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Dr Saul Alonso Monsalve (ETH Zurich)6/25/24, 8:50 AM
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Teng Li (Shandong University)6/25/24, 9:05 AM
The Jiangmen Underground Neutrino Observatory (JUNO) is a next-generation neutrino experiment currently under construction in southern China. It is designed with a 20 kton liquid scintillator detector and 78% photomultiplier tube (PMT) coverage. The primary physics goal of JUNO is to determine the neutrino mass ordering and measure oscillation parameters with unprecedented precision. JUNO’s...
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Guihong Huang (Wuyi University)6/25/24, 9:40 AM
The Jiangmen Underground Neutrino Observatory (JUNO) is a state-of-the-art 20 kton liquid scintillator detector designed to achieve an unprecedented energy resolution of 3% @ 1 MeV. The energy resolution is of paramount importance for the measurement of neutrino mass ordering (NMO) through the study of reactor neutrinos at JUNO. A key factor contributing to the energy resolution in JUNO is the...
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Arsenii Gavrikov (INFN-Padova + The University of Padova)6/25/24, 10:05 AM
The Jiangmen Underground Neutrino Observatory (JUNO) is a neutrino experiment under construction with a broad physics program. The main goals of JUNO are the determination of the neutrino mass ordering and the high-precision measurement of neutrino oscillation properties with anti-neutrinos produced in commercial nuclear reactors. JUNO's central detector is an acrylic sphere 35.4 meters in...
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Dr Feng Gao (Université libre de Bruxelles (ULB))6/25/24, 11:00 AM
The Jiangmen Underground Neutrino Observatory (JUNO) is a next-generation large (20 kton) liquid-scintillator neutrino detector, designed to determine the neutrino mass ordering from its precise reactor neutrino spectrum measurement. Additionally, high-energy (GeV-level) atmospheric neutrino measurements could also improve its sensitivity to mass ordering via matter effects on oscillations,...
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Wing Yan Ma (Shandong University, China)6/25/24, 11:25 AM
The Jiangmen Underground Neutrino Observation (JUNO), located at Southern China, is a multi-purpose neutrino experiment that consist of a 20-kton liquid scintillator detector. The primary goal of the experiment is to measure the neutrino mass ordering (NMO) and measure the relevant oscillation parameters to a high precision. Atmospheric neutrinos are sensitive to NMO via matter effects and can...
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38. Deep learning approaches for fast event reconstruction in the SNO+ scintillator phase and beyondCal Hewitt (University of Oxford), Mark Anderson (Queen's University)6/25/24, 11:50 AM
SNO+ is an operational multi-purpose neutrino detector located 2km underground at SNOLAB in Sudbury, Ontario, Canada. 780 tonnes of linear alkylbenzene-based liquid scintillator are observed by ~9300 photomultiplier tubes (PMTs) mounted outside the spherical scintillator volume. SNO+ has a broad physics program which will include a search for the neutrinoless double beta decay of...
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Matthew Rosenberg (Tufts University)6/25/24, 1:35 PM
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...
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Luis Mora Lepin6/25/24, 2:00 PM
We present a first search for dark-trident scattering in a neutrino beam using a data set taken with the MicroBooNE detector at Fermilab. Proton interactions in the neutrino target at the Main Injector produce neutral mesons, which could decay into dark-matter (DM) particles mediated via a dark photon A′. A convolutional neural network (CNN) is trained to identify interactions of the DM...
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Polina Abratenko (Tufts University)6/25/24, 2:25 PM
MicroBooNE is a short baseline neutrino oscillation experiment that employs a Liquid Argon Time Projection Chamber (LArTPC) together with an array of Photomultiplier Tubes (PMTs), which detect scintillation light. This light detection is necessary for providing a means to reject cosmic ray background and trigger on beam-related interactions. Thus, accurate modeling of the expected optical...
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Omar Alterkait (Tufts University / IAIFI)6/25/24, 2:55 PM
Training neural networks for analyzing three-dimensional trajectories in particle detectors presents challenges due to the high combinatorial complexity of the data. Incorporating networks with Euclidean Equivariance could significantly reduce the reliance on data augmentation. To achieve Euclidean Equivariance, we construct neural networks that primarily represent data and perform...
