RadioSED I：来自非均匀巡天的射电SED的贝叶斯推断
RadioSED I: Bayesian inference of radio SEDs from inhomogeneous surveys
AI总结：
本研究提出了基于公开大天区巡天数据的贝叶斯推断框架RadioSED，可对射电SED进行建模分类，能识别难以观测的年轻峰谱AGN，经合成与观测数据验证有效，代码已开源，适配下一代全天巡天需求。
摘要：
我们在此提出RadioSED，这是一个专为宽带射电光谱能量分布（SEDs）建模与分类而定制的贝叶斯推断框架，仅使用公开发布的大天区巡天数据即可工作。我们概述了RadioSED的功能，其重点关注宽带射电辐射——这类辐射能够追踪活动星系核（AGN）的射电喷流和环星系介质中千秒差距尺度的吸收现象。我们特别讨论了RadioSED如何推动我们对最年轻、最致密源中AGN物理性质与组成的理解，这类源通常无法获得高分辨率成像观测。这些年轻射电AGN通常表现为峰谱（PS）源，在RadioSED出现之前，由于通常需要大的宽带频率覆盖范围，这类源难以识别，但它们为理解AGN演化与反馈提供了极具价值的研究环境。我们讨论了RadioSED的实现细节，并通过合成数据与观测数据验证了我们的方法。由于所使用的巡天数据来自多个观测历元，我们还结合AGN光变特性分析了RadioSED的输出结果。最后，我们展示了RadioSED能够还原文献中一批已被充分表征的射电源的预期SED形状，并讨论了以射电SED拟合为起点，对这些源及其他源开展进一步研究的方向。该框架的可扩展性与模块化特性使其成为多波段天文学家的有力工具，适用于下一代望远镜即将开展的多项全天巡天项目。因此，我们将基于Python编写的RadioSED代码在Github上公开提供。
英文摘要：
We present here RadioSED, a Bayesian inference framework tailored to modelling and classifying broadband radio spectral energy distributions (SEDs) using only data from publicly-released, large-area surveys. We outline the functionality of RadioSED, with its focus on broadband radio emissions which can trace kiloparsec-scale absorption within both the radio jets and the circumgalactic medium of Active Galactic Nuclei (AGN). In particular, we discuss the capability of RadioSED to advance our understanding of AGN physics and composition within youngest and most compact sources, for which high resolution imaging is often unavailable. These young radio AGN typically manifest as peaked spectrum (PS) sources which, before RadioSED, were difficult to identify owing to the large, broadband frequency coverage typically required, and yet they provide an invaluable environment for understanding AGN evolution and feedback. We discuss the implementation details of RadioSED, and we validate our approach against both synthetic and observational data. Since the surveys used are drawn from multiple epochs of observation, we also consider the output from RadioSED in the context of AGN variability. Finally, we show that RadioSED recovers the expected SED shapes for a selection of well-characterised radio sources from the literature, and we discuss avenues for further study of these and other sources using radio SED fitting as a starting point. The scalability and modularity of this framework make it an exciting tool for multiwavelength astronomers as next-generation telescopes begin several all-sky surveys. Accordingly, we make the code for RadioSED, which is written in Python, available on Github.
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