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Applied Sciences
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Machine Learning Applications in Atlas and CMS Experiments at LHC
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Machine Learning Applications in Atlas and CMS Experiments at LHC

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 6621

Special Issue Editors

Dr. Francesco Conventi

E-Mail Website
Guest Editor
Universitá degli Studi di Napoli “Parthenope” and National Institute for Nuclear Physics (INFN), Naples, Italy
Interests: high-energy physics; data analysis; machine learning
Dr. Orso Iorio

E-Mail Website
Guest Editor
Universitá degli Studi di Napoli “Federico II” and and National Institute for Nuclear Physics (INFN), Naples, Italy
Interests: high-energy physics; data analysis; machine learning

Special Issue Information

Dear Colleagues,

A major aim of the physics program of the Atlas and CMS experiments is to search for signs of new physics in an immense number of collisions at CERN’s Large Hadron Collider, either by finding rare signals of new particles produced among overwhelming amounts of background-originated collisions or by looking for deviations from Standard Model predictions small enough to have escaped previous detection attempts. The data collected by the LHC experiments are high-dimensional and complex, and the complexity is growing with the increase of LHC performance. The increasingly challenging experimental conditions of LHC also demand continuous advancements in reconstruction techniques and in noise rejection strategies at all levels of data taking.

Thus, the physics reach of the experiments is strongly dependent on the physics performance of the algorithms and on the quality of data.

Neural networks have been used in HEP for a long time; however, the introduction of deep learning has led to a new generation of machine learning algorithms that, in many circumstances, outperform more conventional ones.

This Special Issue focuses on the latest research and development in machine learning application in Atlas and CMS experiments at LHC applied in the context of improving the final analysis selection, object reconstruction, object calibration, object identification, triggering, simulation, and automation.

Prof. Dr. Francesco Conventi
Dr. Orso Iorio
Guest Editors

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Keywords

  • deep learning
  • LHC
  • atlas
  • CMS
  • machine learning
  • high-energy physics
  • statistical analysis

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Published Papers (3 papers)

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Review

12 pages, 1749 KiB  
Review
VBF Event Classification with Recurrent Neural Networks at ATLAS’s LHC Experiment
by Silvia Auricchio, Francesco Cirotto and Antonio Giannini
Appl. Sci. 2023, 13(5), 3282; https://doi.org/10.3390/app13053282 - 4 Mar 2023
Cited by 1 | Viewed by 1609
Abstract
A novel machine learning (ML) approach based on a recurrent neural network (RNN) for event topology identification in high energy physics (HEP) is presented. The vector-boson fusion (VBF) production mechanism arising in proton-to-proton collisions is predicted both from the current theoretical model of [...] Read more.
A novel machine learning (ML) approach based on a recurrent neural network (RNN) for event topology identification in high energy physics (HEP) is presented. The vector-boson fusion (VBF) production mechanism arising in proton-to-proton collisions is predicted both from the current theoretical model of the particle physics, the standard model, and from its extensions that foresee potential new physics phenomena. This physical process has a well-defined event topology in the final state and a distinctive detector signature. In this work, an ML approach based on the RNN architecture is developed to deal with hadronic-only event information in order to enhance the acceptance of this production mechanism in physics analysis of the data. This technique was applied to a physics analysis in the context of high-mass diboson resonance searches using data collected by the ATLAS experiment. Full article
(This article belongs to the Special Issue Machine Learning Applications in Atlas and CMS Experiments at LHC)
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Figure 1

11 pages, 581 KiB  
Review
Machine-Learning Application for a Likelihood Ratio Estimation Problem at LHC
by Silvia Auricchio, Francesco Cirotto and Antonio Giannini
Appl. Sci. 2023, 13(1), 86; https://doi.org/10.3390/app13010086 - 21 Dec 2022
Cited by 1 | Viewed by 1193
Abstract
High-energy physics is now entering a new era. Current particle experiments, such as ATLAS at the Large Hadron Collider at CERN, offer the possibility of discovering new and interesting physics’ phenomena beyond the Standard Model, the theoretical framework which describes fundamental interactions between [...] Read more.
High-energy physics is now entering a new era. Current particle experiments, such as ATLAS at the Large Hadron Collider at CERN, offer the possibility of discovering new and interesting physics’ phenomena beyond the Standard Model, the theoretical framework which describes fundamental interactions between particles. In this paper a machine-learning algorithm to deal with the estimate of the expected background processes distribution, a very demanding task for particle physics analyses, is described. A new technique is exploited, inspired by the well-known likelihood ratio estimation problem, called direct importance estimation in statistics. First, its theoretical formulation is discussed, then its performances in two ATLAS analyses are described. Full article
(This article belongs to the Special Issue Machine Learning Applications in Atlas and CMS Experiments at LHC)
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Figure 1

13 pages, 1393 KiB  
Review
Machine Learning Applications for Jet Tagging in the CMS Experiment
by Antimo Cagnotta, Francesco Carnevali and Agostino De Iorio
Appl. Sci. 2022, 12(20), 10574; https://doi.org/10.3390/app122010574 - 19 Oct 2022
Cited by 4 | Viewed by 2778
Abstract
The fundamental physics research at the frontier accessible by today’s particle accelerators such as the CERN Large Hadron Collider pose unique challenges in terms of complexity and abundance of data to analyse. In this context, it is of paramount importance to develop algorithms [...] Read more.
The fundamental physics research at the frontier accessible by today’s particle accelerators such as the CERN Large Hadron Collider pose unique challenges in terms of complexity and abundance of data to analyse. In this context, it is of paramount importance to develop algorithms capable of dealing with multivariate problems to enhance humans’ ability to interpret data and ultimately increase the discovery potential of the experiments. Machine learning techniques therefore assume an increasingly important role in the experiments at the LHC. In this work, we give an overview of the latest developments in this field, with a particular focus on the algorithms developed and used within the CMS Collaboration. The review follows this structure: (1) Introduction presents the CMS Experiment at LHC and the most common methods used in particle physics; (2) Jet Flavour Tagging briefly describes the main algorithms used to reconstruct heavy-flavour jets; (3) Jet Substructure and Deep Tagging focuses on the identification of heavy-particle decay in boosted jets; (4) Analysis Applications gives examples of applying the algorithm in physics analyses; and (5) Conclusions summarises the state-of-the-art and gives indications for future studies. Full article
(This article belongs to the Special Issue Machine Learning Applications in Atlas and CMS Experiments at LHC)
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