A4 Vertaisarvioitu artikkeli konferenssijulkaisussa
Insight into the Jet Emission Properties from Long-Term Monitoring of the TeV Blazar PG 1553+113
Tekijät: Prandini, E.; Hovatta, T.; Stamerra, A.; Silvestri, G.; MAGIC collaboration
Toimittaja: N/A
Konferenssin vakiintunut nimi: International Cosmic Ray Conference
Kustantaja: Sissa Medialab Srl
Julkaisuvuosi: 2025
Lehti: POS Proceedings of Science
Kokoomateoksen nimi: Proceedings of 39th International Cosmic Ray Conference (ICRC2025)
Sarjan nimi: POS Proceedings of Science
Vuosikerta: 501
eISSN: 1824-8039
DOI: https://doi.org/10.22323/1.501.0813
Julkaisun avoimuus kirjaamishetkellä: Avoimesti saatavilla
Julkaisukanavan avoimuus : Kokonaan avoin julkaisukanava
Verkko-osoite: https://doi.org/10.22323/1.501.0813
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/508986792
Rinnakkaistallenteen lisenssi: CC BY NC ND
Rinnakkaistallennetun julkaisun versio: Kustantajan versio
PG 1553+113 is a distant TeV blazar known for its ~2.2-year periodic gamma-ray signal detected with Fermi-LAT. We present results from a decade-long, multiwavelength monitoring campaign of this source.
Our analysis confirms the periodicity in gamma-ray and optical bands; however, no significant periodicity is found at TeV and X-ray energies, based on observations with MAGIC and Swift-XRT, respectively. These findings, combined with a study of variability on different timescales, support a multi-zone emission scenario, as further corroborated by recent IXPE observations.
We test a two-zone, synchrotron self-Compton model on 2019 multi-frequency flare data. The long- and short-term variability, along with inter-band correlations from the monitoring, serve as key inputs to constrain the model, reducing the large degrees of freedom. Based on this approach, we propose a set of parameters that effectively describe the PG 1553+113 emitting region responsible for the observed radiation.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
Julkaisussa olevat rahoitustiedot:
The financial support of the German BMBF, MPG and HGF; the Italian INFN and INAF; the Swiss National Fund SNF; the grants PID2019-107988GB-C22, PID2022-136828NB-C41, PID2022-137810NBC22, PID2022-138172NB-C41, PID2022-138172NB-C42, PID2022-138172NB-C43, PID2022- 139117NB-C41, PID2022-139117NB-C42, PID2022-139117NB-C43, PID2022-139117NB-C44, CNS2023-144504 funded by the Spanish MCIN/AEI/ 10.13039/501100011033 and "ERDF A way of making Europe; the Indian Department of Atomic Energy; the Japanese ICRR, the University of Tokyo, JSPS, and MEXT; the Bulgarian Ministry of Education and Science, National RI Roadmap Project DO1-400/18.12.2020 and the Academy of Finland grant nr. 320045 is gratefully acknowledged. This work was also been supported by Centros de Excelencia “Severo Ochoa” y Unidades “María de Maeztu” program of the Spanish MCIN/AEI/ 10.13039/501100011033 (CEX2019-000920-S, CEX2019-000918-M, CEX2021-001131-S) and by the CERCA institution and grants 2021SGR00426 and 2021SGR00773 of the Generalitat de Catalunya; by the Croatian Science Foundation (HrZZ) Project IP-2022-10-4595 and the University of Rijeka Project uniriprirod-18-48; by the Deutsche Forschungsgemeinschaft (SFB1491) and by the Lamarr-Institute for Machine Learning and Artificial Intelligence; by the Polish Ministry Of Education and Science grant No. 2021/WK/08; and by the Brazilian MCTIC, CNPq and FAPERJ. EP acknowledges funding for the project “SKYNET: Deep Learning for Astroparticle Physics”, 693 PRIN 2022 (CUP: D53D23002610006).
