Search

The RES-NOVA project is an experimental initiative aimed at detecting neutrinos from the next galactic supernova using PbWO$_{4}$ cryogenic detectors, operated at low temperatures in a low-background environment. By utilizing archaeological lead (Pb) as the target material, RES-NOVA leverages its high radiopurity, large nuclear mass, and the natural abundance of $^{207}$Pb, making it well-suited for exploring both spin-independent and spin-dependent Dark Matter (DM) interactions via nuclear scattering. This work presents a background model developed for the RES-NOVA technology demonstrator and evaluates its implications for Dark Matter detection. Detailed calculations of nuclear matrix elements, combined with the unique properties of archaeological Pb, demonstrate RES-NOVA's potential as a complementary tool to existing direct detection experiments for studying Dark Matter interactions. The experiment will conduct DM searches over a broad mass range spanning 4 orders of magnitude, from sub-GeV/$c^2$ to TeV/$c^2$. In the most optimistic scenario, RES-NOVA is expected to probe DM-nucleon cross-sections down to 1$\times 10^{-43}$ cm$^2$ and 2$\times 10^{-46}$ cm$^2$ for candidates with masses of 2 GeV/$c^2$ and 20 GeV/$c^2$, respectively., Comment: updated limit calculation

Early screening for cancer has proven to improve the survival rate and spare patients from intensive and costly treatments due to late diagnosis. Cancer screening in the healthy population involves an initial risk stratification step to determine the screening method and frequency, primarily to optimize resource allocation by targeting screening towards individuals who draw most benefit. For most screening programs, age and clinical risk factors such as family history are part of the initial risk stratification algorithm. In this paper, we focus on developing a blood marker-based risk stratification approach, which could be used to identify patients with elevated cancer risk to be encouraged for taking a diagnostic test or participate in a screening program. We demonstrate that the combination of simple, widely available blood tests, such as complete blood count and complete metabolic panel, could potentially be used to identify patients at risk for colorectal, liver, and lung cancers with areas under the ROC curve of 0.76, 0.85, 0.78, respectively. Furthermore, we hypothesize that such an approach could not only be used as pre-screening risk assessment for individuals but also as population health management tool, for example to better interrogate the cancer risk in certain sub-populations.

Image dehazing is crucial for clarifying images obscured by haze or fog, but current learning-based approaches is dependent on large volumes of training data and hence consumed significant computational power. Additionally, their performance is often inadequate under non-uniform or heavy haze. To address these challenges, we developed the Detail Recovery And Contrastive DehazeNet, which facilitates efficient and effective dehazing via a dense dilated inverted residual block and an attention-based detail recovery network that tailors enhancements to specific dehazed scene contexts. A major innovation is its ability to train effectively with limited data, achieved through a novel quadruplet loss-based contrastive dehazing paradigm. This approach distinctly separates hazy and clear image features while also distinguish lower-quality and higher-quality dehazed images obtained from each sub-modules of our network, thereby refining the dehazing process to a larger extent. Extensive tests on a variety of benchmarked haze datasets demonstrated the superiority of our approach. The code repository for this work will be available soon., Comment: Submitted to a journal and currently under review. Once the paper is accepted and published, the copyright will be transferred to the corresponding journal

While neutrinos are often treated as a background for many dark matter experiments, these particles offer a new avenue for physics: the detection of core-collapse supernovae. Supernovae are extremely energetic, violent and complex events that mark the death of massive stars. During their collapse stars emit a large number of neutrinos in a short burst. These neutrinos carry 99\% of the emitted energy which makes their detection fundamental in understanding supernovae. This paper illustrates how COSINUS (Cryogenic Observatory for SIgnatures seen in Next-generation Underground Searches), a sodium iodide (NaI) based dark matter search, will be sensitive to the next galactic core-collapse supernova. The experiment is composed of two separate detectors which will be sensitive to far and nearby supernovae. The inner core of the experiment will consist of NaI crystals operating as scintillating calorimeters, mainly sensitive to the Coherent Elastic Scattering of Neutrinos (CE$\nu$NS) against the Na and I nuclei. The low mass of the cryogenic detectors gives the experiment a sensitivity to close supernovae below 1kpc without pileup. They will see up to hundreds of CE$\nu$NS events from a supernova happening at 200pc. The crystals reside at the center of a cylindrical 230T water tank, instrumented with 30 photomultipliers. This tank acts as a passive and active shield able to detect the Cherenkov radiation induced by impinging charged particles from ambient and cosmogenic radioactivity. A supernova near the Milky Way Center (10kpc) will be easily detected inducing $\sim$60 measurable events, and the water tank will have a 3$\sigma$ sensitivity to supernovae up to 22kpc, seeing $\sim$10 events. This paper shows how, even without dedicated optimization, modern dark matter experiments will also play their part in the multi-messenger effort to detect the next galactic core-collapse supernova.

