在星系的形成和演化过程中，恒星的形成和演化与AGN活动之间有着密切而错综复杂的关系。如何更好的认识这些复杂的相互关系是人们理解星系的形成和演化过程的一个关键，也是目前该领域研究中的一个重点和难点。恒星形成和演化与AGN活动都会在星系的多波段能谱中留下印记。因此，星族和AGN的电磁能谱模型是从星系的多波段观测数据反推其年龄、恒星质量、恒星形成率、黑洞质量、黑洞吸积率等基本物理参数，进而研究其相互关系的重要基础。然而，目前人们对星系中星族和AGN成分自身及其相对比例的认识都存在着较大的不确定性，从而导致能谱模型中存在大量的自由参数，并且很多参数之间简并严重。这些困难的存在对传统的能谱分析方法的可靠性提出了巨大的挑战。为此，本项目将在之前工作的基础上，系统的发展一套统一的、基于贝叶斯推理和机器学习方法的、测光和分光数据相结合的能谱分析方法，用于研究星系的星族和AGN成分的物理性质及其相互关系。

In the formation and evolution of galaxies, there are tight and intricate relationships between the formation and evolution of stars and AGN activities, which are the keys to understanding the formation and evolution of galaxies while poorly understood. Fortunately, these complex physical processes leave their imprints in the integrated multi-wavelength spectral energy distributions (SEDs) of galaxies. Therefore, the models for the SEDs of stellar population and AGN are important basis for determining the basic physical parameters, such as age, stellar mass, star formation rate, black hole mass and black hole accretion rate of galaxies, from their multi-wavelength observations. However, a lot of uncertainties remain in the SED models of stellar population and AGN, which lead to a large number of possibly highly degenerated free parameters. This presents a great challenge to the traditional method of SED analysis. Given this, based on Bayesian inference and machine learning, we will systematically develop a unified set of methods for a more comprehensive analysis of the multi-wavelength photometric and spectroscopic SEDs of galaxies. By using these methods, the physical properties of stellar population and AGN components of galaxies and the relationships between them will be better understood.