A study of an energy-dependent anisotropy of cosmic rays beyond the GZK cut-off with deep neural networks. (arXiv:2105.06414v1 [astro-ph.HE])
<a href="http://arxiv.org/find/astro-ph/1/au:+Kalashev_O/0/1/0/all/0/1">Oleg Kalashev</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Pshirkov_M/0/1/0/all/0/1">Maxim Pshirkov</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Zotov_M/0/1/0/all/0/1">Mikhail Zotov</a>
In this letter, we present an update of a method for analysing arrival
directions of ultra-high-energy cosmic rays (UHECRs) above the
Greisen–Zatsepin–Kuz’min cut-off with a deep convolutional neural network
developed originally in Kalashev, Pshirkov, Zotov (2020). Namely, we introduce
energy as another variable employed in the analysis. This allows us to take
into account the intrinsic uncertainties in energy of primary cosmic rays
present in any experiment, which were not taken into account in the previous
study, without any loss of quality of the classifier. We present the
architecture of the new neural network, results of its application to mock maps
of UHECR arrival directions and outline possible directions of a further
improvement of the method.
In this letter, we present an update of a method for analysing arrival
directions of ultra-high-energy cosmic rays (UHECRs) above the
Greisen–Zatsepin–Kuz’min cut-off with a deep convolutional neural network
developed originally in Kalashev, Pshirkov, Zotov (2020). Namely, we introduce
energy as another variable employed in the analysis. This allows us to take
into account the intrinsic uncertainties in energy of primary cosmic rays
present in any experiment, which were not taken into account in the previous
study, without any loss of quality of the classifier. We present the
architecture of the new neural network, results of its application to mock maps
of UHECR arrival directions and outline possible directions of a further
improvement of the method.
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