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1. Quasielastic lepton-nucleus scattering and the correlated Fermi gas model

   https://doi.org/10.1103/PhysRevD.111.096021  

   Bhattacharya, Bhubanjyoti; Carey, Sam; Cohen, Erez O; Paz, Gil (May 2025, Physical Review D)

   The neutrino research program in the coming decades will require improved precision. A major source of uncertainty is the interaction of neutrinos with nuclei that serve as targets for such experiments. Broadly speaking, this interaction often depends, e.g., for charge-current quasielastic scattering, on the combination of “nucleon physics,” expressed by form factors, and “nuclear physics,” expressed by a nuclear model. It is important to get a good handle on both. We present a fully analytic implementation of the correlated Fermi gas model for electron-nucleus and charge-current quasielastic neutrino-nucleus scattering. The implementation is used to compare separately form factors and nuclear model effects for both electron-carbon and neutrino-carbon scattering data. Published by the American Physical Society2025 

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

   https://doi.org/10.1103/PhysRevD.110.092010  

   Abratenko, P; Alterkait, O; Andrade_Aldana, D; Arellano, L; Asaadi, J; Ashkenazi, A; Balasubramanian, S; Baller, B; Barnard, A; Barr, G; et al (November 2024, Physical Review D)

   We present a deep learning-based method for estimating the neutrino energy of charged-current neutrino-argon interactions. We employ a recurrent neural network (RNN) architecture for neutrino energy estimation in the MicroBooNE experiment, utilizing liquid argon time projection chamber (LArTPC) detector technology. Traditional energy estimation approaches in LArTPCs, which largely rely on reconstructing and summing visible energies, often experience sizable biases and resolution smearing because of the complex nature of neutrino interactions and the detector response. The estimation of neutrino energy can be improved after considering the kinematics information of reconstructed final-state particles. Utilizing kinematic information of reconstructed particles, the deep learning-based approach shows improved resolution and reduced bias for the muon neutrino Monte Carlo simulation sample compared to the traditional approach. In order to address the common concern about the effectiveness of this method on experimental data, the RNN-based energy estimator is further examined and validated with dedicated data-simulation consistency tests using MicroBooNE data. We also assess its potential impact on a neutrino oscillation study after accounting for all statistical and systematic uncertainties and show that it enhances physics sensitivity. This method has good potential to improve the performance of other physics analyses. Published by the American Physical Society2024 

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3. Enhancing events in neutrino telescopes through deep-learning-driven superresolution

   https://doi.org/10.1103/PhysRevD.111.L041301  

   Yu, Felix J; Kamp, Nicholas; Argüelles, Carlos A (February 2025, Physical Review D)

   Recent discoveries by neutrino telescopes, such as the IceCube Neutrino Observatory, relied extensively on machine learning (ML) tools to infer physical quantities from the raw photon hits detected. Neutrino telescope reconstruction algorithms are limited by the sparse sampling of photons by the optical modules due to the relatively large spacing (10–100 m) between them. In this Letter, we propose a novel technique that learns photon transport through the detector medium through the use of deep-learning-driven superresolution of data events. These “improved” events can then be reconstructed using traditional or ML techniques, resulting in improved resolution. Our strategy arranges additional “virtual” optical modules within an existing detector geometry and trains a convolutional neural network to predict the hits on these virtual optical modules. We show that this technique improves the angular reconstruction of muons in a generic ice-based neutrino telescope. Our results readily extend to water-based neutrino telescopes and other event morphologies. Published by the American Physical Society2025 

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4. Are there correlations in the HAWC and IceCube high energy skymaps outside the Galactic plane?

   https://doi.org/10.1103/PhysRevD.110.023009  

   Kumar, Jason; Rott, Carsten; Sandick, Pearl; Tapia-Arellano, Natalia (July 2024, Physical Review D)

   We use publicly available data to perform a search for correlations of high energy neutrino candidate events detected by IceCube and high-energy photons seen by the HAWC Collaboration. Our search is focused on unveiling such correlations outside of the Galactic plane. This search is sensitive to correlations in the neutrino candidate and photon skymaps which would arise from a population of unidentified point sources. We find no evidence for such a correlation, but suggest strategies for improvements with new datasets. Published by the American Physical Society2024 

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5. Search for a Hidden Sector Scalar from Kaon Decay in the Dimuon Final State at ICARUS

   https://doi.org/10.1103/PhysRevLett.134.151801  

   Abd_Alrahman, F; Abratenko, P; Abrego-Martinez, N; Aduszkiewicz, A; Akbar, F; Aliaga_Soplin, L; Alvarez_Garrote, R; Artero_Pons, M; Asaadi, J; Badgett, W F; et al (April 2025, Physical Review Letters)

   We present a search for long-lived particles (LLPs), produced in kaon decays, that decay to two muons inside the ICARUS neutrino detector. This channel would be a signal of hidden sector models that can address outstanding issues in particle physics such as the strong CP problem and the microphysical origin of dark matter. The search is performed with data collected in the Neutrinos at the Main Injector (NuMI) beam at Fermilab corresponding to $2.41\times {10}^{20}$protons-on-target. No new physics signal is observed, and we set world leading limits on heavy QCD axions, as well as for the Higgs portal scalar among dedicated searches. Limits are also presented in a model-independent way applicable to any new physics model predicting the process $K\to \pi +S(\to \mu \mu )$, for a LLP $S$. This result is the first search for new physics performed with the ICARUS detector at Fermilab. It paves the way for the future program of LLP searches at ICARUS. Published by the American Physical Society2025 

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