BlaST

• Verfasst von: Glauch, Theo [VerfasserIn]
• Verfasst von: Kerscher, T. [VerfasserIn]
• Verfasst von: Giommi, Paolo [VerfasserIn]
• Titel: BlaST
• Titelzusatz: a machine-learning estimator for the synchrotron peak of blazars
• Verf.angabe: T. Glauch, T. Kerscher, P. Giommi
• E-Jahr: 2022
• Jahr: 27 August 2022
• Umfang: 10 S.
• Fussnoten: Gesehen am 26.10.2022
• Titel Quelle: Enthalten in: Astronomy and computing
• Ort Quelle: Amsterdam [u.a.] : Elsevier, 2013
• Jahr Quelle: 2022
• Band/Heft Quelle: 41(2022), Artikel-ID 100646, Seite 1-10
• ISSN Quelle: 2213-1337
• DOI: doi:10.1016/j.ascom.2022.100646
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Astronomical databases: miscellaneous
• Sach-SW: BL Lacertae objects: general
• Sach-SW: Galaxies: active
• Sach-SW: Galaxies: jets
• Sach-SW: Machine learning
• Sach-SW: Methods: data analysis
• K10plus-PPN: 1820037134

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 νpeakSmanually, 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 BlaSTimproves the νpeakS estimation especially in those cases where the contribution of components not related to the jet is important.