DeeLeMa: Missing information search with Deep Learning for Mass estimation

TL;DR

DeeLeMa is a deep learning network designed to analyze high-energy particle collision events with missing information, accurately estimating mass distributions despite uncertainties and detector effects.

Contribution

It introduces a novel deep learning approach tailored for analyzing complex collision events with invisible particles, leveraging kinematic constraints and symmetries.

Findings

Robust mass estimation in complex collision events

Effective handling of combinatorial uncertainties

Flexible application to various event topologies

Abstract

We introduce DeeLeMa, a deep learning-based network for the analysis of energy and momentum in high-energy particle collisions. This novel approach is specifically designed to address the challenge of analyzing collision events with multiple invisible particles, which are prevalent in many high-energy physics experiments. DeeLeMa is constructed based on the kinematic constraints and symmetry of the event topologies. We show that DeeLeMa can robustly estimate mass distribution even in the presence of combinatorial uncertainties and detector smearing effects. The approach is flexible and can be applied to various event topologies by leveraging the relevant kinematic symmetries. This work opens up exciting opportunities for the analysis of high-energy particle collision data, and we believe that DeeLeMa has the potential to become a valuable tool for the high-energy physics community.