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The liquid argon time projection chamber (LArTPC) detector technology has an excellent capability to measure properties of low-energy neutrinos produced by the sun and supernovae and to look for exotic physics at very low energies. In order to achieve those physics goals, it is crucial to identify and reconstruct signals in the waveforms recorded on each TPC wire. In this paper, we report on a novel algorithm based on a one-dimensional convolutional neural network (CNN) to look for the region-of-interest (ROI) in raw waveforms. We test this algorithm using data from the ArgoNeuT experiment in conjunction with an improved noise mitigation procedure and a more realistic data-driven noise model for simulated events. This deep-learning ROI finder shows promising performance in extracting small signals and gives an efficiency approximately twice that of the traditional algorithm in the low energy region of \\(\\sim\\)0.03-0.1 MeV. This method offers great potential to explore low-energy physics using LArTPCs.

A search for millicharged particles, a simple extension of the standard model, has been performed with the ArgoNeuT detector exposed to the Neutrinos at the Main Injector beam at Fermilab. The ArgoNeuT Liquid Argon Time Projection Chamber detector enables a search for millicharged particles through the detection of visible electron recoils. We search for an event signature with two soft hits (MeV-scale energy depositions) aligned with the upstream target. For an exposure of the detector of \\(1.0\\) \\(\\times\\) \\(10^{20}\\) protons on target, one candidate event has been observed, compatible with the expected background. This search is sensitive to millicharged particles with charges between \\(10^{-3}e\\) and \\(10^{-1}e\\) and with masses in the range from \\(0.1\\) GeV to \\(3\\) GeV. This measurement provides leading constraints on millicharged particles in this large unexplored parameter space region.

We report the first electron neutrino cross section measurements on argon, based on data collected by the ArgoNeuT experiment running in the GeV-scale NuMI beamline at Fermilab. A flux-averaged \\(\\nu_e + \\overline{\\nu}_e\\) total and a lepton angle differential cross section are extracted using 13 \\(\\nu_e\\) and \\(\\overline{\\nu}_e\\) events identified with fully-automated selection and reconstruction. We employ electromagnetic-induced shower characterization and analysis tools developed to identify \\(\\nu_e/\\overline{\\nu}_e\\)-like events among complex interaction topologies present in ArgoNeuT data (\\(\\langle E_{\\bar{\\nu}_e} \\rangle = 4.3\\) GeV and \\(\\langle E_{\\nu_e} \\rangle = 10.5\\) GeV). The techniques are widely applicable to searches for electron-flavor appearance at short- and long-baseline using liquid argon time projection chamber technology. Notably, the data-driven studies of GeV-scale \\(\\nu_e/\\overline{\\nu}_e\\) interactions presented in this Letter probe an energy regime relevant for future DUNE oscillation physics.

We present the results of a search for heavy QCD axions performed by the ArgoNeuT experiment at Fermilab. We search for heavy axions produced in the NuMI neutrino beam target and absorber decaying into dimuon pairs, which can be identified using the unique capabilities of ArgoNeuT and the MINOS near detector. This decay channel is motivated by a broad class of heavy QCD axion models that address the strong CP and axion quality problems with axion masses above the dimuon threshold. We obtain new constraints at a 95\\% confidence level for heavy axions in the previously unexplored mass range between 0.2-0.9 GeV, for axion decay constants around tens of TeV.

We report the first double-differential cross section measurement of neutral-current neutral pion (NC\\(\\pi^0\\)) production in neutrino-argon scattering, as well as single-differential measurements of the same channel in terms of final states with and without protons. The kinematic variables of interest for these measurements are the \\(\\pi^0\\) momentum and the \\(\\pi^0\\) scattering angle with respect to the neutrino beam. A total of 4971 candidate NC\\(\\pi^0\\) events fully-contained within the MicroBooNE detector are selected using data collected at a mean neutrino energy of \\(\\sim 0.8\\)~GeV from \\(6.4\\times10^{20}\\) protons on target from the Booster Neutrino Beam at the Fermi National Accelerator Laboratory. After extensive data-driven model validation to ensure unbiased unfolding, the Wiener-SVD method is used to extract nominal flux-averaged cross sections. The results are compared to predictions from commonly used neutrino event generators, which tend to overpredict the measured NC\\(\\pi^0\\) cross section, especially in the 0.2-0.5~GeV/c \\(\\pi^0\\) momentum range and at forward scattering angles. Events with at least one proton present in the final state are also underestimated. This data will help improve the modeling of NC\\(\\pi^0\\) production, which represents a major background in measurements of charge-parity violation in the neutrino sector and in searches for new physics beyond the Standard Model.

