Deep learning for the R-parity violating supersymmetry searches at the LHC
Jun Guo, Jinmian Li, Tianjun Li, Fangzhou Xu, Wenxing Zhang

TL;DR
This paper develops a deep neural network approach, specifically a CNN using jet images, to improve the detection of neutralino jets in R-parity violating supersymmetry searches at the LHC, outperforming traditional methods.
Contribution
It introduces a CNN-based jet tagging method that enhances neutralino jet identification and applies it to recast ATLAS data, improving gluino mass exclusion limits.
Findings
CNN outperforms N-subjettiness in jet tagging efficiency
Combining CNN output with jet mass improves detection across neutralino masses
Exclusion limits on gluino mass increased by approximately 200 GeV
Abstract
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. When applied to pure jet samples, such a CNN outperforms the N-subjettiness variable by a factor of a few in tagging efficiency. Moreover, we find the method, which combines the CNN output and jet invariant mass, can perform better and is applicable to a wider range of neutralino mass than the CNN alone. Finally, the ATLAS…
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