Searches for the BSM scenarios at the LHC using decision tree-based machine learning algorithms: a comparative study and review of random forest, AdaBoost, XGBoost and LightGBM frameworks

Machine learning algorithms are now being extensively used in our daily lives, spanning across diverse industries as well as academia. In the field of high energy physics (HEP), the most common and challenging task is separating a rare signal from a much larger background. The boosted decision tree...

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Veröffentlicht in:The European physical journal. ST, Special topics Jg. 233; H. 15-16; S. 2425 - 2463
Hauptverfasser: Choudhury, Arghya, Mondal, Arpita, Sarkar, Subhadeep
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2024
Springer Nature B.V
Schlagworte:
Algorithms
Artificial intelligence
Atomic
Classical and Continuum Physics
Classification
Comparative studies
Condensed Matter Physics
Data analysis
Datasets
Decision trees
Deep learning
High energy physics
Large Hadron Collider
Luminosity
Machine learning
Materials Science
Measurement Science and Instrumentation
Modern Machine Learning and Particle Physics: An In-Depth Review
Molecular
Neural networks
Optical and Plasma Physics
Physics
Physics and Astronomy
Regular Article
Supersymmetry
ISSN:1951-6355, 1951-6401
Online-Zugang:Volltext
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Zusammenfassung:Machine learning algorithms are now being extensively used in our daily lives, spanning across diverse industries as well as academia. In the field of high energy physics (HEP), the most common and challenging task is separating a rare signal from a much larger background. The boosted decision tree algorithm has been a cornerstone of the high energy physics for analyzing event triggering, particle identification, jet tagging, object reconstruction, event classification, and other related tasks for quite some time. This article presents a comprehensive overview of research conducted by both HEP experimental and phenomenological groups that utilize decision tree algorithms in the context of the standard model and supersymmetry (SUSY). We also summarize the basic concept of machine learning and decision tree algorithm along with the working principle of random forest, AdaBoost and two gradient boosting frameworks, such as XGBoost and LightGBM. Using a case study of electroweakino production at the high-luminosity LHC, we demonstrate how these algorithms lead to improvement in the search sensitivity compared to traditional cut-based methods in both compressed and non-compressed R-parity conserving SUSY scenarios. The effect of different hyperparameters and their optimization, and feature importance study using SHapley values are discussed in detail.
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ISSN:1951-6355
1951-6401
DOI:10.1140/epjs/s11734-024-01308-x