# Anti-electron Neutrino Event Selection from Backgrounds Based on Machine Learning

## TL;DR

This paper explores the use of machine learning within a ROOT framework to improve the selection efficiency of neutrino-induced inverse beta decay events in liquid scintillator detectors for reactor neutrino experiments.

## Contribution

It introduces a machine learning approach embedded in ROOT for better event selection in liquid scintillator neutrino detectors, demonstrating improved efficiency.

## Key findings

- Higher selection efficiencies for neutrino events achieved
- Machine learning method outperforms traditional criteria
- Simulations validate the approach's effectiveness

## Abstract

For reactor neutrino experiments including the next--generation experiments will be adopting the liquid scintillator technique, criteria and time to select neutrino--induced inverse beta decay events from the background events need to be established. For higher performance efficiency, we investigated the results of applying a machine learning technique embedded in a standard ROOT package to select IBD signals. To obtain a higher statistics, the signals and background events in a gadolinium-loaded liquid scintillation detector were reproduced by Monte Carlo simulation. We report the efficiencies of neutrino--induced $n-H$ and $n-Gd$ events selection using the machine learning technique.