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At least 217 records · Page 12

Progress Toward a Deep-Learning-Based Search for Low-Energy Electron Neutrinos in MicroBooNE

MicroBooNE is a neutrino experiment based at Fermilab which consists of a liquid argon time-projection chamber in the Booster Neutrino Beam (BNB). The experiment aims to investigate the excess of electron neutrino-like events seen by the MiniBooNE experiment, also located in the BNB. I will describe the status of our deep-learning-based search for low-energy electron neutrino interactions within the MicroBooNE detector and the muon neutrino interactions by which we will constrain them. This analysis features a novel hybrid approach of traditional reconstruction methods combined with the use of convolutional neural networks, a type of deep learning algorithm highly adept at pattern recognition. I will present work on ongoing studies that will characterize and quantify the systematic uncertainties associated with this analysis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Towards Measuring Longitudinal Electron Diffusion in the MicroBooNE LArTPC

MicroBooNE Liquid Argon Time Projection Chamber (LArTPC) Neutrino interactions reconstructed based on ionization electrons and scintillation light Primary goals: Investigate MiniBooNE low-energy excess Neutrino-argon cross-sections LArTPC Electron Diffusion Simulation and Selection Waveform Averaging Extraction of D L Challenges and Future Work

43 PARTICLE ACCELERATORS↗

Hunting Muon Neutrinos in MicroBooNE With Deep Learning Techniques

The MicroBooNE experiment is a large Liquid Argon TPC located on the Booster Neutrino Beam at Fermi National Laboratory. The primary goal is to investigate the source of the excess of low energy electron neutrino events observed by the MiniBooNE experiment. The main background to the oscillation signal arises from intrinsic electron neutrinos coming from decay of pions and muons in the beam rather than oscillation. To constrain the systematic uncertainty on this background, MicroBooNE will perform a combined fit to both the muon and electron neutrino spectra. This method provides constraints on flux, cross section, and detector uncertainties. This poster will discuss the identification and selection of muon neutrino events using a novel hybrid approach of Deep Learning and traditional reconstruction algorithms that can be used for this constraint.

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Search for a Low Energy Excess in MicroBooNE

MicroBooNE (the Micro Booster Neutrino Experiment) is a liquid argon time-projection chamber (TPC) experiment designed for short-baseline neutrino physics, currently running at Fermilab. It aims to address the anomalous excess of low-energy events observed by the MiniBooNE experiment. Recent progress towards the search for the low-energy events have brought to develop fully automated event selection algorithm to identify charged-current electron neutrino event candidates with no pions and at least one proton in the final state ($\nu_e~\text{CC} 0\pi \text{N}p$) using the Pandora multi-algorithm pattern recognition. Several cross checks and sidebands have been studied so far to validate the analysis.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

MicroBooNE's Search for Photon-Like Low Energy Excess Under a Neutral Current $\Delta \to N_{\gamma}$ Hypothesis

MicroBooNE is a Liquid Argon Time Projection Chamber which has been taking neutrino data at Fermilab’s Booster Neutrino Beamline since October 2015. One of its primary goals is to investigate the Low Energy Excess of neutrino events observed by the MiniBooNE experiment, for which one candidate interpretation is neutrino neutral current (NC) resonant ∆ production with subsequent radiative decay. This is a standard model (SM) source of low energy single-photon events that has never been directly measured in neutrino scattering. If found to have a higher than SM-predicted rate it could be a sizable contribution to the Low Energy Excess. In MicroBooNE it would be identified with an event signature of one detached photon shower and some number of proton tracks associated with a reconstructed neutrino vertex. This poster will describe the analysis developed to search for NC ∆ radiative events in MicroBooNE, consisting of a Boosted Decision Tree based event selection wit h enhanced background reduction using a Semantic Segmentation Network to target the dominant NC π0 background.

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A Flexible Trigger for SBND

The Short Baseline Neutrino (SBN) program at Fermilab will search for neutrino oscillations occurring over a 600m baseline with sensitivity to Δm2 of order 1eV2. The Short Baseline Near Detector (SBND) will serve as the near detector for the SBN program and will enable a sensitive test of the light sterile neutrino hypothesis, aiming either at an unambiguous discovery or a 5σ exclusion of the area of 3+1 oscillation parameter space allowed by the LSND and MiniBooNE anomalies.

