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arXiv 1910.08088astro-ph.IMastro-ph.HE
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$γ$射线天文学中开放源科学工具和背景模型构造的验证

Validation of open-source science tools and background model construction in $γ$-ray astronomy

Lars Mohrmann, Andreas Specovius, Domenico Tiziani, Stefan Funk, Dmitry Malyshev, Kaori Nakashima, Christopher van Eldik

备注：
16(+6) pages, 23(+10) figures
发表信息：
A&A 632, A72 (2019)
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摘要：

在来自IACT（例如H.E.S.S.）的$γ$射线数据的经典分析中，孔径光度测定或光子计数被应用于包围源的（通常为圆形的）感兴趣区域（RoI）。分析中的一个关键因素是估计由于数据中剩余宇宙射线引起的空气簇射而导致的RoI中的背景量。在过去的几十年中，已经开发了各种标准的背景估计技术，它们中的大多数依赖于从观察视场内的无源区域测量背景。然而，特别是在银道面，大量的，有时重叠的，$γ$射线源和大规模弥漫的$γ$射线发射阻碍了源分析和背景估计。对于复杂的视野，三维（3D）的似然分析显示出潜在的是优于经典的分析。在这种分析技术中，光谱形态学模型，包括一个或多个源组件和背景组件，适合的数据，从而在一个完整的光谱和空间描述的视野。对于IACT数据的应用，这种方法的主要挑战是构建一个强大的背景模型。在这项工作中，我们将3D似然分析应用于H.E.S.S.最近公布的各种测试数据，使用开放的分析框架ctools和Gammapy。首先，我们表明，当使用这些工具在一个经典的分析方法，并比较专有的H.E.S.S.分析框架，几乎相同的高层次的分析结果。然后，我们描述了从H.E.S.S.的数据的通用背景模型的构造。观察，并证明了使用该背景模型的3D似然分析产生与从经典分析获得的结果高度兼容的高水平分析结果。（节略）

英文摘要：

In classical analyses of $γ$-ray data from IACTs, such as H.E.S.S., aperture photometry, or photon counting, is applied in a (typically circular) region of interest (RoI) encompassing the source. A key element in the analysis is to estimate the amount of background in the RoI due to residual cosmic ray-induced air showers in the data. Various standard background estimation techniques have been developed in the last decades, most of them rely on a measurement of the background from source-free regions within the observed field of view. However, in particular in the Galactic plane, source analysis and background estimation are hampered by the large number of, sometimes overlapping, $γ$-ray sources and large-scale diffuse $γ$-ray emission. For complicated fields of view, a three-dimensional (3D) likelihood analysis shows the potential to be superior to classical analysis. In this analysis technique, a spectromorphological model, consisting of one or multiple source components and a background component, is fitted to the data, resulting in a complete spectral and spatial description of the field of view. For the application to IACT data, the major challenge of such an approach is the construction of a robust background model. In this work, we apply the 3D likelihood analysis to various test data recently made public by H.E.S.S., using the open analysis frameworks ctools and Gammapy. First, we show that, when using these tools in a classical analysis approach and comparing to the proprietary H.E.S.S. analysis framework, virtually identical high-level analysis results are obtained. We then describe the construction of a generic background model from data of H.E.S.S. observations, and demonstrate that a 3D likelihood analysis using this background model yields high-level analysis results that are highly compatible with those obtained from the classical analyses. (abridged)

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