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 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.11.2024
Springer Nature B.V |
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| ISSN: | 1951-6355, 1951-6401 |
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| Abstract | 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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| AbstractList | 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. |
| Author | Mukherjee, Swagata Bhattacherjee, Biplob |
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| DOI | 10.1140/epjs/s11734-024-01364-3 |
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| SubjectTerms | 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 |
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