PREPRINT
22E5531A-20D8-4A63-B5CC-0F4C33D39DBE

# BlaST -- A Machine-Learning Estimator for the Synchrotron Peak of Blazars

Theo Glauch, Tobias Kerscher, Paolo Giommi
arXiv:2207.03813

Submitted on 8 July 2022

## Abstract

Active Galaxies with a jet pointing towards us, so-called blazars, play an important role in the field of high-energy astrophysics. One of the most important features in the classification scheme of blazars is the peak frequency of the synchrotron emission (${\nu }_{\mathrm{p}\mathrm{e}\mathrm{a}\mathrm{k}}^{S}$) in the spectral energy distribution (SED). In contrast to standard blazar catalogs that usually calculate the ${\nu }_{\mathrm{p}\mathrm{e}\mathrm{a}\mathrm{k}}^{S}$ manually, we have developed a machine-learning algorithm - BlaST - that not only simplifies the estimation, but also provides a reliable uncertainty evaluation. Furthermore, it naturally accounts for additional SED components from the host galaxy and the disk emission, which may be a major source of confusion. Using our tool, we re-estimate the synchrotron peaks in the Fermi 4LAC-DR2 catalog. We find that BlaST, improves the ${\nu }_{\mathrm{p}\mathrm{e}\mathrm{a}\mathrm{k}}^{S}$ estimation especially in those cases where the contribution of components not related to the jet is important.

## Preprint

Comment: Submitted to Astronomy and Computing

Subjects: Astrophysics - High Energy Astrophysical Phenomena; Astrophysics - Astrophysics of Galaxies; Astrophysics - Instrumentation and Methods for Astrophysics; High Energy Physics - Experiment