Machine learning driven reconstruction of cosmic-ray air showers for next generation radio arrays
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911048349515776 |
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| author | Koundal, Paras |
| author_facet | Koundal, Paras |
| contents | Surface radio antenna-based measurements of cosmic-ray air showers present significant computational challenges in accurately reconstructing physics observables, in particular, the depth of shower maximum, X$_{max}$. State-of-the-art template fitting methods rely on extensive simulation libraries, limiting scalability. This work introduces a technique utilizing graph neural networks to reconstruct key air-shower parameters, in particular, direction and shower-core, energy, and X$_{max}$. For training and testing of the networks, we use a CoREAS simulation library made for a future enhancement of IceCube's surface array with radio antennas. The neural networks provide a scalable framework for large-scale data analysis for next-generation astroparticle observatories, such as IceCube-Gen2. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_07219 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine learning driven reconstruction of cosmic-ray air showers for next generation radio arrays Koundal, Paras High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics Surface radio antenna-based measurements of cosmic-ray air showers present significant computational challenges in accurately reconstructing physics observables, in particular, the depth of shower maximum, X$_{max}$. State-of-the-art template fitting methods rely on extensive simulation libraries, limiting scalability. This work introduces a technique utilizing graph neural networks to reconstruct key air-shower parameters, in particular, direction and shower-core, energy, and X$_{max}$. For training and testing of the networks, we use a CoREAS simulation library made for a future enhancement of IceCube's surface array with radio antennas. The neural networks provide a scalable framework for large-scale data analysis for next-generation astroparticle observatories, such as IceCube-Gen2. |
| title | Machine learning driven reconstruction of cosmic-ray air showers for next generation radio arrays |
| topic | High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2507.07219 |