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Dae Heun Koh (SLAC), Drielsma Francois (SLAC)6/25/24, 3:25 PM
The ICARUS T600 detector is a liquid argon time projection chamber (LArTPC) installed at Fermilab, aimed towards a sensitive search for possible electron neutrino excess in the 200-1000 MeV region. To investigate nue appearance signals in ICARUS, a fast and accurate algorithm for selecting electron neutrino events from a background of cosmic interactions is required. We present an application...
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Daniel Carber (Colorado State University, Fort Collins)6/25/24, 4:30 PM
The ICARUS T600 Liquid Argon Time Projection Chamber (LArTPC) detector is the far detector of the Short Baseline Neutrino (SBN) Program located at Fermilab National Laboratory (FNAL). The data collection for ICARUS began in May 2021, utilizing neutrinos from the Booster Neutrino Beam (BNB) and the Neutrinos at the Main Injector off-axis beam (NuMI). The SBN Program has been designed to...
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BARBARA YAEGGY (University of Cincinnati)6/25/24, 5:05 PM
The NOvA experiment uses the ~1 MW NuMI beam from Fermilab to study neutrino oscillations: electron neutrino appearance and muon neutrino disappearance in a baseline of 810 km, with a 300-ton near detector and a 14-kiloton far detector. NOvA was the first experiment in high-energy physics to apply convolutional neural networks to classify neutrino interactions and composite particles in a...
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Jianming Bian (University of California, Irvine)6/25/24, 5:30 PM
The NOvA experiment is a long-baseline accelerator neutrino experiment utilizing Fermilab's upgraded NuMI beam. It measures the appearance of electron neutrinos and the disappearance of muon neutrinos at its Far Detector in Ash River, Minnesota. NOvA is the first neutrino experiment to use convolutional neural networks (CNNs) for event identification and reconstruction. Recently, we introduced...
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Lorena Escudero (University of Cambridge)6/26/24, 9:00 AM
Imaging is one of the main pillars of clinical protocols for cancer care that provides essential non-invasive biomarkers for detection, diagnosis and response assessment. The development of Artificial Intelligence (AI) tools, and Machine Learning (ML) in particular, have proven potential to transform the analysis of radiological images, by significantly reducing processing time, by increasing...
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Fabian Kellerer (Universitat de Valencia)6/26/24, 9:35 AM
The NEXT experiment is an international collaboration that searches for the neutrinoless double-beta decay using $^{136}\mathrm{Xe}$. It features an entirely gaseous TPC, which allows for the resolution of individual electron tracks. This opens up the possibility to employ machine learning techniques to distinguish between signal and background events based on their topological signature. In...
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Tianai Ye (Queen's University)6/26/24, 10:00 AM
The LEGEND experiment is dedicated to the search for neutrinoless double beta decay using $^{76}Ge$-enriched High Purity Germanium detectors. While LEGEND has excellent energy resolution and ultra-low background levels, noise from readout electronics can make identifying events of interest more challenging. An efficient signal denoising algorithm can further enhance LEGEND’s energy resolution,...
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Sebastian Vetter (Karlsruhe Institute of Technology)6/26/24, 11:00 AM
The XENONnT detector is a dual-phase xenon time projection chamber to search for rare low-energy events. While its main purpose is the direct detection of Dark Matter, XENONnT is also sensitive to neutrino interactions for example from solar 8B neutrinos. To fully utilize the fiducialization and background reduction capabilities of the XENONnT detector, it is important to know the exact...
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Marta Babicz (University of Zurich)6/26/24, 11:25 AM
At the forefront of investigating neutrinoless double beta decay (0νββ) using 76Ge-enriched detectors, the LEGEND experiment is driven by the quest to unravel the mysteries of neutrinos and explore physics beyond the Standard Model. In its initial phase, LEGEND-200 deploys 200 kg of germanium at INFN Gran Sasso National Laboratory, aiming for a discovery half-life sensitivity surpassing...
