. C., Tripathi, J., Tsaris, A., Urheim, J., Vahle, P., Vasel, J., Vinton, L., Vold, A., Vrba, T., Wang, B., Wetstein, M., Whittington, D., Wojcicki, S. G., Wolcott, J., Yadav, N., Yang, S., Zalesak, J., Zamorano, B., and Zwaska, R.
Subjects: High Energy Physics - Experiment
Abstract: This Letter reports new results on muon neutrino disappearance from NOvA, using a 14 kton detector equivalent exposure of $6.05\times10^{20}$ protons-on-target from the NuMI beam at the Fermi National Accelerator Laboratory. The measurement probes the muon-tau symmetry hypothesis that requires maximal mixing ($\theta_{23} = \pi/4$). Assuming the normal mass hierarchy, we find $\Delta m^2 = (2.67 \pm 0.11)\times 10^{-3}$ eV$^2$ and $\sin^2 \theta_{23}$ at the two statistically degenerate values $0.404^{+0.030}_{-0.022}$ and $0.624^{+0.022}_{-0.030}$, both at the 68% confidence level. Our data disfavor the maximal mixing scenario with 2.6 $\sigma$ significance., Comment: 7 pages, 3 figures
Published: 2017
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49. IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads.

Author: Aymen Al Saadi, Dario Alfè, Yadu N. Babuji, Agastya Bhati, Ben Blaiszik, Alexander Brace, Thomas S. Brettin, Kyle Chard, Ryan Chard, Austin Clyde, Peter V. Coveney, Ian T. Foster, Tom Gibbs, Shantenu Jha, Kristopher Keipert, Dieter Kranzlmüller, Thorsten Kurth, Hyungro Lee, Zhuozhao Li, Heng Ma, Gerald Mathias, André Merzky, Alexander Partin, Arvind Ramanathan, Ashka Shah, Abraham C. Stern, Rick Stevens, Li Tan, Mikhail Titov, Anda Trifan, Aristeidis Tsaris, Matteo Turilli, Huub J. J. Van Dam, Shunzhou Wan, David Wifling, and Junqi Yin
Published: 2021
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50. Distributed Training for High Resolution Images: A Domain and Spatial Decomposition Approach.

Author: Aristeidis Tsaris, Jacob D. Hinkle, Dalton D. Lunga, and Philipe Ambrozio Dias
Published: 2021
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