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 (νpeakS) in the spectral energy distribution (SED). In contrast to standard blazar catalogs that usually calculate the νpeakS 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 νpeakS 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

URL: https://arxiv.org/abs/2207.03813