Machine Learning
Fuente:
arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866908707105800192 |
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| author | Duarte, Javier M. Seljak, Uros Terao, Kazu |
| author_facet | Duarte, Javier M. Seljak, Uros Terao, Kazu |
| contents | This chapter gives an overview of the core concepts of machine learning (ML) -- the use of algorithms that learn from data, identify patterns, and make predictions or decisions without being explicitly programmed -- that are relevant to particle physics with some examples of applications to the energy, intensity, cosmic, and accelerator frontiers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11133 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning Duarte, Javier M. Seljak, Uros Terao, Kazu Data Analysis, Statistics and Probability High Energy Physics - Experiment High Energy Physics - Phenomenology High Energy Physics - Theory This chapter gives an overview of the core concepts of machine learning (ML) -- the use of algorithms that learn from data, identify patterns, and make predictions or decisions without being explicitly programmed -- that are relevant to particle physics with some examples of applications to the energy, intensity, cosmic, and accelerator frontiers. |
| title | Machine Learning |
| topic | Data Analysis, Statistics and Probability High Energy Physics - Experiment High Energy Physics - Phenomenology High Energy Physics - Theory |
| url | https://arxiv.org/abs/2512.11133 |