Cosmic-ray acceleration and escape from supernova remnant W44 as probed by Fermi-LAT and MAGIC
Approved
Classifications
MinEdu publication type
A1 Journal article (peer-reviewed)
Definition
Article
Target group
Scientific
Peer reviewed
Peer-reviewed
Article type
Journal article
Host publication type
Journal
Publication channel information
Title of journal/series
Astronomy and astrophysics
ISSN (print)
0004-6361
ISSN (electronic)
1432-0746
ISSN (linking)
0004-6361
Publisher
EDP sciences
Publication forum ID
51953
Publication forum level
3
Internationality
Yes
Detailed publication information
Publication year
2025
Reporting year
2025
Journal/series volume number
693
Article number
A255
DOI
10.1051/0004-6361/202449748
Language of publication
English
Co-publication information
International co-publication
Yes
Co-publication with a company
No
Availability
Link to online publication
Link to self-archived version
Classification and additional information
MinEdu field of science classification
115 Astronomy, Space science
Keywords
acceleration of particles; diffusion; cosmic rays; ISM: supernova remnants; gamma rays: general
Funding information
Funding information in the publication
The financial support of the German BMBF, MPG and HGF; the Italian INFN and INAF; the Swiss National Fund SNF; the grants PID2019-104114RB-C31, PID2019-104114RB-C32, PID2019-104114RB-C33, PID2019-105510GB-C31, PID2019-107847RB-C41, PID2019-107847RB-C42, PID2019-107847RB-C44, PID2019-107988GB-C22, PID2022-136828NB-C41, PID2022-137810NB-C22, PID2022-138172NB-C41, PID2022-138172NB-C42, PID2022-138172NB-C43, PID2022-139117NB-C41, PID2022-139117NB-C42, PID2022-139117NB-C43, PID2022-139117NB-C44 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 uniri-prirod-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.
Source database ID
WoS ID
WOS:001413222500001
Scopus ID
2-s2.0-85218957802