卷积非参数伽马射线信号与背景分离
Convolutional non-parametric Gamma-Ray Signal and Background Separation
- ARC Centre of Excellence for Dark Matter Particle Physics & CSSM, Department of Physics, Adelaide University（阿德莱德大学暗物质粒子物理卓越研究中心与CSSM）
- Irfu, CEA Saclay, Université Paris-Saclay（巴黎萨克雷大学CEA萨克莱研究设施）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种基于变分自编码器集成的非参数方法，用于甚高能伽马射线信号与背景的自动分离，在多种模拟和真实数据上实现像素级重建，并具有稳定性能。
AI中文摘要：
在甚高能伽马射线天文学中，必须将信号与来自误判宇宙射线质子的残余背景分离。在先前工作的基础上，我们引入了一个变分自编码器集成，旨在以最少的假设实现自动信号-背景分离，这些假设包括信号和背景中空间与能量分布的可分离性，且无需预先指定成分数量或信号本身的点状/弥漫性质。此外，我们不假设知道坐标空间中哪个区域以背景为主，也不假设信号在视场中的预期位置。我们在解析点源混合场景、银河系中心暗物质湮灭的真实模拟以及来自公开H.E.S.S数据发布的蟹状星云和MSH 15-52的真实观测上测试了该模型。该模型在所有场景中均能在像素级别完全重建信号和背景，同时对输入进行去噪。即使在低信号-背景比下，也能获得稳定的性能和合理的误差估计。
英文摘要：
In very-high-energy gamma-ray astronomy, signals must be separated from the residual background which arises from misidentified cosmic-ray protons. Building on previous work, we introduce an ensemble of variational autoencoders that aims to perform automatic signal-background separation with minimal assumptions that include separability of the spatial and energy distributions in both the signal and background, with no prior specification of the number of components or the point-like/diffuse nature of the signal itself. In addition, we do not assume knowledge of which region of the coordinate space is background-dominated or where the signal is supposed to be located in the field of view. We test the model on an analytic point-source mixture scenario, a realistic simulation of dark matter annihilation in the Galactic centre, and real observations of the Crab nebula and MSH 15-52 from the public H.E.S.S data release. The model proves capable of completely reconstructing the signal and background at a pixel by pixel level in all scenarios, whilst also denoising the inputs. Stable performance and reasonable error estimates are obtained even for low signal-to-background ratios.