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Andre Scaffidi (SISSA)6/26/24, 12:00 PM
This talk presents a novel approach to dark matter direct detection using anomaly-aware machine learning techniques in the DARWIN next-generation dark matter direct detection experiment. I will introduce a semi-unsupervised deep learning pipeline that falls under the umbrella of generalized Simulation-Based Inference (SBI), an approach that allows one to effectively learn likelihoods straight...
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Alessandro Schwemmer (Technical University of Munich, Germany)6/26/24, 1:35 PM
The Karlsruhe Tritium Neutrino (KATRIN) experiment probes the effective electron anti-neutrino mass by precisely measuring the tritium beta-decay spectrum close to its kinematic endpoint.
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A world-leading upper limit of $0.8 \,$eV$\,$c$^{-2}$ (90$\,$\% CL) has been set with the first two measurement campaigns.
Subsequent improvements in operational conditions and a substantial increase in... -
Francois Drielsma (SLAC)6/26/24, 2:10 PM
Recent leaps in Computer Vision (CV), made possible by Machine Learning (ML), have motivated a new approach to the analysis of particle imaging detector data. Unlike previous efforts which tackled isolated CV tasks, this talk introduces an end-to-end, ML-based data reconstruction chain for Liquid Argon Time Projection Chambers (LArTPCs), the state-of-the-art in precision imaging at the...
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Jessie Micallef (Institute for AI and Fundamental Interactions (MIT & Tufts))6/26/24, 2:45 PM
The DUNE near detector is employing new technologies in Liquid Argon Time Projection Chamber (LArTPC) detection methods, including a 3D charge pixel readout, and is modularized into a 5x7 rectangular grid of TPCs. A smaller 2x2 prototype is nearing testing in the NuMI neutrino beam at Fermilab and we are faced with reconstructing the modularized, 3D LArTPC images. While a chain of machine...
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Yeon-jae Jwa (SLAC)6/26/24, 3:10 PM
The ICARUS detector, situated on the Fermilab beamline as the Far Detector of the SBN (Short Baseline Neutrino) program, is the first large-scale operating LArTPC (Liquid Argon Time Projection Chamber). The mm-scale spatial resolution and precise timing of LArTPC enable voxelized 3D event reconstruction with high precision. A scalable deep-learning (DL)-based event reconstruction framework for...
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Zeviel Imani (Tufts University / IAIFI)6/26/24, 3:35 PM
Generating simulation data for future and current LArTPC experiments requires addressing several challenges, such as reducing computation time and the expression of detector model uncertainties. Inspired by the success of recently developed generative models to produce complex, high-dimensional data such as natural images, we are exploring how these methods might be applied to LArTPCs. Initial...
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Radi Radev (CERN)6/26/24, 4:30 PM
Deep generative models have entered the mainstream with transformer-based chatbots and diffusion-based image generation models. We showcase the utility of such models with several studies focused on different aspects of neutrino physics.
Firstly, we present a method based on flow matching suitable for cross-section measurements in the precision era of neutrino physics. Using flow matching,...
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Brinden Carlson (University of Florida)6/26/24, 5:05 PM
The Short-Baseline Near Detector (SBND) is a 100-ton scale Liquid Argon Time Projection Chamber (LArTPC) neutrino detector positioned in the Booster Neutrino Beam (BNB) at Fermilab, as part of the Short-Baseline Neutrino (SBN) program. Recent inroads in Computer Vision (CV) and Machine Learning (ML) have motivated a new approach to the analysis of particle imaging detector data. SBND data can...
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Kazuhiro Terao (SLAC)6/26/24, 5:40 PM
Large-scale public datasets have been always a key to accelerate research and enable new discoveries in the machine learning research community. We propose to build a public dataset repository for the experimental neutrino physics community, which will be a new machine learning research hub that connects researchers from multiple domains including neutrino physics, machine learning, and more. ...
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Maria Brigida Brunetti (University of Warwick)6/27/24, 9:00 AM
The Deep Underground Neutrino Experiment (DUNE) will measure all long-baseline neutrino oscillation parameters, including the CP-violating phase and the neutrino mass ordering, in one single experiment. DUNE will also study astrophysical neutrinos and perform a broad range of new physics searches. This ambitious programme is enabled by the very high-resolution imaging capabilities of...
