2D Convolutional Neural Network for Event Reconstruction in IceCube DeepCore
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arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866912860233269248 |
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| author | Peterson, J. H. Rodriguez, M. Prado Hanson, K. |
| author_facet | Peterson, J. H. Rodriguez, M. Prado Hanson, K. |
| contents | IceCube DeepCore is an extension of the IceCube Neutrino Observatory designed to measure GeV scale atmospheric neutrino interactions for the purpose of neutrino oscillation studies. Distinguishing muon neutrinos from other flavors and reconstructing inelasticity are especially difficult tasks at GeV scale energies in IceCube DeepCore due to sparse instrumentation. Convolutional neural networks (CNNs) have been found to have better success at neutrino event reconstruction than conventional likelihood-based methods. In this contribution, we present a new CNN model that exploits time and depth translational symmetry in IceCube DeepCore data and present the model's performance, specifically for flavor identification and inelasticity reconstruction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_16373 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | 2D Convolutional Neural Network for Event Reconstruction in IceCube DeepCore Peterson, J. H. Rodriguez, M. Prado Hanson, K. High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics High Energy Physics - Experiment Machine Learning IceCube DeepCore is an extension of the IceCube Neutrino Observatory designed to measure GeV scale atmospheric neutrino interactions for the purpose of neutrino oscillation studies. Distinguishing muon neutrinos from other flavors and reconstructing inelasticity are especially difficult tasks at GeV scale energies in IceCube DeepCore due to sparse instrumentation. Convolutional neural networks (CNNs) have been found to have better success at neutrino event reconstruction than conventional likelihood-based methods. In this contribution, we present a new CNN model that exploits time and depth translational symmetry in IceCube DeepCore data and present the model's performance, specifically for flavor identification and inelasticity reconstruction. |
| title | 2D Convolutional Neural Network for Event Reconstruction in IceCube DeepCore |
| topic | High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics High Energy Physics - Experiment Machine Learning |
| url | https://arxiv.org/abs/2307.16373 |