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A Search for Anomalous Muon Neutrino Disappearance and Tau Neutrino Appearance using NOvA Near Detector

The NOvA experiment consists of two finely segmented, liquid scintillator detectors operating 14.6 mrad off-axis from the NuMI muon-neutrino beam. The Near Detector (ND) is located in the Fermilab campus, 1 km from the NuMI target, while the Far Detector (FD) is located at Ash River, MN, 810 km from the NuMI target. The NOvA experiment is primarily designed to measure electron-neutrino appearance at the FD using the ND to control systematic uncertainties. However, the ND is well suited to search for anomalous short-baseline (SBL) oscillations. The standard three-flavor neutrino oscillations have well explained by a wide range of neutrino experiments. The anomalous results, such as electron-antineutrino excess seen by LSND and MiniBooNE do not fit the three-flavor paradigm. This can be explained by an additional fourth flavor sterile neutrino at a larger mass scale than the existing three flavor neutrinos. This thesis presents a novel method for selecting tau neutrino interact ions with high purity at the ND using a convolutional neural network (CNN). Using this method, the preliminary sensitivities of anomalous SBL muon-neutrino disappearance and tau-neutrino appearance due to sterile neutrino oscillations in NOvA are shown.

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A 300 kA Pulsed Power Supply for LBNF

The Long Baseline Neutrino Facility (LBNF) will produce the worlds most intense neutrino beam. Three series connected magnetic horns will require 5kV, 300kA, 800$\mu$s pulses at a rate of 0.7Hz to focus the beam. Fermilab has designed and built pulsed high current supplies for horns in the past. Pulsed currents of 205 kA for Neutrinos at Main Injector (NuMI - NOvA), focusing a 120 GeV beam, and 170 kA for Booster Neutrino Beam (BNB - MiniBooNE) for focusing an 8 GeV beam have been operational for about 18 years. While the magnetic horns are expected to be replaced, the LBNF horn power supply is expected to last the lifetime of the project, 30 years. A resonant, half sine wave pulser was used for NuMI and BNB and has many practical advantages. The system has impedance limited fault currents by design, albeit large ones. That along with the resonant behavior means that thyristor switches are well suited. Many fault modes of the system have also been calculated. Several circuit changes were incorporated so that a single fault can be tolerated. The supply must be reversable to enable both neutrinos and anti-neutrinos to be produced. These reversal requirements required a significant amount of mechanical design and consideration to allow for quick reversal of all the semiconductors.

43 PARTICLE ACCELERATORS↗

A Search for Sterile-Neutrino-Based Muon Neutrino Disappearance Using the MicroBooNe Deep Learning Analysis

We describe a search for νµ disappearance using the MicroBooNE Deep Learning analysis 1µ1p selection. Presently, the unexplained MiniBooNE and LSND anomalies could be explained by a sterile neutrino impacting neutrino oscillations. Our analysis searches for the allowed parameter space that could describe such a sterile neutrino. We determine the allowed and excluded region of a 3+1 sterile-based muon neutrino disappearance model in MicroBooNE at 90% confidence. Our allowed region includes both the null model, and current global best fit model. Context for the underlying Deep Learning analysis is provided and several validation studies surrounding both the disappearance search, and originating 1µ1p selection are performed to strengthen confidence in the result. In addition, a next-generation deep learning tool for cosmic-ray-muon event discrimination is proposed and evaluated, demonstrating a removal of 70% of the remaining event background when added to current methods, under the cut criteria used.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

A Search for Sterile-Neutrino-Based Muon Neutrino Disappearance Using the MicroBooNe Deep Learning Analysis

We describe a search for νµ disappearance using the MicroBooNE Deep Learning analysis 1µ1p selection. Presently, the unexplained MiniBooNE and LSND anomalies could be explained by a sterile neutrino impacting neutrino oscillations. Our analysis searches for the allowed parameter space that could describe such a sterile neutrino. We determine the allowed and excluded region of a 3+1 sterile-based muon neutrino disappearance model in MicroBooNE at 90% confidence. Our allowed region includes both the null model, and current global best fit model. Context for the underlying Deep Learning analysis is provided and several validation studies surrounding both the disappearance search, and originating 1µ1p selection are performed to strengthen confidence in the result. In addition, a next-generation deep learning tool for cosmic-ray-muon event discrimination is proposed and evaluated, demonstrating a removal of 70% of the remaining event background when added to current methods, under the cut criteria used.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DarkNews: a Python-based event generator for heavy neutral lepton production in neutrino-nucleus scattering

We introduce DarkNews, a lightweight Python-based Monte-Carlo generator for beyond-the-Standard-Model neutrino-nucleus scattering. The generator handles the production and decay of heavy neutral leptons via additional vector or scalar mediators, as well as through transition magnetic moments. DarkNews samples pre-computed neutrino-nucleus upscattering cross sections and heavy neutrino decay rates to produce dilepton and single-photon events in accelerator neutrino experiments. We present two case studies with differential distributions for models that can explain the MiniBooNE excess. The aim of this code is to aid the neutrino theory and experimental communities in performing searches and sensitivity studies for new particles produced in neutrino upscattering.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Testing Explanations of Short Baseline Neutrino Anomalies

