Constraining nuclear effects in Argon using machine learning algorithms

TL;DR

This paper investigates the use of machine learning algorithms to improve neutrino energy reconstruction in Argon nuclei, aiming to better understand nuclear effects in neutrino oscillation experiments.

Contribution

It demonstrates the effectiveness of combining data from different neutrino event generators to enhance machine learning-based neutrino energy reconstruction in Argon.

Findings

MLA improves neutrino energy reconstruction accuracy.

Combining GENIE and GiBUU data enhances results.

Quantifies nuclear effects via Ar/H ratio.

Abstract

Neutrino oscillation experiments aim to measure the neutrino oscillation parameters with accuracy and achieve a complete understanding of neutrino physics. For determining the neutrino oscillation parameters, knowledge of neutrino energy is a prerequisite. But neutrino energy needs to be reconstructed, based on the particles in the final state that emerge out of the nucleus following a neutrino-nucleus interaction. Current and upcoming neutrino oscillation experiments use heavy nuclear targets (viz. Argon(Ar), Calcium(Ca), etc.) but the neutrino scattering with such targets becomes complicated as compared to that with a clean target like Hydrogen(H). This work explores the viability of using machine learning algorithms (MLA) in reconstructing neutrino energy. We use final state kinematics generated from two neutrino event generators viz. GENIE and GiBUU to train the MLA. We calculate the Ar/H ratio in an attempt to quantify nuclear effects in the Ar target. We observe a significant improvement in our results when we train the MLA by combining the FSI kinematics of neutrino interactions from both the neutrino event generators.