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Abstract <p>We address the challenge of reconstructing the energy of three ultrahigh-energy cosmic rays registered with a small fluorescence telescope EUSO-TA that operated in 2015 at the site of the Telescope Array experiment in Utah, United States. Each of these events was recorded within one time frame. Conventional methods of energy reconstruction are not applicable in this case because the events do not have light curves but a single data point. As an alternative, we consider a number of approaches based on artificial neural networks. We demonstrate that a signal recorded by a fluorescence telescope within one time frame might be enough to reconstruct the energy of a primary particle with reasonable accuracy using an ensemble of simple convolutional neural networks. Contrary to the conventional approach, reconstruction of the shower geometry is not needed for this. More than this, preliminary estimates can be obtained even without recognizing the shower track. However, there remain some problems that do not allow us to claim that the suggested method is universal and always works. We discuss difficulties that we faced and possible ways of improving the method.</p>

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Reconstruction of Energy of Ultrahigh-Energy Cosmic Rays Registered with a Fluorescence Telescope: One Time Frame Might Be Enough

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • M. Yu. Zotov,
  • A. A. Trusov

摘要

Abstract

We address the challenge of reconstructing the energy of three ultrahigh-energy cosmic rays registered with a small fluorescence telescope EUSO-TA that operated in 2015 at the site of the Telescope Array experiment in Utah, United States. Each of these events was recorded within one time frame. Conventional methods of energy reconstruction are not applicable in this case because the events do not have light curves but a single data point. As an alternative, we consider a number of approaches based on artificial neural networks. We demonstrate that a signal recorded by a fluorescence telescope within one time frame might be enough to reconstruct the energy of a primary particle with reasonable accuracy using an ensemble of simple convolutional neural networks. Contrary to the conventional approach, reconstruction of the shower geometry is not needed for this. More than this, preliminary estimates can be obtained even without recognizing the shower track. However, there remain some problems that do not allow us to claim that the suggested method is universal and always works. We discuss difficulties that we faced and possible ways of improving the method.