Using Machine Learning Techniques to Characterize Dark Matter from Mono-X signals at the LHC

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Using Machine Learning Techniques to Characterize Dark Matter from Mono-X signals at the LHC

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Dnr:

NAISS 2023/22-305

Type:

NAISS Small Compute

Principal Investigator:

Yong Sheng Koay

Affiliation:

Uppsala universitet

Start Date:

2023-03-10

End Date:

2024-04-01

Primary Classification:

10301: Subatomic Physics

Webpage:

Allocation

Rackham at UPPMAX: 2 x 1000 core-h/month
Snowy at UPPMAX: 2 x 1000 core-h/month

Abstract

We will study the prospects of characterising dark matter at the large hadron colliders using machine learning techniques. We will constrain ourselves within the framework of simplified t-channel dark matter models. If mono-X signals (ie. mono-jet, mono-photon, mono-z, mono-higgs) are observed, we want to be able to identify the underlying dark matter candidate that can best explain the signal.