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Isobel Mawby (Lancaster University)6/27/24, 9:25 AM
One of the primary oscillation physics goals of the Deep Underground Neutrino Experiment (DUNE) far detector (FD) is the measurement of CP violation in the neutrino sector. To achieve this, DUNE plans to employ large-scale liquid-argon time-projection chamber technology to capture neutrino interactions in unprecedented detail. Such fine-grain images demand a highly sophisticated automated...
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Giuseppe Cerati (Fermilab)6/27/24, 9:50 AM
Neutrino experiments are set to probe some of the most important open questions in physics, from CP violation and the nature of dark matter. The technology of choice for many of these experiments is the liquid argon time projection chamber (LArTPC). In current LArTPC experiments, reconstruction performance often represents a limiting factor for the sensitivity. New developments are therefore...
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Adam Aurisano (University of Cincinnati)6/27/24, 11:00 AM
The highly detailed images produced by liquid argon time projection chamber (LArTPC) technology hold the promise of an unprecedented window into neutrino interactions; however, traditional reconstruction techniques struggle to efficiently use all available information. This is especially true for complicated interactions produced by tau neutrinos, which are typically large, consist of many...
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Andrew Chappell (University of Warwick)6/27/24, 11:25 AM
The Deep Underground Neutrino Experiment will operate large-scale Liquid-Argon Time-Projection Chambers at the far site in South Dakota, producing high-resolution images of neutrino interactions. Extracting the maximum value from the images requires sophisticated pattern-recognition to interpret detector signals as physically meaningful objects for physics analyses. Identifying the neutrino...
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Roberto Moretti (INFN - Sezione di Milano Bicocca)6/27/24, 11:50 AM
Deep Learning (DL) techniques for background event rejection in Liquid Argon Time Projection Chambers (LArTPCs) have been extensively studied for various physics channels [1,2], yielding promising results. However, the potential of massive LArTPCs in the low-energy regime remains to be fully exploited, particularly in the classification of few-hits events that encode information hardly...
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Alex Wilkinson (University College London)6/27/24, 1:25 PM
A key challenge in the application of deep learning to neutrino experiments is overcoming the discrepancy between data and Monte Carlo simulation used in training. In order to mitigate bias when deep learning models are used as part of an analysis they must be made robust to mismodelling of the detector simulation. We demonstrate that contrastive learning can be applied as a pre-training step...
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Christine Quach (LLR - CNRS)6/27/24, 2:00 PM
Hyper-Kamiokande (HK) is the next generation neutrino observatory in Japan and the successor of Super-Kamiokande (SK) detector. It has been designed to extend the legacy of its predecessor into new realms of neutrino physics ranging from MeV (Solar or Supernovae neutrino) to several GeV energy scales, and in particular, discover CP violation for the very first time in the lepton sector. To...
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Jorge Prado González (KM3NeT)6/27/24, 2:35 PM
KM3NeT/ARCA and KM3NeT/ORCA are the new generation of neutrino telescopes located in the depths of the Mediterranean Sea. Each comprises a grid of optical sensors that capture the Cherenkov light emitted by charged particles produced in neutrino interactions. KM3NeT/ARCA, sensitive to interactions with energies ranging from TeV to PeV, focuses on cosmic neutrinos, while KM3NeT/ORCA...
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Anaelle Chalumeau (LPNHE)6/27/24, 3:00 PM
The T2K near detector ND280 is currently being upgraded to prepare the second phase of data taking T2K-II, with the purpose of confirming at 3σ level if CP symmetry is conserved or violated in the neutrino oscillations. For this upgrade, new detectors are currently being installed including so-called High-Angle Time Projection Chambers. They are instrumented with Encapsulated Resistive...
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Mayeul Aubin (ETH Zurich)6/27/24, 3:25 PM
Deep learning methods are becoming indispensable in the data analysis of particle physics experiments, with current neutrino studies demonstrating their superiority over traditional tools in various domains, particularly in identifying particles produced by neutrino interactions and fitting their trajectories. This talk will showcase a comprehensive reconstruction strategy of the neutrino...