We report the measurement of the differential cross section \\(d^{2}\\sigma (E_{\\nu})/ d\\cos(\\theta_{\\mu}) dP_{\\mu}\\) for inclusive muon-neutrino charged-current scattering on argon. This measurement utilizes data from 6.4\\(\\times10^{20}\\) protons on target of exposure collected using the MicroBooNE liquid argon time projection chamber located along the Fermilab Booster Neutrino Beam with a mean neutrino energy of approximately 0.8~GeV. The mapping from reconstructed kinematics to truth quantities, particularly from reconstructed to true neutrino energy, is validated within uncertainties by comparing the distribution of reconstructed hadronic energy in data to that of the model prediction in different muon scattering angle bins after applying a conditional constraint from the muon momentum distribution in data. The success of this validation gives confidence that the missing energy in the MicroBooNE detector is well-modeled within uncertainties in simulation, enabling the unfolding to a three-dimensional measurement over muon momentum, muon scattering angle, and neutrino energy. The unfolded measurement covers an extensive phase space, providing a wealth of information useful for future liquid argon time projection chamber experiments measuring neutrino oscillations. Comparisons against a number of commonly used model predictions are included and their performance in different parts of the available phase-space is discussed.

A detailed understanding of inclusive muon neutrino charged-current interactions on argon is crucial to the study of neutrino oscillations in current and future experiments using liquid argon time projection chambers. To that end, we report a comprehensive set of differential cross section measurements for this channel that simultaneously probe the leptonic and hadronic systems by dividing the channel into final states with and without protons. Measurements of the proton kinematics and proton multiplicity of the final state are also presented. For these measurements, we utilize data collected with the MicroBooNE detector from 6.4\\(\\times10^{20}\\) protons on target from the Fermilab Booster Neutrino Beam at a mean neutrino energy of approximately 0.8 GeV. We present in detail the cross section extraction procedure, including the unfolding, and model validation that uses data to model comparisons and the conditional constraint formalism to detect mismodeling that may introduce biases to extracted cross sections that are larger than their uncertainties. The validation exposes insufficiencies in the overall model, motivating the inclusion of an additional data-driven reweighting systematic to ensure the accuracy of the unfolding. The extracted results are compared to a number of event generators and their performance is discussed with a focus on the regions of phase-space that indicate the greatest need for modeling improvements.

We present a novel methodology to search for intranuclear neutron-antineutron transition (\\(n\\rightarrow\\bar{n}\\)) followed by \\(\\bar{n}\\)-nucleon annihilation within an \\(^{40}\\)Ar nucleus, using the MicroBooNE liquid argon time projection chamber (LArTPC) detector. A discovery of \\(n\\rightarrow\\bar{n}\\) transition or a new best limit on the lifetime of this process would either constitute physics beyond the Standard Model or greatly constrain theories of baryogenesis, respectively. The approach presented in this paper makes use of deep learning methods to select \\(n\\rightarrow\\bar{n}\\) events based on their unique features and differentiate them from cosmogenic backgrounds. The achieved signal and background efficiencies are (70.22\\(\\pm\\)6.04)\\% and (0.0020\\(\\pm\\)0.0003)\\%, respectively. A demonstration of a search is performed with a data set corresponding to an exposure of \\(3.32 \\times10^{26}\\,\\)neutron-years, and where the background rate is constrained through direct measurement, assuming the presence of a negligible signal. With this approach, no excess of events over the background prediction is observed, setting a demonstrative lower bound on the \\(n\\rightarrow\\bar{n}\\) lifetime in \\(^{40}\\)Ar of \\(\\tau_{\\textrm{m}} \\gtrsim 1.1\\times10^{26}\\,\\)years, and on the free \\(n\\rightarrow\\bar{n}\\) transition time of \\(\\tau_{\\textrm{\\nnbar}} \\gtrsim 2.6\\times10^{5}\\,\\)s, each at the \\(90\\%\\) confidence level. This analysis represents a first-ever proof-of-principle demonstration of the ability to search for this rare process in LArTPCs with high efficiency and low background.

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.

We present a first search for dark-trident scattering in a neutrino beam using a data set corresponding to \\(7.2 \\times 10^{20}\\) protons on target taken with the MicroBooNE detector at Fermilab. Proton interactions in the neutrino target at the Main Injector produce \\(\\pi^0\\) and \\(\\eta\\) mesons, which could decay into dark-matter (DM) particles mediated via a dark photon \\(A^\\prime\\). A convolutional neural network is trained to identify interactions of the DM particles in the liquid-argon time projection chamber (LArTPC) exploiting its image-like reconstruction capability. In the absence of a DM signal, we provide limits at the \\(90\\%\\) confidence level on the squared kinematic mixing parameter \\(\\varepsilon^2\\) as a function of the dark-photon mass in the range \\(10\\le M_{A^\\prime}\\le 400\\) MeV. The limits cover previously unconstrained parameter space for the production of fermion or scalar DM particles \\(\\chi\\) for two benchmark models with mass ratios \\(M_{\\chi}/M_{A^\\prime}=0.6\\) and \\(2\\) and for dark fine-structure constants \\(0.1\\le\\alpha_D\\le 1\\).