SBND Analysis using ML Reconstruction Chain

Abstract

As part of the Short Baseline Neutrino (SBN) Program at Fermilab, the Short Baseline Near Detector (SBND) is positioned in the Booster Neutrino Beam (BNB) and explores neutrino-argon interactions with unprecedented statistics. SBND is a Liquid Argon Time Projection Chamber (LArTPC). Electrons produced through ionization drift toward three wire planes, providing signals that form 2D images of particle trajectories. I introduce the Scalable Particle Imaging using Neural Embeddings (SPINE) framework, which employs a Machine Learning (ML)-based 3D reconstruction using a series of neural networks. Here, we present SPINE’s reconstruction chain, analysis approaches, and results from our latest simulation samples.