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Roger Huang (Lawrence Berkeley National Laboratory)6/27/24, 4:30 PM
The choice of unfolding method for a cross-section measurement is tightly coupled to the model dependence of the efficiency correction and the overall impact of cross-section modeling uncertainties in the analysis. A key issue is the dimensionality used, as the kinematics of all outgoing particles in an event typically affects the reconstruction performance in a neutrino detector. OmniFold is...
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Nicola Fulvio Calabria (Dipartimento Interateneo di Fisica "M. Merlin", Politecnico di Bari)6/27/24, 4:55 PM
In this preliminary study we consider and explore the application of Machine Learning algorithms for reconstruction in Super-Kamiokande. To do so simulated event samples have been used. The aim is the development of a tool to be employed in proton decay analysis along with the official reconstruction software (fiTQun). The final goal will be to improve Cherenkov ring detection and...
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Garrett Wendel (Penn State University)6/27/24, 5:20 PM
Building upon the LiquidO detection paradigm, the CLOUD detector represents a significant evolution in neutrino detection, offering rich capabilities in capturing both spatial and temporal information of low-energy particle interactions. With a 5–10-ton opaque scintillator inner detector volume, CLOUD is the byproduct of the EIC/UKRI funded AntiMatter-OTech project, whose main objective is to...
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Patrick Tsang (SLAC)6/28/24, 9:00 AM
Modeling the light propagation in LArTPC with sinusoidal representation networks (SIREN) is scalable and capable of being calibrated using data.
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In this talk, I will demonstrate a few applications of the SIREN in position reconstruction and charge-light signal correlation. -
Pierre Granger (APC/CNRS)6/28/24, 9:35 AM
Liquid argon time projection chambers (LArTPCs) are highly attractive for particle detection because of their tracking resolution and calorimetric reconstruction capabilities. Developing high-quality simulators for such detectors is very challenging because conventional approaches to describe different detector parameters or processes ignore their entanglement (ie, calibrations are done one at...
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Yifan Chen (SLAC)6/28/24, 10:10 AM
The fidelity of detector simulation is crucial for precision experiments, such as DUNE which uses liquid argon time projection chambers (LArTPCs). Conventional calibration approaches usually tackle individual detector processes and require careful tuning of the calibration procedure to mitigate the impact from elsewhere. We have previously shown a successful demonstration of differentiable...
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Junjie Xia (IPMU)6/28/24, 11:15 AM
The water Cherenkov detector stands as a cornerstone in numerous physics programs such as nucleon decay search and precise neutrino measurements. Over recent decades, many such detectors have achieved groundbreaking discoveries, with preparations underway for the next generation of advancements. However, like in all other experiments, accurately quantifying detector systematic uncertainties...
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César Jesús-Valls (Kavli IPMU, University of Tokyo)6/28/24, 11:40 AM
Drawing statistical conclusions out of experimental data entails to compare it to the physics model predictions of interest. In modern particle physics experiments, producing predictions often requires of three subtasks: 1) Simulating the particles interactions and propagation within the detector, 2) Describing the detector response and tuning its description to calibration data and 3)...
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Jiri Franc (Czech Technical University in Prague)6/28/24, 12:05 PM
In high energy physics, the detection of rare events and the computation of their properties require precise and reliable statistical methods, with uncertainty quantification playing a crucial role. Nowadays, most research relies on machine learning methods, where the calibration of output probabilities is not always straightforward. How can we then draw conclusions with the required five...
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Beata Kowal (University of Wroclaw)6/28/24, 12:30 PM
We shall review the results of our recent work on the developments of the NuWro Monte Carlo generator of events. We are working on applying deep learning techniques to optimize the NuWro generator. In the first step, we work on the neural network model that generates the lepton-nucleus cross-sections. We obtained a deep neural network model that predicts the electron-carbon cross-section over...
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Daniel Douglas (SLAC National Accelerator Laboratory)6/28/24, 12:55 PM
With the rising importance of large sequential models in neutrino imaging and reconstruction tasks, a robust estimation of uncertainty at each stage of reconstruction is essential. These chained models produce valuable physical representations of the evolution of a neutrino interaction, which are of interest to many disparate fields of study. This talk will discuss methods available for...
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Kazuhiro Terao (SLAC)6/28/24, 1:20 PM
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