标题： RadioSED I：从非均匀调查中进行射电SED的贝叶斯推断 显示英文标题

标题： RadioSED I: Bayesian inference of radio SEDs from inhomogeneous surveys

摘要： 我们在这里介绍RadioSED，这是一个贝叶斯推断框架，专门用于仅使用公开发布的大面积调查数据来建模和分类宽频带射电光谱能量分布（SED）。 我们概述了RadioSED的功能，其重点在于宽频带射电辐射，这些辐射可以追踪活动星系核（AGN）的射电喷流和星系际介质中的千秒差距尺度吸收。 特别是，我们讨论了RadioSED在推进对最年轻和最紧凑源中AGN物理和组成的理解方面的能力，对于这些源来说，高分辨率成像通常不可用。 这些年轻的射电AGN通常表现为峰值谱（PS）源，在RadioSED出现之前，由于通常需要大范围的宽频带频率覆盖，因此难以识别，但它们为理解AGN演化和反馈提供了无价的环境。 我们讨论了RadioSED的实现细节，并通过合成数据和观测数据验证了我们的方法。 由于所使用的调查数据来自多个观测时期，我们还考虑了RadioSED输出在AGN变化性的背景下。 最后，我们展示了RadioSED能够恢复文献中一些已知射电源的预期SED形状，并讨论了使用射电SED拟合作为起点进一步研究这些和其他源的途径。 该框架的可扩展性和模块化使其成为多波长天文学家的令人兴奋的工具，随着下一代望远镜开始进行多项全天调查。 因此，我们将用Python编写的RadioSED代码放在Github上。 显示英文摘要

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.