Modern machine learning and particle physics: an in-depth review

Modern machine learning (ML) techniques are ubiquitous in the field of particle physics. These ML models are primarily meant for exploiting large amounts of high-dimensional data to reduce complexity and extract as much information as possible from data. This special issue presents a series of ten c...

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Veröffentlicht in:The European physical journal. ST, Special topics Jg. 233; H. 15-16; S. 2421 - 2424
Hauptverfasser: Bhattacherjee, Biplob, Mukherjee, Swagata
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2024
Springer Nature B.V
Schlagworte:
Algorithms
Atomic
Classical and Continuum Physics
Condensed Matter Physics
Decision trees
Editorial
Experiments
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
Particle physics
Physics
Physics and Astronomy
Quarks
Sensors
Simulation
ISSN:1951-6355, 1951-6401
Online-Zugang:Volltext
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Zusammenfassung:Modern machine learning (ML) techniques are ubiquitous in the field of particle physics. These ML models are primarily meant for exploiting large amounts of high-dimensional data to reduce complexity and extract as much information as possible from data. This special issue presents a series of ten contributions in the area of application of modern ML techniques in theoretical and experimental particle physics.
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ISSN:1951-6355
1951-6401
DOI:10.1140/epjs/s11734-024-01364-3