Top squark signal significance enhancement by different Machine Learning Algorithms

Fraga, J.; Ronald, R.A.; Jesus, S. M.; Juan, Molano; Ávila, C.

doi:10.48550/arxiv.2106.06813

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Simulation and Jet Image1

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2022

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“…On the other hand, we veto the events with an identified lepton(l = e, µ, τ ) and b-jet to reduce the backgrounds. In addition, we use the requirements of ∆φ(j 1 , / E T ) > 0.7 to reduce most of the QCD backgrounds [71,72]. The basic selections of the signal and background events are shown in Tab.…”

Section: Simulation and Jet Imagementioning

confidence: 99%

Deep Learning Jet Image as a Probe of Light Higgsino Dark Matter at the LHC

Lv,

Wang,

2022

Preprint

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Higgsino in supersymmetric standard models can play the role of dark matter particle. In conjunction with the naturalness criterion, the higgsino mass parameter is expected to be around the electroweak scale. In this work, we explore the potential of probing the nearly degenerate light higgsinos with machine learning at the LHC. By representing the jets as 2D images, we use the convolutional neural network (CNN) to enhance the signal significance. We find that our deep learning jet image method can improve the previous result based on the conventional cut-flow by about a factor of two at the High-Luminosity LHC.

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Section: Simulation and Jet Imagementioning

confidence: 99%