Title: Graph Neural Networks for Photon Searches with the Underground Muon Detector of the Pierre Auger Observatory Show Chinese title

Title: 地下缪子探测器的光子搜索图神经网络

Abstract: Ultra-high-energy photons have long been sought as tracers of the most energetic processes in the Universe. Several sources can contribute to a diffuse photon flux, including interactions of cosmic rays with Galactic matter and radiation fields, as well as more exotic scenarios such as the decay of super-heavy dark matter. Regardless of their origin, the expected flux is extremely low, making direct detection impractical and thereby requiring indirect detection by extensive ground-based detector arrays. In this contribution, we present a novel method for photon-hadron discrimination in the energy range of $50$ to $300\,\text{PeV}$ based on deep learning algorithms. Our approach relies on information from both the Surface Detector (SD) and the Underground Muon Detector (UMD) of the Pierre Auger Observatory. The SD consists of an array of water-Cherenkov detectors. It is used to measure the electromagnetic and muonic components of extensive air showers at ground level. Meanwhile, the UMD is composed of buried scintillator modules. It is sensitive to air-shower muons with energies above ${\sim}1\,\text{GeV}$, enhancing the identification of muon-poor air showers as initiated by photon primaries. Our method represents air-shower events as graphs, and consequently, the network architecture is composed of graph attention layers. We assess the performance of the method on a data subset and discuss the implications of unblinding the full current dataset, as well as the prospects of the increasing data volume expected in the coming years, particularly in terms of sensitivity to various diffuse fluxes from theoretical predictions. Show Chinese abstract

Abstract: 超高能光子长期以来一直被用作宇宙中最剧烈过程的示踪剂。 多个来源可以对弥散光子通量做出贡献，包括宇宙射线与银河物质和辐射场的相互作用，以及更奇特的情况，如超重暗物质的衰变。 无论其起源如何，预期的通量都非常低，使得直接探测不切实际，因此需要通过广泛的地面探测器阵列进行间接探测。 在本篇贡献中，我们提出了一种基于深度学习算法的方法，在 $50$ 到 $300\,\text{PeV}$ 能量范围内的光子-强子区分。 我们的方法依赖于皮埃尔·奥加大气簇射观测站（Pierre Auger Observatory）的地面探测器（SD）和地下μ子探测器（UMD）的信息。 SD 包含一个水契伦科夫探测器阵列，用于测量地面上广延大气簇射的电磁成分和μ子成分。 同时，UMD 由埋藏的闪烁模块组成，对能量高于 ${\sim}1\,\text{GeV}$ 的空气簇射μ子敏感，从而增强了对由光子初级粒子引发的μ子贫乏空气簇射的识别。 我们的方法将空气簇射事件表示为图，因此网络架构由图注意力层组成。 我们在数据子集上评估了该方法的性能，并讨论了对完整当前数据集去盲的影响，以及未来几年预期的数据量增加的前景，特别是在对各种来自理论预测的弥散通量的灵敏度方面。