Speaker
Description
Modeling the spectral energy distributions (SEDs) of blazars with physically motivated models is computationally expensive, as it requires solving coupled differential equations numerically and scanning high-dimensional parameter spaces.
In this contribution I will present our recent application of machine learning to accelerate the evaluations of blazar SED. Our method relies on a neural network (NN) architecture based on Gated Recurrent Units (GRUs), trained on a large sample of lepto-hadronic blazar simulations computed with the publicly available codes $AM^3$ and $LeHaMoC$. The resulting NN offers a strongly reduced run time while maintaining high accuracy in SED prediction. This efficiency enables Bayesian inference to be performed efficiently, making the method suitable for real-time analysis of blazar data.
