通过深度学习驱动的超分辨率增强中微子望远镜中的事件
Enhancing Events in Neutrino Telescopes through Deep Learning-Driven Super-Resolution
AI总结：
该研究提出利用深度学习超分辨率技术，在探测器内添加虚拟光学模块并训练卷积神经网络预测击中，以改进中微子望远镜中缪子角度重建精度。
摘要：
中微子望远镜（如冰立方中微子天文台）的最新发现广泛依赖于机器学习（ML）工具，从探测到的原始光子击中中推断物理量。中微子望远镜的重建算法受到光学模块对光子稀疏采样的限制，这是由于模块之间的间距相对较大（10-100米）。在这封信中，我们提出了一种新技术，通过使用深度学习驱动的数据事件超分辨率来学习光子穿过探测器介质的传输过程。这些“改进的”事件随后可以使用传统或机器学习技术进行重建，从而获得改进的分辨率。我们的策略在现有探测器几何结构内布置额外的“虚拟”光学模块，并训练卷积神经网络来预测这些虚拟光学模块上的击中。我们展示了该技术提高了通用冰基中微子望远镜中缪子的角度重建精度。我们的结果可直接扩展到水基中微子望远镜和其他事件形态。
英文摘要：
Recent discoveries by neutrino telescopes, such as the IceCube Neutrino Observatory, relied extensively on machine learning (ML) tools to infer physical quantities from the raw photon hits detected. Neutrino telescope reconstruction algorithms are limited by the sparse sampling of photons by the optical modules due to the relatively large spacing ($10-100\,{\rm m})$ between them. In this letter, we propose a novel technique that learns photon transport through the detector medium through the use of deep learning-driven super-resolution of data events. These ``improved'' events can then be reconstructed using traditional or ML techniques, resulting in improved resolution. Our strategy arranges additional ``virtual'' optical modules within an existing detector geometry and trains a convolutional neural network to predict the hits on these virtual optical modules. We show that this technique improves the angular reconstruction of muons in a generic ice-based neutrino telescope. Our results readily extend to water-based neutrino telescopes and other event morphologies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
