Xenon Signal Denoising via Supervised, Semi-Supervised, and Unsupervised Models
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911549685235712 |
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| author | Parker, Grant Kendrick Brodsky, Jason Chakraborty, Indra |
| author_facet | Parker, Grant Kendrick Brodsky, Jason Chakraborty, Indra |
| contents | This study presents a denoising algorithm trained using machine learning to improve the energy resolution of a single-phase liquid xenon time projection chamber for neutrinoless double beta decay detection. Supervised, unsupervised, and semi-supervised models are demonstrated to significantly remove noise from simulated measurements while preserving signal information. The supervised model achieves an energy resolution of $<1\%$, while the semi-supervised models achieve energy resolutions of $\sim 1\%$, and the unsupervised model performance is $\sim 1.5\%$. This work is evidence that machine learning denoising can improve energy resolution compared to traditional algorithms, even when experimentalists lack perfect a priori knowledge of the signals. Such models provide a realistic path toward next-generation sensitivity in $0νββ$ searches. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_27005 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Xenon Signal Denoising via Supervised, Semi-Supervised, and Unsupervised Models Parker, Grant Kendrick Brodsky, Jason Chakraborty, Indra Instrumentation and Detectors High Energy Physics - Experiment Nuclear Experiment This study presents a denoising algorithm trained using machine learning to improve the energy resolution of a single-phase liquid xenon time projection chamber for neutrinoless double beta decay detection. Supervised, unsupervised, and semi-supervised models are demonstrated to significantly remove noise from simulated measurements while preserving signal information. The supervised model achieves an energy resolution of $<1\%$, while the semi-supervised models achieve energy resolutions of $\sim 1\%$, and the unsupervised model performance is $\sim 1.5\%$. This work is evidence that machine learning denoising can improve energy resolution compared to traditional algorithms, even when experimentalists lack perfect a priori knowledge of the signals. Such models provide a realistic path toward next-generation sensitivity in $0νββ$ searches. |
| title | Xenon Signal Denoising via Supervised, Semi-Supervised, and Unsupervised Models |
| topic | Instrumentation and Detectors High Energy Physics - Experiment Nuclear Experiment |
| url | https://arxiv.org/abs/2603.27005 |