标题： 使用神经网络加速天体物理对象的SED建模 显示英文标题

标题： Accelerating SED Modeling of Astrophysical Objects Using Neural Networks

摘要： 用物理驱动的模型解释天体物理对象的光谱能量分布（SED）在计算上是昂贵的。 这些模型需要在高维参数空间中求解耦合的微分方程，使得传统的拟合技术如马尔可夫链蒙特卡罗或嵌套抽样变得不可行。 一个关键的例子是模拟耀变体喷流中的非热辐射——这是活动星系核中超大质量黑洞产生的相对论性喷流，它们是宇宙中最强大的发射体之一。 为了解决这一挑战，我们采用机器学习来加速SED评估，从而实现高效的贝叶斯推断。 我们生成了一组大量电子-强子耀变体辐射模型，并训练一个神经网络（NN）来预测光子谱，同时显著减少运行时间并保持准确性。 作为概念验证，我们提出了一种基于神经网络的耀变体SED建模工具，为未来的扩展奠定了基础，并为天体物理学界提供了一个开放访问的资源。 显示英文摘要

摘要： Interpreting the spectral energy distributions (SEDs) of astrophysical objects with physically motivated models is computationally expensive. These models require solving coupled differential equations in high-dimensional parameter spaces, making traditional fitting techniques such as Markov Chain Monte Carlo or nested sampling prohibitive. A key example is modeling non-thermal emission from blazar jets - relativistic outflows from supermassive black holes in Active Galactic Nuclei that are among the most powerful emitters in the Universe. To address this challenge, we employ machine learning to accelerate SED evaluations, enabling efficient Bayesian inference. We generate a large sample of lepto-hadronic blazar emission models and train a neural network (NN) to predict the photon spectrum with strongly reduced run time while preserving accuracy. As a proof of concept, we present an NN-based tool for blazar SED modeling, laying the groundwork for future extensions and for providing an open-access resource for the astrophysics community.