Towards a method to anticipate dark matter signals with deep learning at the LHC

Abstract

We study several simplified dark matter (DM) models and their signatures at the LHC using neural networks. We focus on the usual monojet plus missing transverse energy channel, but to train the algorithms we organize the data in 2D histograms instead of event-by-event arrays. This results in a large performance boost to distinguish between standard model (SM) only and SM plus new physics signals. We use the kinematic monojet features as input data which allow us to describe families of models with a single data sample. We found that the neural network performance does not depend on the simulated number of background events if they are presented as a function of $S/\sqrt{B}$, where $S$ and $B$ are the number of signal and background events per histogram, respectively. This provides flexibility to the method, since testing a particular model in that case only requires knowing the new physics monojet cross section. Furthermore, we also discuss the network performance under incorrect assumptions about the true DM nature. Finally, we propose multimodel classifiers to search and identify new signals in a more general way, for the next LHC run.

52 pages, 29 figures, 3 tables. v2: Added a subsection to briefly discuss and compare event-by-event combination versus histogram or ensemble classifiers. v3: minor changes

References in corpus (15)

• 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
• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• An Introduction to PYTHIA 8.2
• Dark Matter Candidates from Particle Physics and Methods of Detection
• FeynRules - Feynman rules made easy
• Classification without labels: Learning from mixed samples in high energy physics
• Search for new phenomena in events with an energetic jet and missing transverse momentum in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Constraining Dark Sectors at Colliders: Beyond the Effective Theory Approach
• A Living Review of Machine Learning for Particle Physics
• Mapping monojet constraints onto Simplified Dark Matter Models
• Search for dark matter in association with an energetic photon in pp collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector
• Direct Detection of Dark Matter Substructure in Strong Lens Images with Convolutional Neural Networks
• A complete effective field theory for dark matter
• Dark Matter Subhalos, Strong Lensing and Machine Learning

Cited by in corpus (4)

• Boosting mono-jet searches with model-agnostic machine learning
• A method for approximating optimal statistical significances with machine-learned likelihoods
• Insights into Dark Matter Direct Detection Experiments: Decision Trees versus Deep Learning
• Machine Learning Techniques for Intermediate Mass Gap Lepton Partner Searches at the Large Hadron Collider