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 back-ground 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.
Highlights
JHEP08(2019)[055] nearly three decades [44]
We propose to represent collision events as event graphs and use the message passing neutral network (MPNN) to analyze the events
We proposed to represent a collision event in high energy physics as an event graph with a set of nodes and edges, and use the Message Passing Neural Networks to deal with the problems of discrimination of signal and background events at colliders
Summary
A collision event usually produces a number of final state objects which are reconstructed as photons, leptons and jets with four-momentum information. The message passing techniques are utilized to perform event graph embedding, which will encode the whole event graph into each node state vector. After T iterations, each resulting node state is an encoding of the whole graph, which is a compact representation of the information of both the kinematic features of all final states and the geometrical relationship between them. They are the event features that are automatically extracted from the input event graph.
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