Using Machine Learning to disentangle LHC signatures of Dark Matter candidates

Abstract

We study the prospects of characterising Dark Matter at colliders using Machine Learning (ML) techniques. We focus on the monojet and missing transverse energy (MET) channel and propose a set of benchmark models for the study: a typical WIMP Dark Matter candidate in the form of a SUSY neutralino, a pseudo-Goldstone impostor in the shape of an Axion-Like Particle, and a light Dark Matter impostor whose interactions are mediated by a heavy particle. All these benchmarks are tensioned against each other, and against the main SM background ($Z$+jets). Our analysis uses both the leading-order kinematic features as well as the information of an additional hard jet. We explore different representations of the data, from a simple event data sample with values of kinematic variables fed into a Logistic Regression algorithm or a Fully Connected Neural Network, to a transformation of the data into images related to probability distributions, fed to Deep and Convolutional Neural Networks. We also study the robustness of our method against including detector effects, dropping kinematic variables, or changing the number of events per image. In the case of signals with more combinatorial possibilities (events with more than one hard jet), the most crucial data features are selected by performing a Principal Component Analysis. We compare the performance of all these methods, and find that using the 2D images of the combined information of multiple events significantly improves the discrimination performance.

21 figures, 6 tables, minor revision

References in corpus (7)

• Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC
• Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC
• FeynRules - Feynman rules made easy
• Maverick dark matter at colliders
• Solving differential equations with neural networks: Applications to the calculation of cosmological phase transitions
• A deep learning approach to cosmological dark energy models
• Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning

Cited by in corpus (9)

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• Machine Learning Techniques for Intermediate Mass Gap Lepton Partner Searches at the Large Hadron Collider
• Machine Learning Electroweakino Production
• Tagging the Higgs boson decay to bottom quarks with colour-sensitive observables and the Lund jet plane