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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2410.13148 |
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| _version_ | 1866908674534932480 |
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| author | Yu, Felix J. Kamp, Nicholas Argüelles, Carlos A. |
| author_facet | Yu, Felix J. Kamp, Nicholas Argüelles, Carlos A. |
| contents | Neutrino telescopes detect rare interactions of particles produced in some of the most extreme environments in the Universe. This is accomplished by instrumenting a cubic-kilometer scale volume of naturally occurring transparent medium with light sensors. Given their substantial size and the high frequency of background interactions, these telescopes amass an enormous quantity of large variance, high-dimensional data. These attributes create substantial challenges for analyzing and reconstructing interactions, particularly when utilizing machine learning (ML) techniques. In this paper, we present a novel approach, called om2vec, that employs transformer-based variational autoencoders to efficiently represent the detected photon arrival time distributions of neutrino telescope events by learning compact and descriptive latent representations. We demonstrate that these latent representations offer enhanced flexibility and improved computational efficiency, thereby facilitating downstream tasks in data analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_13148 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning Efficient Representations of Neutrino Telescope Events Yu, Felix J. Kamp, Nicholas Argüelles, Carlos A. Data Analysis, Statistics and Probability Instrumentation and Methods for Astrophysics Machine Learning High Energy Physics - Experiment Neutrino telescopes detect rare interactions of particles produced in some of the most extreme environments in the Universe. This is accomplished by instrumenting a cubic-kilometer scale volume of naturally occurring transparent medium with light sensors. Given their substantial size and the high frequency of background interactions, these telescopes amass an enormous quantity of large variance, high-dimensional data. These attributes create substantial challenges for analyzing and reconstructing interactions, particularly when utilizing machine learning (ML) techniques. In this paper, we present a novel approach, called om2vec, that employs transformer-based variational autoencoders to efficiently represent the detected photon arrival time distributions of neutrino telescope events by learning compact and descriptive latent representations. We demonstrate that these latent representations offer enhanced flexibility and improved computational efficiency, thereby facilitating downstream tasks in data analysis. |
| title | Learning Efficient Representations of Neutrino Telescope Events |
| topic | Data Analysis, Statistics and Probability Instrumentation and Methods for Astrophysics Machine Learning High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2410.13148 |