Probing stop pair production at the LHC with graph neural networks

TL;DR

This paper introduces a novel graph neural network approach to analyze LHC collision events, improving the detection sensitivity for stop pair production in supersymmetry searches.

Contribution

The paper presents a new method using event graphs and message passing neural networks to enhance stop detection at the LHC, outperforming traditional machine learning techniques.

Findings

MPNN effectively discriminates signal from background events.

The method extends the stop mass reach by over 100 GeV.

Graph-based analysis improves detection sensitivity.

Abstract

Top-squarks (stops) play a crucial role for the naturalness of supersymmetry (SUSY). However, searching for the stops is a tough task at the LHC. To dig the stops out of the huge LHC data, various expert-constructed kinematic variables or cutting-edge analysis techniques have been invented. In this paper, we propose to represent collision events as event graphs and use the message passing neutral network (MPNN) to analyze the events. As a proof-of-concept, we use our method in the search of the stop pair production at the LHC, and find that our MPNN can efficiently discriminate the signal and background events. In comparison with other machine learning methods (e.g. DNN), MPNN can enhance the mass reach of stop mass by several tens of GeV to over a hundred GeV.