The Cryogenic Underground Observatory for Rare Events (CUORE) is a detector array comprised by 988 5$\;$cm$\times$5$\;$cm$\times$5$\;$cm TeO$_2$ crystals held below 20 mK, primarily searching for neutrinoless double-beta decay in $^{130}$Te. Unprecedented in size amongst cryogenic calorimetric experiments, CUORE provides a promising setting for the study of exotic through-going particles. Using the first tonne-year of CUORE's exposure, we perform a search for hypothesized fractionally charged particles (FCPs), which are well-motivated by various Standard Model extensions and would have suppressed interactions with matter. No excess of FCP candidate tracks is observed over background, setting leading limits on the underground FCP flux with charges between $e/24-e/5$ at 90\% confidence level. Using the low background environment and segmented geometry of CUORE, we establish the sensitivity of tonne-scale sub-Kelvin detectors to diverse signatures of new physics., Comment: 7 pages, 5 figures

Superconducting qubits can be sensitive to abrupt energy deposits caused by cosmic rays and ambient radioactivity. Previous studies have focused on understanding possible correlated effects over time and distance due to cosmic rays. In this study, for the first time, we directly compare the response of a transmon qubit measured initially at the Fermilab SQMS above-ground facilities and then at the deep underground Gran Sasso Laboratory (INFN-LNGS, Italy). We observe same average qubit lifetime T$_1$ of roughly 80 microseconds at above and underground facilities. We then apply a fast decay detection protocol and investigate the time structure, sensitivity and relative rates of triggered events due to radiation versus intrinsic noise, comparing above and underground performance of several high-coherence qubits. Using gamma sources of variable activity we calibrate the response of the qubit to different levels of radiation in an environment with minimal background radiation. Results indicate that qubits respond to a strong gamma source and it is possible to detect particle impacts. However, when comparing above and underground results, we do not observe a difference in radiation induced-like events for these sapphire and niobium-based transmon qubits. We conclude that the majority of these events are not radiation related and to be attributed to other noise sources which by far dominate single qubit errors in modern transmon qubits., Comment: 9+7 pages, 7+5 figures

We present the model we developed to reconstruct the CUORE radioactive background based on the analysis of an experimental exposure of 1038.4 kg yr. The data reconstruction relies on a simultaneous Bayesian fit applied to energy spectra over a broad energy range. The high granularity of the CUORE detector, together with the large exposure and extended stable operations, allow for an in-depth exploration of both spatial and time dependence of backgrounds. We achieve high sensitivity to both bulk and surface activities of the materials of the setup, detecting levels as low as 10 nBq kg$^{-1}$ and 0.1 nBq cm$^{-2}$, respectively. We compare the contamination levels we extract from the background model with prior radio-assay data, which informs future background risk mitigation strategies. The results of this background model play a crucial role in constructing the background budget for the CUPID experiment as it will exploit the same CUORE infrastructure.

