Adversarial methods to reduce simulation bias in neutrino interaction event filtering at Liquid Argon Time Projection Chambers

TL;DR

This paper introduces a neural network approach using 3D Submanifold Sparse Convolutional Networks and Domain Adversarial Neural Networks to improve neutrino interaction detection in Liquid Argon TPCs, reducing cosmic background bias effectively.

Contribution

It presents a novel application of sparse convolutional networks combined with adversarial training to reduce simulation bias in neutrino event filtering.

Findings

Cosmic background reduced by up to 76.3%

Neutrino interaction efficiency over 98.9%

DANNs effectively mitigate simulation biases

Abstract

For current and future neutrino oscillation experiments using large Liquid Argon Time Projection Chambers (LAr-TPCs), a key challenge is identifying neutrino interactions from the pervading cosmic-ray background. Rejection of such background is often possible using traditional cut-based selections, but this typically requires the prior use of computationally expensive reconstruction algorithms. This work demonstrates an alternative approach of using a 3D Submanifold Sparse Convolutional Network trained on low-level information from the scintillation light signal of interactions inside LAr-TPCs. This technique is applied to example simulations from ICARUS, the far detector of the Short Baseline Neutrino (SBN) program at Fermilab. The results of the network, show that cosmic background is reduced by up to 76.3% whilst neutrino interaction selection efficiency remains over 98.9%. We further present a way to mitigate potential biases from imperfect input simulations by applying Domain Adversarial Neural Networks (DANNs), for which modified simulated samples are introduced to imitate real data and a small portion of them are used for adverserial training. A series of mock-data studies are performed and demonstrate the effectiveness of using DANNs to mitigate biases, showing neutrino interaction selection efficiency performances significantly better than that achieved without the adversarial training.