Enhancing Sensitivity to Physics beyond the Standard Model and Detector Performance Monitoring in HEP Experiments with Machine Learning

A special issue of Universe (ISSN 2218-1997). This special issue belongs to the section "High Energy Nuclear and Particle Physics".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 4658

Special Issue Editors

Department of Agricultural Sciences, University of Naples Federico II, 80055 Portici, Italy
Interests: high-energy particle physics; flavor physics; top quark physics; Belle and Belle II experiments; CMS experiment; high-energy calorimetry; accelerator physics; Monte Carlo simulations; statistical methods in data analysis
Department of Physics, University of Rome “G. Marconi”, Via Plinio, 44, 00193 Roma RM, Italy
Interests: Higgs physics; detector physics; Monte Carlo simulations; statistical methods in data analysis

Special Issue Information

Dear colleagues,

Enhancing sensitivity to physics beyond the Standard Model and detector performance monitoring in HEP experiments with Machine Learning

A major aim of experimental high-energy physics (HEP) is to find rare signals of new particles produced in large numbers of collisions or to look for deviations from Standard Model predictions.  

In such experiments, a challenging but essential aspect of data processing is to construct a complete physical model starting from measurements from different subdetectors. Moreover, the activities connected to detector operation and monitoring of its parameters can be a long and tedious task that is subject to human errors.

Neural networks have been used in HEP for a long time; however, recent developments in computer science have given rise to new sets of machine learning algorithms that, in many circumstances, out-perform more conventional algorithms. Recent research in high-energy physics indicates that deep neural networks can extract more information from low-level features in comparison to features created by experienced human analysts. Recently, attention has focused on deep learning to ensure a powerful and fully automatized discrimination of the backgrounds as well as to tackle the increase in detector resolutions and data rates in HEP experiments, also using an approach to process data inherited from computer vision, such as semantic segmentation and image captioning.

This Special Issue will collect contributions on the use of deep learning algorithms for complex physics analyses or enhance the sensitivity of searches for physics beyond the Standard Model. Contributions on machine learning approaches to detector operation and monitoring are also welcome.

Prof. Dr. Mario Merola
Prof. Dr. Sabino Meola
Guest Editors

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Keywords

  • HEP experiments
  • Physics beyond SM
  • Machine learning
  • Computer vision
  • Fast simulation
  • Big data analysis
  • Detector physics

Published Papers (2 papers)

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Research

16 pages, 2851 KiB  
Article
Possibilities of Detecting Light Dark Matter Produced via Drell-Yan Channel in a Fixed Target Experiment
by Eduard Ursov, Anna Anokhina, Emil Khalikov, Ivan Vidulin and Tatiana Roganova
Universe 2021, 7(2), 33; https://0-doi-org.brum.beds.ac.uk/10.3390/universe7020033 - 01 Feb 2021
Viewed by 2139
Abstract
This work presents the complete modeling scheme of production and detection of two types of light dark matter (LDM)—Dirac fermionic and scalar particles—in a fixed target experiment using SHiP experiment as an example. The Drell-Yan process was chosen as a channel of LDM [...] Read more.
This work presents the complete modeling scheme of production and detection of two types of light dark matter (LDM)—Dirac fermionic and scalar particles—in a fixed target experiment using SHiP experiment as an example. The Drell-Yan process was chosen as a channel of LDM production; the deep inelastic scattering on lead nuclei was simulated and analyzed in the detector; the production of secondary particles was modeled with the aid of PYTHIA6 toolkit. Obtained observable parameters of secondary particles produced in events associated with LDM were compared with the background neutrino events that were simulated using GENIE toolkit. The yield of LDM events was calculated with various model parameter values. Using machine learning methods, a classifier that is able to distinguish LDM events from neutrino background events based on the observed parameters with high precision has been developed. Full article
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15 pages, 527 KiB  
Article
Machine Learning Using Rapidity-Mass Matrices for Event Classification Problems in HEP
by Sergei V. Chekanov
Universe 2021, 7(1), 19; https://0-doi-org.brum.beds.ac.uk/10.3390/universe7010019 - 19 Jan 2021
Cited by 3 | Viewed by 1902
Abstract
In this work, supervised artificial neural networks (ANN) with rapidity–mass matrix (RMM) inputs are studied using several Monte Carlo event samples for various pp collision processes. The study shows the usability of this approach for general event classification problems. The proposed standardization [...] Read more.
In this work, supervised artificial neural networks (ANN) with rapidity–mass matrix (RMM) inputs are studied using several Monte Carlo event samples for various pp collision processes. The study shows the usability of this approach for general event classification problems. The proposed standardization of the ANN feature space can simplify searches for signatures of new physics at the Large Hadron Collider (LHC) when using machine learning techniques. In particular, we illustrate how to improve signal-over-background ratios in the search for new physics, how to filter out Standard Model events for model-agnostic searches, and how to separate gluon and quark jets for Standard Model measurements. Full article
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