High-quality images are crucial in remote sensing and UAV applications, but atmospheric haze can severely degrade image quality, making image dehazing a critical research area. Since the introduction of deep convolutional neural networks, numerous approaches have been proposed, and even more have emerged with the development of vision transformers and contrastive/few-shot learning. Simultaneously, papers describing dehazing architectures applicable to various Remote Sensing (RS) domains are also being published. This review goes beyond the traditional focus on benchmarked haze datasets, as we also explore the application of dehazing techniques to remote sensing and UAV datasets, providing a comprehensive overview of both deep learning and prior-based approaches in these domains. We identify key challenges, including the lack of large-scale RS datasets and the need for more robust evaluation metrics, and outline potential solutions and future research directions to address them. This review is the first, to our knowledge, to provide comprehensive discussions on both existing and very recent dehazing approaches (as of 2024) on benchmarked and RS datasets, including UAV-based imagery., Comment: Submitted to journal and under review, once the paper is accepted, the copyright will be transferred to the corresponding journal

COSINUS is a dark matter (DM) direct search experiment that uses sodium iodide (NaI) crystals as cryogenic calorimeters. Thanks to the low nuclear recoil energy threshold and event-by-event discrimination capability, COSINUS will address the long-standing DM claim made by the DAMA/LIBRA collaboration. The experiment is currently under construction at the Laboratori Nazionali del Gran Sasso, Italy, and employs a large cylindrical water tank as a passive shield to meet the required background rate. However, muon-induced neutrons can mimic a DM signal therefore requiring an active veto system, which is achieved by instrumenting the water tank with an array of photomultiplier tubes (PMTs). This study optimizes the number, arrangement, and trigger conditions of the PMTs as well as the size of an optically invisible region. The objective was to maximize the muon veto efficiency while minimizing the accidental trigger rate due to the ambient and instrumental background. The final configuration predicts a veto efficiency of 99.63 $\pm$ 0.16 $\%$ and 44.4 $\pm$ $5.6\%$ in the tagging of muon events and showers of secondary particles, respectively. The active veto will reduce the cosmogenic neutron background rate to 0.11 $\pm$ 0.02 cts$\cdot$kg$^{-1}$$\cdot$year$^{-1}$, corresponding to less than one background event in the region of interest for the whole COSINUS-1$\pi$ exposure of 1000 kg$\cdot$days.

In this paper, we present the first measurement of a Gallium Arsenide crystal working as low-temperature calorimeter for direct Dark Matter (DM) searches within the DAREDEVIL (DARk-mattEr DEVIces for Low energy detection) project. In the quest for direct dark matter detection, innovative approaches to lower the detection thershold and explore the sub-GeV mass DM range, have gained high relevance in the last decade. This study presents the pioneering use of Gallium Arsenide (GaAs) as a low-temperature calorimeter for probing the dark matter-electron interaction channel. Our experimental setup features a GaAs crystal at ultralow temperature of 15 mK, coupled with a Neutron Transmutation Doped (NTD) thermal sensor for precise energy estimation. This configuration is the first step towards the detection of single electrons scattered by dark matter particles within the GaAs crystal, to significantly improve the sensitivity to low-mass dark matter candidates.

The matter-antimatter asymmetry underlines the incompleteness of the current understanding of particle physics. Neutrinoless double-beta ($0\nu \beta\beta$) decay may help explain this asymmetry, while unveiling the Majorana nature of the neutrino. The CUORE experiment searches for $0\nu \beta\beta$ decay of $^{130}$Te using a tonne-scale cryogenic calorimeter operated at milli-kelvin temperatures. We report no evidence for $0\nu \beta\beta$ decay and place a lower limit on the half-life of T$_{1/2}$ $>$ 3.8 $\times$ 10$^{25}$ years (90% C.I.) with over 2 tonne$\cdot$year TeO$_2$ exposure. The tools and techniques developed for this result and the 5 year stable operation of nearly 1000 detectors demonstrate the infrastructure for a next-generation experiment capable of searching for $0\nu \beta\beta$ decay across multiple isotopes.

Carbon emissions have become a specific alarming indicators and intricate challenges that lead an extended argue about climate change. The growing trend in the utilization of fossil fuels for the economic progress and simultaneously reducing the carbon quantity has turn into a substantial and global challenge. The aim of this paper is to examine the driving factors of CO$_2$ emissions from energy sector in Romania during the period 2008-2022 emissions using the log mean Divisia index (LMDI) method and takes into account five items: CO$_2$ emissions, primary energy resources, energy consumption, gross domestic product and population, the driving forces of CO$_2$ emissions, based on which it was calculated the contribution of carbon intensity, energy mixes, generating efficiency, economy, and population. The results indicate that generating efficiency effect -90968.57 is the largest inhibiting index while economic effect is the largest positive index 69084.04 having the role of increasing CO$_2$ emissions.

