Insights into Dark Matter Direct Detection Experiments: Decision Trees versus Deep Learning
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866910786444591104 |
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| author | Lopez-Fogliani, Daniel E. Perez, Andres D. de Austri, Roberto Ruiz |
| author_facet | Lopez-Fogliani, Daniel E. Perez, Andres D. de Austri, Roberto Ruiz |
| contents | The detection of Dark Matter (DM) remains a significant challenge in particle physics. This study exploits advanced machine learning models to improve detection capabilities of liquid xenon time projection chamber experiments, utilizing state-of-the-art transformers alongside traditional methods like Multilayer Perceptrons and Convolutional Neural Networks. We evaluate various data representations and find that simplified feature representations, particularly corrected S1 and S2 signals as well as a few shape-related features including the time difference between signals, retain critical information for classification. Our results show that while transformers offer promising performance, simpler models like XGBoost can achieve comparable results with optimal data representations. We also derive exclusion limits in the cross-section versus DM mass parameter space, showing minimal differences between XGBoost and the best performing deep learning models. The comparative analysis of different machine learning approaches provides a valuable reference for future experiments by guiding the choice of models and data representations to maximize detection capabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_10372 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Insights into Dark Matter Direct Detection Experiments: Decision Trees versus Deep Learning Lopez-Fogliani, Daniel E. Perez, Andres D. de Austri, Roberto Ruiz Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena High Energy Physics - Experiment High Energy Physics - Phenomenology Data Analysis, Statistics and Probability The detection of Dark Matter (DM) remains a significant challenge in particle physics. This study exploits advanced machine learning models to improve detection capabilities of liquid xenon time projection chamber experiments, utilizing state-of-the-art transformers alongside traditional methods like Multilayer Perceptrons and Convolutional Neural Networks. We evaluate various data representations and find that simplified feature representations, particularly corrected S1 and S2 signals as well as a few shape-related features including the time difference between signals, retain critical information for classification. Our results show that while transformers offer promising performance, simpler models like XGBoost can achieve comparable results with optimal data representations. We also derive exclusion limits in the cross-section versus DM mass parameter space, showing minimal differences between XGBoost and the best performing deep learning models. The comparative analysis of different machine learning approaches provides a valuable reference for future experiments by guiding the choice of models and data representations to maximize detection capabilities. |
| title | Insights into Dark Matter Direct Detection Experiments: Decision Trees versus Deep Learning |
| topic | Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena High Energy Physics - Experiment High Energy Physics - Phenomenology Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2406.10372 |