The experimental observation of neutrino oscillations profoundly impacted the physics of neutrinos, from being well understood theoretically to requiring new physics beyond the standard model of particle physics. Indeed, the mystery of neutrino masses implies the presence of new particles never observed before, often called sterile neutrinos, as they would not undergo standard weak interactions. And while neutrino oscillation measurements entered the precision era, reaching percent-level precision, many experimental results show significant discrepancies with the standard model, at baselines much shorter than typical oscillation baselines, like LSND, MiniBooNE, gallium experiments, and reactor antineutrino measurements. These short baseline anomalies could be explained by the addition of a light sterile neutrino, with mass in the $1-10~\text{eV}$ range, however, in strong tension with many null experimental observations. Other explanations that rely on sterile neutrinos with masses in the $1-500~\text{MeV}$ could resolve the tension. Here we test both classes of models. On the one hand, we look for datasets collected at a short baseline which can constrain heavy sterile neutrino models. We find that the minimal model is fully constrained, but several extensions of this model could weaken the current constraint and be tested with current and future datasets. On the other hand, we test the presence of neutrino oscillations at short baselines, induced by a light sterile state, with the data collected by the MicroBooNE experiment, a liquid argon time projection chamber specifically designed to resolve the details of each neutrino interaction. We report null results from both analyses, further constraining the space of possible explanations for the short baseline anomalies. If new physics lies behind the short baseline anomaly puzzle, it is definitely not described by a simple model.

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Measurement of $\nu_{\mu}$CC1$\pi^0$ cross-section on heavy nuclei in MINERvA

MINERvA is a neutrino-nucleus scattering experiment that investigates the effects of the nuclear medium on neutrino interactions on different targets in the energy range of the few-GeV. One of the motivations of those measurements is to provide a solid understanding of cross-sections in that energy regime that can be useful for the physics program of the current and future neutrino oscillation experiments. Because of the different interaction types in the few-GeV regime, MINERvA specializes in studying interactions with several final state production channels. Previous measurements of single-$\pi^0$ production on hydrocarbon using a neutrino beam with a mean energy of 3 GeV have shown remarkable discrepancies between the predicted and the observed cross-sections in the low-$Q^2$ regime. Such results were consistent with similar measurements from the MiniBooNE and MINOS experiments, both made of carbon-based detectors. This thesis will describe the results of the novel analysis of neu trino-induced single-$\pi^0$ production on lead and iron using a neutrino beam with a mean energy of 6 GeV. This thesis work aims to investigate similar suppression effects at low momentum transfer and offer a glance at those effects in a heavy nuclei medium.

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Search for Low mass Higgs Portal Scalars at the MicroBooNE Detector in the NuMI Beam

The MicroBooNE experiment exploits Liquid Argon Time Projection Chamber (LArTPC) technology to detect neutrinos from two beams at Fermilab, Chicago: on-axis Booster Neutrino Beam (BNB) and the off-axis Neutrinos at the Main Injector (NuMI) beam. While the aim of the experiment is to study the low-energy excess of electron neutrino events observed by the MiniBooNE experiment, to search for eV-scale sterile neutrinos and to characterise neutrino interactions on argon, it could also be exploited to probe Beyond Standard Model (BSM) Physics. This thesis presents a BSM search for low-mass Higgs Portal Scalars at MicroBooNE in the NuMI beam using data collected during the Run 1 and Run 3 data-taking periods with a total exposure corresponding to $7.01 \times 10^{20}$ protons on target (POT). The Higgs Portal Model is an extension to the Standard Model in which a dark-sector scalar, $S$, mixes with the Higgs boson with mixing angle $\theta$ and acquires a coupling to the Standard Model fermions via the Higgs Yukawa coupling. We search for low-mass Higgs Portal scalars, in the mass range $100-200$ MeV, at MicroBooNE via the production channel $K \rightarrow \pi + S$, where scalars are produced from kaons decaying at rest in the NuMI target and beam dump, as well as kaons decaying in flight in the decay pipe of the NuMI beam. In the MicroBooNE detector, we search for the decay channel $S \rightarrow e^+e^-$, which is the only decay channel available to the scalars in the mass range of our search. The results are expressed as limits, at the 95\% confidence level, on the scalar-Higgs mixing angle $\theta$ for scalars in the mass range $100-200$ MeV.Date of Award1 Aug 2023Original languageEnglishAwarding InstitutionThe University of ManchesterSupervisorStefan Soldner-Rembold (Supervisor) & Justin Evans (Supervisor)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Identifying Neutrino Final States and Energies in MicroBooNE with New Deep-Learning Based LArTPC Reconstruction Frameworks