Current bounds on neutrino Majorana mass are affected by significant uncertainties in the nuclear calculations for neutrinoless double-beta decay. A key issue for a data-driven improvement of the nuclear theory is the actual value of the axial coupling constant $g_A$, which can be investigated through forbidden $\beta$-decays. We present the first measurement of 4$^{th}$-forbidden $\beta$-decay of $^{115}$In with a cryogenic calorimeter based on Indium Iodide. Exploiting the enhanced spectral shape method for the first time to this isotope, our study accurately determines simultaneously spectral shape, $g_A$, and half-life. The Interacting Shell Model, which best fits our data, indicates a half-life for this decay at $T_{1/2}=(5.26\pm0.06) \times 10^{14}$,yr.

The LUCE (LUtetium sCintillation Experiment) project will search for the 176Lu electron capture based on a milli-Kelvin calorimetric approach. This decay is of special interest in the field of nuclear structure, with implications for the s-process and for a better comprehension of the nuclear matrix elements of neutrinoless double beta decay (0{\nu}\b{eta}\b{eta}) and two-neutrino double beta decay (2{\nu}\b{eta}\b{eta}). Possible impacts also include the development of a new class of coherent elastic neutrino-nucleus scattering (CE{\nu}NS) and spin-dependent (independent) dark matter detectors. We report on the current status and design of a novel detector cryogenic-module for the measurement of the electron capture and detail a future measurement plan., Comment: proceedings

Recent advancements have significantly improved the efficiency and effectiveness of deep learning methods for imagebased remote sensing tasks. However, the requirement for large amounts of labeled data can limit the applicability of deep neural networks to existing remote sensing datasets. To overcome this challenge, fewshot learning has emerged as a valuable approach for enabling learning with limited data. While previous research has evaluated the effectiveness of fewshot learning methods on satellite based datasets, little attention has been paid to exploring the applications of these methods to datasets obtained from UAVs, which are increasingly used in remote sensing studies. In this review, we provide an up to date overview of both existing and newly proposed fewshot classification techniques, along with appropriate datasets that are used for both satellite based and UAV based data. Our systematic approach demonstrates that fewshot learning can effectively adapt to the broader and more diverse perspectives that UAVbased platforms can provide. We also evaluate some SOTA fewshot approaches on a UAV disaster scene classification dataset, yielding promising results. We emphasize the importance of integrating XAI techniques like attention maps and prototype analysis to increase the transparency, accountability, and trustworthiness of fewshot models for remote sensing. Key challenges and future research directions are identified, including tailored fewshot methods for UAVs, extending to unseen tasks like segmentation, and developing optimized XAI techniques suited for fewshot remote sensing problems. This review aims to provide researchers and practitioners with an improved understanding of fewshot learnings capabilities and limitations in remote sensing, while highlighting open problems to guide future progress in efficient, reliable, and interpretable fewshot methods., Comment: Under review, once the paper is accepted, the copyright will be transferred to the corresponding journal

Sodium iodide (NaI) based cryogenic scintillating calorimeters using quantum sensors for signal read out have shown promising first results towards a model-independent test of the annually modulating signal detected by the DAMA/LIBRA dark matter experiment. The COSINUS collaboration has previously reported on the first above-ground measurements using a dual channel readout of phonons and light based on transition edge sensors (TESs) that allows for particle discrimination on an event-by-event basis. In this letter, we outline the first underground measurement of a NaI cryogenic calorimeter read out via the novel remoTES scheme. A 3.67 g NaI absorber with an improved silicon light detector design was operated at the Laboratori Nazionali del Gran Sasso, Italy. A significant improvement in the discrimination power of $e^-$/$\gamma$-events to nuclear recoils was observed with a five-fold improvement in the nuclear recoil baseline resolution, achieving $\sigma$ = 441 eV. Furthermore, we present a limit on the spin-independent dark-matter nucleon elastic scattering cross-section achieving a sensitivity of $\mathcal{O}$(pb) with an exposure of only 11.6 g d., Comment: 11 pages, 14 figures

