Department of Physics

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Author: search.filters.author.Chauhan, Bhavesh

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    Dune software and computing research and development
    (2025-03) Chauhan, Bhavesh
    The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy toward the implementation of this leading-edge, large-scale science project. The ambitious physics program of Phase I and Phase II of DUNE is dependent upon deployment and utilization of significant computing resources, and successful research and development of software (both infrastructure and algorithmic) in order to achieve these scientific goals. This submission discusses the computing resources projections, infrastructure support, and software development needed for DUNE during the coming decades as an input to the European Strategy for Particle Physics Update for 2026. The DUNE collaboration is submitting four main contributions to the 2026 Update of the European Strategy for Particle Physics process. This submission to the 'Computing' stream focuses on DUNE software and computing. Additional inputs related to the DUNE science program, DUNE detector technologies and R&D, and European contributions to Fermilab accelerator upgrades and facilities for the DUNE experiment, are also being submitted to other streams.
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    European contributions to fermilab accelerator upgrades and facilities for the dune experiment
    (2025-03) Chauhan, Bhavesh
    The Proton Improvement Plan (PIP-II) to the FNAL accelerator chain and the Long-Baseline Neutrino Facility (LBNF) will provide the world's most intense neutrino beam to the Deep Underground Neutrino Experiment (DUNE) enabling a wide-ranging physics program. This document outlines the significant contributions made by European national laboratories and institutes towards realizing the first phase of the project with a 1.2 MW neutrino beam. Construction of this first phase is well underway. For DUNE Phase II, this will be closely followed by an upgrade of the beam power to > 2 MW, for which the European groups again have a key role and which will require the continued support of the European community for machine aspects of neutrino physics. Beyond the neutrino beam aspects, LBNF is also responsible for providing unique infrastructure to install and operate the DUNE neutrino detectors at FNAL and at the Sanford Underground Research Facility (SURF). The cryostats for the first two Liquid Argon Time Projection Chamber detector modules at SURF, a contribution of CERN to LBNF, are central to the success of the ongoing execution of DUNE Phase I. Likewise, successful and timely procurement of cryostats for two additional detector modules at SURF will be critical to the success of DUNE Phase II and the overall physics program. The DUNE Collaboration is submitting four main contributions to the 2026 Update of the European Strategy for Particle Physics process. This paper is being submitted to the 'Accelerator technologies' and 'Projects and Large Experiments' streams. Additional inputs related to the DUNE science program, DUNE detector technologies and R&D, and DUNE software and computing, are also being submitted to other streams.
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    Neutrino interaction vertex reconstruction in DUNE with Pandora deep learning
    (Springer, 2025-06) Chauhan, Bhavesh
    The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours
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    Probing the cosmic sterile-neutrino background with IceCube
    (APS, 2025-08) Chauhan, Bhavesh
    In this paper, we take a close look at the interaction between the TeV–PeV energy astrophysical neutrinos and a hypothetical cosmic sterile-neutrino background. These interactions yield absorption features, also called “dips”, in the astrophysical neutrino spectrum, which are studied using the deposited energy distribution of high-energy starting events (HESE) in the IceCube detector. We improve upon the previous analysis by including the effects of regeneration and a realistic source distribution on the propagation of astrophysical neutrinos. We use the latest 7.5-year HESE dataset and include the observation of Glashow resonance in our analysis. We evaluate the impact of these dips on the inferred spectral index and overall normalization of the astrophysical neutrinos. We find a mild preference for dips in the 300–800 TeV range, and the best-fit parameters for the mass of sterile-neutrino and the mediator are 0.5 eV and 23 MeV, respectively. We find that the inclusion of these absorption features lowers the spectral index of astrophysical neutrinos to 2.60+0.19−0.16. We show qualitatively that the lower spectral index from the HESE sample can reduce the disagreement with the Northern Tracks sample. We also forecast the event spectrum for IceCube-Gen2 for the two different fits.
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    Spatial and temporal evaluations of the liquid argon purity in ProtoDUNE-SP
    (IOP, 2025-09) Chauhan, Bhavesh
    Liquid argon time projection chambers (LArTPCs) rely on highly pure argon to ensure that ionization electrons produced by charged particles reach readout arrays. ProtoDUNE Single-Phase (ProtoDUNE-SP) was an approximately 700-ton liquid argon detector intended to prototype the Deep Underground Neutrino Experiment (DUNE) Far Detector Horizontal Drift module. It contains two drift volumes bisected by the cathode plane assembly, which is biased to create an almost uniform electric field in both volumes. The DUNE Far Detector modules must have robust cryogenic systems capable of filtering argon and supplying the TPC with clean liquid. This paper will explore comparisons of the argon purity measured by the purity monitors with those measured using muons in the TPC from October 2018 to November 2018. A new method is introduced to measure the liquid argon purity in the TPC using muons crossing both drift volumes of ProtoDUNE-SP. For extended periods on the timescale of weeks, the drift electron lifetime was measured to be above 30 ms using both systems. A particular focus will be placed on the measured purity of argon as a function of position in the detector
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    Towards mono-energetic virtual ν beam cross-section measurements: A feasibility study of ν-Ar interaction analysis with DUNE-PRISM
    (2025-09) Chauhan, Bhavesh
    Neutrino-nucleus cross-section measurements are critical for future neutrino oscillation analyses. However, our models to describe them require further refinement, and a deeper understanding of the underlying physics is essential for future neutrino oscillation experiments to realize their ambitious physics goals. Current neutrino cross-section measurements provide clear deficiencies in neutrino interaction modeling, but almost all are reported averaged over broad neutrino fluxes, rendering their interpretation challenging. Using the DUNE-PRISM concept (Deep Underground Neutrino Experiment Precision Reaction Independent Spectrum Measurement) -- a movable near detector that samples multiple off-axis positions -- neutrino interaction measurements can be used to construct narrow virtual fluxes (less than 100 MeV wide). These fluxes can be used to extract charged-current neutrino-nucleus cross sections as functions of outgoing lepton kinematics within specific neutrino energy ranges. Based on a dedicated simulation with realistic event statistics and flux-related systematic uncertainties, but assuming an almost-perfect detector, we run a feasibility study demonstrating how DUNE-PRISM data can be used to measure muon neutrino charged-current integrated and differential cross sections over narrow fluxes. We find that this approach enables a model independent reconstruction of powerful observables, including energy transfer, typically accessible only in electron scattering measurements, but that large exposures may be required for differential cross-section measurements with few-% statistical uncertainties.