MicroBooNE, a Liquid Argon Time Projection Chamber (LArTPC) located in the $\nu_{\mu}$-dominated Booster Neutrino Beam at Fermilab, has been studying $\nu_{e}$ charged-current (CC) interaction rates to shed light on the MiniBooNE low energy excess. The LArTPC technology employed by MicroBooNE provides the capability to image neutrino interactions with mm-scale precision. Computer vision and other machine learning techniques are promising tools for image processing that could boost efficiencies for selecting $\nu_{e}$-CC and other rare signals, reduce cosmic and beam-induced backgrounds, and improve the reconstruction of neutrino energies. The MicroBooNE experiment has been at the forefront of developing and testing such techniques for use in physics analyses. In this poster we overview deep-learning based reconstruction methods. We will showcase the use of a recurrent neural network to estimate neutrino energies and present a new reconstruction framework that uses convolutional neural networks to locate neutrino interaction vertices, tag pixels with track and shower labels, and perform particle identification on reconstructed clusters. We will present studies characterizing the performance of these new tools and demonstrate their effectiveness through their use in an inclusive $\nu_{e}$-CC event selection.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Dark Photon Search at the Short-Baseline Near Detector

Neutrino physics has long been a key field in elementary particle physics, both enhancing our understanding of the Standard Model (SM) and raising new questions. Among these are the so-called "Short-Baseline Anomalies" observed by neutrino experiments, particularly the MiniBooNE experiment at Fermilab, which detected an excess of low-energy electron-like events. In recent years, beyond Standard Model (BSM) explanations have been proposed to address this anomaly, with a focus on neutrino beam-related processes. A novel interpretation involving a dark-sector explanation was recently suggested, introducing a vector portal that connects the SM and dark sectors through a new interaction mediated by a bosonic particle, the Dark Photon. This work investigates the production of dark-sector particles, specifically Dark Photons, in the Booster Neutrino Beam (BNB) at Fermilab and their potential detection at the Short-Baseline Near Detector (SBND). The BNB produces mesons which decay into Dark Photons, detectable via their decay into electron-positron pairs at SBND. By exploiting the temporal structure of the neutrino beam, we propose a method to isolate Dark Photon signals from neutrino backgrounds using time-delayed event detection. In this thesis, the sensitivity of SBND to Dark Photons is assessed using a three-year exposure, demonstrating that SBND has the potential to significantly improve current experimental constraints on Dark Photons. This analysis provides a promising avenue for future dark sector searches in neutrino experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Towards An Updated Simulation of the Booster Neutrino Beam

Precise, accurate neutrino flux predictions for neutrino beam experiments are crucial for physics results. Flux predictions for the Booster Neutrino Beam at Fermilab were first published in 2009 by the MiniBooNE collaboration. It is no longer possible to run the simulation used to create these predictions due to outdated software versions. In this exciting period for the Short-Baseline Neutrino Program at Fermilab, with both the far detector (ICARUS) and the near detector (SBND) operating, an updated flux model for the BNB is necessary. For the purpose of using a dynamic, up-to-date beam model, the SBN program has created a new simulation named G4BNB. This framework contains new features, such as a full neutrino ancestry to handle hadron production systematics with more precision, and the inclusion of neutral mesons from the proton-beryllium scatter to allow the study of exotic BSM scenarios.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

A search for Heavy Neutral Leptons with masses up to 150 MeV and studies on scintillation light detection in SBND

Introduction Neutrino physics is one of today’s most promising fields in particle physics. The discovery of neutrino oscillations is an evidence of their mass not being null. However, the Standard Model of elementary particles (SM) does not contain a mechanism that generates them, so we have to search beyond the SM to find answers. Other unknowns remain open, such as the amount of CP symmetry violated in the lepton sector that could help explain the differences between matter and antimatter that formed the present universe. Nor is it understood why these particles are so light, more than six orders of magnitude below the next fermion, the electron. A possible explanation is the existence of heavier neutrino mass states usually referred as heavy neutral leptons (HNLs). One of the most advanced technologies for detecting neutrino interactions is the liquid argon time projection chamber (LArTPC). LArTPC detectors form a 3D image of the ionized electrons in the interaction, producing a detailed description of each neutrino event. Argon is also a prolific scintillator (40k photons/MeV) with light signals indicating the interaction time. The Short-Baseline Near Detector (SBND), a 112-active mass LArTPC at Fermilab, will measure millions of neutrino interactions from the Booster Neutrino Beam (BNB). As the near (L=110 m) detector of the Short-Baseline neutrino program, SBND will search for a fourth light neutrino (∼ eV) in the coming years. This hypothesis could explain the anomalous data observed in the LSND and MiniBooNE experiments.

Álvarez Garrote, Rodrigo [Madrid U.] (ORCID:000000↗