Abstract

Context. Jets from supermassive black holes at the centers of active galaxies are the most powerful and persistent sources of electromagnetic radiation in the Universe. To infer the physical conditions in the otherwise out-of-reach regions of extragalactic jets, we usually rely on fitting their spectral energy distributions (SEDs). The calculation of radiative models for the jet’s non-thermal emission usually relies on numerical solvers of coupled partial differential equations. Aims. In this work, we use machine learning to tackle the problem of high computational complexity to significantly reduce the SED model evaluation time, which is necessary for SED fittings carried out with Bayesian inference methods. Methods. We computed the SEDs based on the synchrotron self-Compton model for blazar emission using the radiation code ATHEvA. We used them to train neural networks (NNs) to explore whether they can replace the original code, which is computationally expensive. Results. We find that a NN with gated recurrent unit neurons (GRUN) can effectively replace the ATHEvA leptonic code for this application, while it can be efficiently coupled with Markov chain Monte Carlo (MCMC) and nested sampling algorithms for fitting purposes. We demonstrate this approach through an application to simulated data sets, as well as a subsequent application to observational data. Conclusions. We present a proof-of-concept application of NNs to blazar science as the first step in a list of future applications involving hadronic processes and even larger parameter spaces. We offer this tool to the community through a public repository.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.