Decay-aware neural network for event classification in collider physics

TL;DR

This paper introduces a decay-aware neural network utilizing multi-task learning to improve event classification in collider physics, effectively incorporating particle decay knowledge to enhance accuracy.

Contribution

The novel decay-aware neural network leverages auxiliary tasks to embed domain knowledge, significantly boosting classification performance over traditional models.

Findings

Improved classification accuracy with the decay-aware model.

Successful integration of decay domain knowledge via multi-task learning.

Outperformed boosted decision trees and simple neural networks.

Abstract

The goal of event classification in collider physics is to distinguish signal events of interest from background events to the extent possible to search for new phenomena in nature. We propose a decay-aware neural network based on a multi-task learning technique to effectively address this event classification. The proposed model is designed to learn the domain knowledge of particle decays as an auxiliary task, which is a novel approach to improving learning efficiency in the event classification. Our experiments using simulation data confirmed that an inductive bias was successfully introduced by adding the auxiliary task, and significant improvements in the event classification were achieved compared with boosted decision tree and simple multi-layer perceptron models.