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

by Bear Carlson (SLAC)

Tuesday Jul 1, 2025, 12:30 PM → 1:30 PM America/Los_Angeles

48/2-224 - Madrone (SLAC)

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 therefore be reconstructed using an end-to-end, ML-based data reconstruction chain for LArTPCs. The reconstruction chain is a multi-task network cascade which combines point-level feature extraction using Sparse Convolutional Neural Networks (CNN) and particle superstructure formation using Graph Neural Networks (GNN). I will first introduce the SBN program and its physics goals. I then demonstrate the expected reconstruction performance on SBND and its role in physics measurements.

https://stanford.zoom.us/j/98973156241?pwd=cEU5RFdlVXoyc0JTeTlDMkozKzQ5UT09

SBND_SPINE_seminar5.pdf