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arXiv 2402.08457astro-ph.COastro-ph.HEastro-ph.IM
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SRG/eROSITA全天巡天:eRASS1星系团宇宙学的X射线选择函数模型

The SRG/eROSITA All-Sky Survey: X-ray selection function models for the eRASS1 galaxy cluster cosmology

N. Clerc, J. Comparat, R. Seppi, E. Artis, Y. E. Bahar, F. Balzer, E. Bulbul, T. Dauser, C. Garrel, V. Ghirardini, S. Grandis, C. Kirsch, M. Kluge, A. Liu, F. P… 展开作者

N. Clerc, J. Comparat, R. Seppi, E. Artis, Y. E. Bahar, F. Balzer, E. Bulbul, T. Dauser, C. Garrel, V. Ghirardini, S. Grandis, C. Kirsch, M. Kluge, A. Liu, F. Pacaud, M. E. Ramos-Ceja, T. H. Reiprich, J. S. Sanders, J. Wilms, X. Zhang

AI总结:

针对eRASS1星系团样本,构建基于表面亮度及全局特征的X射线选择函数模型,并验证其有效性,以支持宇宙学参数约束。

摘要:

从巡天源目录中刻画星系团种群需要对样本不完整性(即选择函数)有所了解。eROSITA搭载于Spectrum Roentgen Gamma(SRG)卫星上进行的首次全天巡天(eRASS1),使得在西部银河半球软X射线波段探测到大量星系团样本成为可能。其核心目标在于约束宇宙学参数,这对选择函数模型的准确性、灵活性和可解释性提出了严格要求。我们使用eRASS1巡天的大量模拟观测数据,并以与真实eRASS1事件完全相同的方式处理模拟数据。我们将探测到的源与模拟星系团进行匹配,并将探测结果关联到星系团的内在属性。我们训练了一系列模型来构建仅依赖于可观测表面亮度数据的选择函数。我们还开发了第二系列模型,这些模型依赖于星系团的整体特征,如X射线光度、通量、预期仪器计数率以及形态学特性。我们使用模拟数据验证了模型,并根据选定的性能指标对其进行排序。我们还使用通过X射线和Sunyaev-Zeldovich效应探测到的星系团数据集对模型进行了验证。我们展示了为此目的开发的完整贝叶斯种群建模框架。我们的结果揭示了在eRASS1样本中与星系团选择最相关的表面亮度特征,特别是中心表面亮度在仪器分辨率尺度上的模糊作用。我们生成了一系列用户友好的选择函数模型,并展示了它们的有效性和局限性。我们的亮源选择函数很好地再现了与外部数据集的目录匹配结果。(删节版)

英文摘要:

Characterising galaxy cluster populations from catalog of sources selected in astronomical surveys requires knowledge of sample incompleteness, known as selection function. The first All-Sky Survey (eRASS1) by eROSITA onboard Spectrum Roentgen Gamma (SRG) has enabled the collection of large samples of galaxy clusters detected in the soft X-ray band over the Western Galactic hemisphere. The driving goal consists in constraining cosmological parameters, which puts stringent requirements on accuracy, flexibility and explainability of the selection function models. We use a large set of mock observations of the eRASS1 survey and we process simulated data identically to the real eRASS1 events. We match detected sources to simulated clusters and we associate detections to intrinsic cluster properties. We train a series of models to build selection functions depending only on observable surface brightness data. We develop a second series of models relying on global cluster characteristics such as X-ray luminosity, flux, and expected instrumental count-rate as well as on morphological properties. We validate our models using our simulations and we rank them according to selected performance metrics. We validate the models with datasets of clusters detected in X-rays and via the Sunyaev-Zeldovich effect. We present the complete Bayesian population modelling framework developed for this purpose. Our results reveal the surface brightness characteristics most relevant to cluster selection in the eRASS1 sample, in particular the ambiguous role of central surface brightness at the scale of the instrument resolution. We have produced a series of user-friendly selection function models and demonstrated their validity and their limitations. Our selection function for bright sources reproduces well the catalog matches with external datasets. (abridged)

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