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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Bibliographic Details
Published in:	The European physical journal. ST, Special topics Vol. 233; no. 15-16; pp. 2425 - 2463
Main Authors:	Choudhury, Arghya, Mondal, Arpita, Sarkar, Subhadeep
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2024 Springer Nature B.V
Subjects:	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 Access:	Get full text
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