论文标题
一种用于在荧光望远镜数据中选择类似轨道事件的神经网络方法
A Neural Network Approach for Selecting Track-like Events in Fluorescence Telescope Data
论文作者
论文摘要
在2016 - 2017年，TUS是世界上第一个测试通过其在地球夜间大气中荧光辐射来注册超高能量宇宙射线（UHECR）的可能性的实验。自2019年以来，俄罗斯 - 意大利荧光望远镜（FT）Mini-Euso（“紫外线气氛”）一直在ISS上运行。计划于2023年使用FT进行ft进行ft进行平流层实验EUSO-SPB2。我们展示了如何有效地使用简单的卷积神经网络，以在使用此类仪器获得的各种数据中找到类似轨道的事件。
In 2016-2017, TUS, the world's first experiment for testing the possibility of registering ultra-high energy cosmic rays (UHECRs) by their fluorescent radiation in the night atmosphere of Earth was carried out. Since 2019, the Russian-Italian fluorescence telescope (FT) Mini-EUSO ("UV Atmosphere") has been operating on the ISS. The stratospheric experiment EUSO-SPB2, which will employ an FT for registering UHECRs, is planned for 2023. We show how a simple convolutional neural network can be effectively used to find track-like events in the variety of data obtained with such instruments.
