Neutrino Physics and Machine Learning 2024

Session

Day 2 - Afternoon

Jun 26, 2024, 1:35 PM

HCI J4 (ETH Zurich)

Description

KATRIN and ML based end-to-end data reconstruction in SBN and DUNE-ND: LArTPCs with high pile-ups

33. Latest results from the KATRIN experiment and insights into the neural network approach

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.
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...

57. End-to-End, Machine-Learning-Based Data Reconstruction Chain for LArTPC detectors

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...

18. One Neural Network, Two Detector Mediums, Three Detection Regions: Multi-detector Machine Learning with DUNE’s Near Detector Prototype

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...

53. Michel Electron Reconstruction Using a Novel Deep-Learning-Based Multi-Level Event Reconstruction in ICARUS

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...

29. Generative Modeling for LArTPC Images

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...

4. Deep Generative Models for Neutrino Physics

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,...

48. Machine-Learning-Based Data Reconstruction Chain for the Short Baseline Near Detector

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...

46. Public Data Challenge

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. ...