A deep learning-based reconstruction of cosmic ray-induced air showers
Erdmann M, Glombitza J, Walz D (2018)
Publication Type: Journal article, Original article
Publication year: 2018
Journal
Book Volume: 97
Pages Range: 46-53
DOI: 10.1016/j.astropartphys.2017.10.006
Abstract
We describe a method of reconstructing air showers induced by cosmic rays using deep learning techniques. We simulate an observatory consisting of ground-based particle detectors with fixed locations on a regular grid. The detector's responses to traversing shower particles are signal amplitudes as a function of time, which provide information on transverse and longitudinal shower properties. In order to take advantage of convolutional network techniques specialized in local pattern recognition, we convert all information to the image-like grid of the detectors. In this way, multiple features, such as arrival times of the first particles and optimized characterizations of time traces, are processed by the network. The reconstruction quality of the cosmic ray arrival direction turns out to be competitive with an analytic reconstruction algorithm. The reconstructed shower direction, energy and shower depth show the expected improvement in resolution for higher cosmic ray energy.
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Germany (DE)How to cite
APA:
Erdmann, M., Glombitza, J., & Walz, D. (2018). A deep learning-based reconstruction of cosmic ray-induced air showers. Astroparticle Physics, 97, 46-53. https://doi.org/10.1016/j.astropartphys.2017.10.006
MLA:
Erdmann, Martin, Jonas Glombitza, and David Walz. "A deep learning-based reconstruction of cosmic ray-induced air showers." Astroparticle Physics 97 (2018): 46-53.
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