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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	The European physical journal. ST, Special topics Ročník 233; číslo 15-16; s. 2421 - 2424
Hlavní autoři:	Bhattacherjee, Biplob, Mukherjee, Swagata
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2024 Springer Nature B.V
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	1951-6355 1951-6401
DOI:	10.1140/epjs/s11734-024-01364-3