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Probing stop pair production at the LHC with graph neural networks · Around

2018

Probing stop pair production at the LHC with graph neural networks

Abdughani, Murat, Ren, Jie, Wu, Lei et al.

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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.

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