Deep learning for the R-parity violating supersymmetry searches at the LHC

Understand

Supersymmetry with hadronic R-parity violation in which the lightest neutralino decays into three quarks is still weakly constrained.

• This work aims to further improve the current search for this scenario by the boosted decision tree method with additional information from jet substructure.
• In particular, we find a deep neural network turns out to perform well in characterizing the neutralino jet substructure.
• We first construct a Convolutional Neutral Network (CNN) which is capable of tagging the neutralino jet in any signal process by using the idea of jet image.