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    Operation of a modular 3D-pixelated liquid argon time-projection chamber in a neutrino beam
    (2025) Chauhan, Bhavesh
    The 2x2 Demonstrator, a prototype for the Deep Underground Neutrino Experiment (DUNE) liquid argon (LAr) Near Detector, was exposed to the Neutrinos from the Main Injector (NuMI) neutrino beam at Fermi National Accelerator Laboratory (Fermilab). This detector prototypes a new modular design for a liquid argon time-projection chamber (LArTPC), comprised of a two-by-two array of four modules, each further segmented into two optically-isolated LArTPCs. The 2x2 Demonstrator features a number of pioneering technologies, including a low-profile resistive field shell to establish drift fields, native 3D ionization pixelated imaging, and a high-coverage dielectric light readout system. The 2.4 tonne active mass detector is flanked upstream and downstream by supplemental solid-scintillator tracking planes, repurposed from the MINERvA experiment, which track ionizing particles exiting the argon volume. The antineutrino beam data collected by the detector over a 4.5 day period in 2024 include over 30,000 neutrino interactions in the LAr active volume-the first neutrino interactions reported by a DUNE detector prototype. During its physics-quality run, the 2x2 Demonstrator operated at a nominal drift field of 500 V/cm and maintained good LAr purity, with a stable electron lifetime of approximately 1.25 ms. This paper describes the detector and supporting systems, summarizes the installation and commissioning, and presents the initial validation of collected NuMI beam and off-beam self-triggers. In addition, it highlights observed interactions in the detector volume, including candidate muon anti-neutrino events.
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    Identification of low-energy kaons in the ProtoDUNE-SP detector
    (2025) Chauhan, Bhavesh
    The Deep Underground Neutrino Experiment (DUNE) is a next-generation neutrino experiment with a rich physics program that includes searches for the hypothetical phenomenon of proton decay. Utilizing liquid-argon time-projection chamber technology, DUNE is expected to achieve world-leading sensitivity in the proton decay channels that involve charged kaons in their final states. The first DUNE demonstrator, ProtoDUNE Single-Phase, was a 0.77 kt detector that operated from 2018 to 2020 at the CERN Neutrino Platform, exposed to a mixed hadron and electron test-beam with momenta ranging from 0.3 to 7 GeV/c. We present a selection of low-energy kaons among the secondary particles produced in hadronic reactions, using data from the 6 and 7 GeV/c beam runs. The selection efficiency is 1% and the sample purity 92%. The initial energies of the selected kaon candidates encompass the expected energy range of kaons originating from proton decay events in DUNE (below ∼200 MeV). In addition, we demonstrate the capability of this detector technology to discriminate between kaons and other particles such as protons and muons, and provide a comprehensive description of their energy loss in liquid argon, which shows good agreement with the simulation. These results pave the way for future proton decay searches at DUNE.
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    Probing the cosmic sterile-neutrino background with IceCube
    (2024-09) Chauhan, Bhavesh
    In this paper, we take a close look at the interaction between the TeV--PeV energy astrophysical neutrinos and a hypothetical cosmic sterile-neutrino background. These interactions yield absorption features, also called ``dips", in the astrophysical neutrino spectrum, which are studied using the deposited energy distribution of high-energy starting events (HESE) in the IceCube detector. We improve upon the previous analysis by including the effects of regeneration and a realistic source distribution on the propagation of astrophysical neutrinos. We use the latest 7.5-year HESE dataset and include the observation of Glashow resonance in our analysis. We evaluate the impact of these dips on the inferred spectral index and overall normalization of the astrophysical neutrinos. We find a mild preference for dips in the 300--800 TeV range, and the best-fit parameters for the mass of sterile-neutrino and the mediator are 0.5 eV and 23 MeV, respectively. We find that the inclusion of these absorption features lowers the spectral index of astrophysical neutrinos to 2.60+0.19−0.16. The lower spectral index can reduce the disagreement with the Northern Tracks sample but requires dedicated analysis. We also forecast the event spectrum for IceCube-Gen2 for the two different fits.
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    Neutrino interaction vertex reconstruction in dune with pandora deep learning
    (2025) Chauhan, Bhavesh
    The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours.