Modeling Blazar Broadband Emission with Convolutional Neural Networks. II. External Compton Model

Abstract

Abstract In the context of modeling spectral energy distributions (SEDs) for blazars, we extend the method that uses a convolutional neural network (CNN) to include external inverse Compton processes. The model assumes that relativistic electrons within the emitting region can interact with and up-scatter external photons originating from the accretion disk, the broad-line region, and the torus, to produce the observed high-energy emission. We trained the CNN on a numerical model that accounts for the injection of electrons, their self-consistent cooling, and pair creation-annihilation processes, considering both internal and all external photon fields. Despite the larger number of parameters compared to the synchrotron self-Compton model and the greater diversity in spectral shapes, the CNN enables an accurate computation of the SED for a specified set of parameters. The performance of the CNN is demonstrated by fitting the SED of two flat-spectrum radio quasars, namely 3C 454.3 and CTA 102, and obtaining their parameter posterior distributions. For the first source, the available data in the low-energy band allowed us to constrain the minimum Lorentz factor of the electrons, γ min , while for the second source, due to the lack of these data, γ min = 10 2 was set. We used the obtained parameters to investigate the energetics of the system. The model developed here, along with one from Bégué et al., enables self-consistent, in-depth modeling of blazar broadband emissions within a leptonic scenario.