Accelerating SED Modeling of Astrophysical Objects Using Neural Networks

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.