The COSINUS direct dark matter experiment situated at Laboratori Nazionali del Gran Sasso in Italy is set to investigate the nature of the annually modulating signal detected by the DAMA/LIBRA experiment. COSINUS has already demonstrated that sodium iodide crystals can be operated at mK temperature as cryogenic scintillating calorimeters using transition edge sensors, despite the complication of handling a hygroscopic and low melting point material. With results from a new COSINUS prototype, we show that particle discrimination on an event-by-event basis in NaI is feasible using the dual-channel readout of both phonons and scintillation light. The detector was mounted in the novel remoTES design and operated in an above-ground facility for 9.06 g$\cdot$d of exposure. With a 3.7 g NaI crystal, e$^-$/$\gamma$ events could be clearly distinguished from nuclear recoils down to the nuclear recoil energy threshold of 15 keV., Comment: 7 pages, 9 figures

We report on the results obtained with the global CUPID-0 background model, which combines the data collected in the two measurement campaigns for a total exposure of 16.6 kg$\times$yr. We identify with improved precision the background sources in the region of interest for neutrinoless double $\beta$-decay, making more solid the foundations for the background budget of the next-generation CUPID experiment. Relying on the excellent data reconstruction, we measure the two-neutrino double $\beta$-decay half-life of $^{82}$Se with unprecedented accuracy: $T_{1/2}^{2\nu} = [8.69 \pm 0.05 \textrm{(stat.)}~^{+0.09}_{-0.06} \textrm{(syst.)}] \times 10^{19}~\textrm{yr}$.

The ACCESS (Array of Cryogenic Calorimeters to Evaluate Spectral Shapes) project aims to establish a novel technique to perform precision measurements of forbidden \b{eta}-decays, which can serve as an important benchmark for nuclear physics calculations and represent a significant background in astroparticle physics experiments. ACCESS will operate a pilot array of cryogenic calorimeters based on natural and doped crystals containing \b{eta}-emitting radionuclides. In this way, natural (e.g. 113 Cd and 115In) and synthetic isotopes (e.g. 99Tc) will be simultaneously measured with a common experimental technique. The array will also include further crystals optimised to disentangle the different background sources, thus reducing the systematic uncertainty. In this paper, we give an overview of the ACCESS research program, discussing a detector design study and promising results of 115In., Comment: 12 pages, 12 figures

Incorporating deep learning (DL) classification models into unmanned aerial vehicles (UAVs) can significantly augment search-and-rescue operations and disaster management efforts. In such critical situations, the UAV's ability to promptly comprehend the crisis and optimally utilize its limited power and processing resources to narrow down search areas is crucial. Therefore, developing an efficient and lightweight method for scene classification is of utmost importance. However, current approaches tend to prioritize accuracy on benchmark datasets at the expense of computational efficiency. To address this shortcoming, we introduce the Wider ATTENTION EfficientNet (WATT-EffNet), a novel method that achieves higher accuracy with a more lightweight architecture compared to the baseline EfficientNet. The WATT-EffNet leverages width-wise incremental feature modules and attention mechanisms over width-wise features to ensure the network structure remains lightweight. We evaluate our method on a UAV-based aerial disaster image classification dataset and demonstrate that it outperforms the baseline by up to 15 times in terms of classification accuracy and 38.3% in terms of computing efficiency as measured by Floating Point Operations per second (FLOPs). Additionally, we conduct an ablation study to investigate the effect of varying the width of WATT-EffNet on accuracy and computational efficiency. Our code is available at \url{https://github.com/TanmDL/WATT-EffNet}., Comment: This paper is accepted in IEEE Trans. GRSL

