Assessment of few-hits machine learning classification algorithms for low energy physics in liquid argon detectors
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866909132930416640 |
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| author | Moretti, Roberto Rossi, Marco Biassoni, Matteo Giachero, Andrea Grossi, Michele Guffanti, Daniele Labranca, Danilo Terranova, Francesco Vallecorsa, Sofia |
| author_facet | Moretti, Roberto Rossi, Marco Biassoni, Matteo Giachero, Andrea Grossi, Michele Guffanti, Daniele Labranca, Danilo Terranova, Francesco Vallecorsa, Sofia |
| contents | The physics potential of massive liquid argon TPCs in the low-energy regime is still to be fully reaped because few-hits events encode information that can hardly be exploited by conventional classification algorithms. Machine learning (ML) techniques give their best in these types of classification problems. In this paper, we evaluate their performance against conventional (deterministic) algorithms. We demonstrate that both Convolutional Neural Networks (CNN) and Transformer-Encoder methods outperform deterministic algorithms in one of the most challenging classification problems of low-energy physics (single- versus double-beta events). We discuss the advantages and pitfalls of Transformer-Encoder methods versus CNN and employ these methods to optimize the detector parameters, with an emphasis on the DUNE Phase II detectors ("Module of Opportunity"). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_09744 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Assessment of few-hits machine learning classification algorithms for low energy physics in liquid argon detectors Moretti, Roberto Rossi, Marco Biassoni, Matteo Giachero, Andrea Grossi, Michele Guffanti, Daniele Labranca, Danilo Terranova, Francesco Vallecorsa, Sofia Instrumentation and Detectors Machine Learning Data Analysis, Statistics and Probability The physics potential of massive liquid argon TPCs in the low-energy regime is still to be fully reaped because few-hits events encode information that can hardly be exploited by conventional classification algorithms. Machine learning (ML) techniques give their best in these types of classification problems. In this paper, we evaluate their performance against conventional (deterministic) algorithms. We demonstrate that both Convolutional Neural Networks (CNN) and Transformer-Encoder methods outperform deterministic algorithms in one of the most challenging classification problems of low-energy physics (single- versus double-beta events). We discuss the advantages and pitfalls of Transformer-Encoder methods versus CNN and employ these methods to optimize the detector parameters, with an emphasis on the DUNE Phase II detectors ("Module of Opportunity"). |
| title | Assessment of few-hits machine learning classification algorithms for low energy physics in liquid argon detectors |
| topic | Instrumentation and Detectors Machine Learning Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2305.09744 |