Deep Learning and IACT: Bridging the gap between Monte-Carlo simulations and LST-1 data using domain adaptation
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916168342700032 |
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| author | Dellaiera, Michael Plard, Cyann Vuillaume, Thomas Benoit, Alexandre Caroff, Sami |
| author_facet | Dellaiera, Michael Plard, Cyann Vuillaume, Thomas Benoit, Alexandre Caroff, Sami |
| contents | The Cherenkov Telescope Array Observatory (CTAO) is the next generation of observatories employing the imaging air Cherenkov technique for the study of very high energy gamma rays. The deployment of deep learning methods for the reconstruction of physical attributes of incident particles has evinced promising outcomes when conducted on simulations. However, the transition of this approach to observational data is accompanied by challenges, as deep learning-based models are susceptible to domain shifts. In this paper, we integrate domain adaptation in the physics-based context of the CTAO and shed light on the gain in performance that these techniques bring using LST-1 real acquisitions. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_13633 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Learning and IACT: Bridging the gap between Monte-Carlo simulations and LST-1 data using domain adaptation Dellaiera, Michael Plard, Cyann Vuillaume, Thomas Benoit, Alexandre Caroff, Sami Instrumentation and Methods for Astrophysics The Cherenkov Telescope Array Observatory (CTAO) is the next generation of observatories employing the imaging air Cherenkov technique for the study of very high energy gamma rays. The deployment of deep learning methods for the reconstruction of physical attributes of incident particles has evinced promising outcomes when conducted on simulations. However, the transition of this approach to observational data is accompanied by challenges, as deep learning-based models are susceptible to domain shifts. In this paper, we integrate domain adaptation in the physics-based context of the CTAO and shed light on the gain in performance that these techniques bring using LST-1 real acquisitions. |
| title | Deep Learning and IACT: Bridging the gap between Monte-Carlo simulations and LST-1 data using domain adaptation |
| topic | Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2403.13633 |