CUPID is a next-generation bolometric experiment aiming at searching for neutrinoless double-beta decay with ~250 kg of isotopic mass of $^{100}$Mo. It will operate at $\sim$10 mK in a cryostat currently hosting a similar-scale bolometric array for the CUORE experiment at the Gran Sasso National Laboratory (Italy). CUPID will be based on large-volume scintillating bolometers consisting of $^{100}$Mo-enriched Li$_2$MoO$_4$ crystals, facing thin Ge-wafer-based bolometric light detectors. In the CUPID design, the detector structure is novel and needs to be validated. In particular, the CUORE cryostat presents a high level of mechanical vibrations due to the use of pulse tubes and the effect of vibrations on the detector performance must be investigated. In this paper we report the first test of the CUPID-design bolometric light detectors with NTD-Ge sensors in a dilution refrigerator equipped with a pulse tube in an above-ground lab. Light detectors are characterized in terms of sensitivity, energy resolution, pulse time constants, and noise power spectrum. Despite the challenging noisy environment due to pulse-tube-induced vibrations, we demonstrate that all the four tested light detectors comply with the CUPID goal in terms of intrinsic energy resolution of 100 eV RMS baseline noise. Indeed, we have measured 70--90 eV RMS for the four devices, which show an excellent reproducibility. We have also obtained outstanding energy resolutions at the 356 keV line from a $^{133}$Ba source with one light detector achieving 0.71(5) keV FWHM, which is -- to our knowledge -- the best ever obtained when compared to $\gamma$ detectors of any technology in this energy range., Comment: Prepared for submission to JINST; 16 pages, 7 figures, and 1 table

An array of twelve 0.28 kg lithium molybdate (LMO) low-temperature bolometers equipped with 16 bolometric Ge light detectors, aiming at optimization of detector structure for CROSS and CUPID double-beta decay experiments, was constructed and tested in a low-background pulse-tube-based cryostat at the Canfranc underground laboratory in Spain. Performance of the scintillating bolometers was studied depending on the size of phonon NTD-Ge sensors glued to both LMO and Ge absorbers, shape of the Ge light detectors (circular vs. square, from two suppliers), in different light collection conditions (with and without reflector, with aluminum coated LMO crystal surface). The scintillating bolometer array was operated over 8 months in the low-background conditions that allowed to probe a very low, $\mu$Bq/kg, level of the LMO crystals radioactive contamination by $^{228}$Th and $^{226}$Ra., Comment: Prepared for submission to JINST; 23 pages, 9 figures, and 4 tables

K-FAC (arXiv:1503.05671, arXiv:1602.01407) is a tractable implementation of Natural Gradient (NG) for Deep Learning (DL), whose bottleneck is computing the inverses of the so-called ``Kronecker-Factors'' (K-factors). RS-KFAC (arXiv:2206.15397) is a K-FAC improvement which provides a cheap way of estimating the K-factors inverses. In this paper, we exploit the exponential-average construction paradigm of the K-factors, and use online numerical linear algebra techniques to propose an even cheaper (but less accurate) way of estimating the K-factors inverses. In particular, we propose a K-factor inverse update which scales linearly in layer size. We also propose an inverse application procedure which scales linearly as well (the one of K-FAC scales cubically and the one of RS-KFAC scales quadratically). Overall, our proposed algorithm gives an approximate K-FAC implementation whose preconditioning part scales linearly in layer size (compare to cubic for K-FAC and quadratic for RS-KFAC). Importantly however, this update is only applicable in some circumstances (typically for all FC layers), unlike the RS-KFAC approach (arXiv:2206.15397). Numerical results show RS-KFAC's inversion error can be reduced with minimal CPU overhead by adding our proposed update to it. Based on the proposed procedure, a correction to it, and RS-KFAC, we propose three practical algorithms for optimizing generic Deep Neural Nets. Numerical results show that two of these outperform RS-KFAC for any target test accuracy on CIFAR10 classification with a slightly modified version of VGG16_bn. Our proposed algorithms achieve 91$\%$ test accuracy faster than SENG (the state of art implementation of empirical NG for DL; arXiv:2006.05924) but underperform it for higher test-accuracy., Comment: Version 2 (new numerical experiments coming soon, in V3)