A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Standardised formats and open-source analysis tools for the MAGIC telescopes data
Tekijät: Abe, S.; Abhir, J.; Abhishek, A.; Acciari, V.A.; Aguasca-Cabot, A.; Agudo, I.; Aniello, T.; Ansoldi, S.; Antonelli, L.A.; Arbet Engels, A.; Arcaro, C.; Artero, M.; Asano, K.; Babić, A.; Barres de Almeida, U.; Barrio, J.A.; Batković, I.; Bautista, A.; Baxter, J.; Becerra González, J.; Bednarek, W.; Bernardini, E.; Bernete, J.; Berti, A.; Besenrieder, J.; Bigongiari, C.; Biland, A.; Blanch, O.; Bonnoli, G.; Bošnjak, ; Bronzini, E.; Burelli, I.; Busetto, G.; Campoy-Ordaz, A.; Carosi, A.; Carosi, R.; Carretero-Castrillo, M.; Castro-Tirado, A.J.; Cerasole, D.; Ceribella, G.; Chai, Y.; Cifuentes, A.; Colombo, E.; Contreras, J.L.; Cortina, J.; Covino, S.; D'Amico, G.; D'Elia, V.; Da Vela, P.; Dazzi, F.; De Angelis, A.; De Lotto, B.; de Menezes, R.; Delfino, M.; Delgado, J.; Di Pierro, F.; Di Tria, R.; Di Venere, L.; Dominis Prester, D.; Donini, A.; Dorner, D.; Doro, M.; Elsaesser, D.; Escudero, J.; Fariña, L.; Fattorini, A.; Foffano, L.; Font, L.; Fröse, S.; Fukami, S.; Fukazawa, Y.; García López, R.J.; Garczarczyk, M.; Gasparyan, S.; Gaug, M.; Giesbrecht Paiva, J.G.; Giglietto, N.; Giordano, F.; Gliwny, P.; Gradetzke, T.; Grau, R.; Green, D.; Green, J.G.; Günther, P.; Hadasch, D.; Hahn, A.; Hassan, T.; Heckmann, L.; Herrera Llorente, J.; Hrupec, D.; Hütten, M.; Imazawa, R.; Ishio, K.; Jiménez Martínez, I.; Jormanainen, J.; Kayanoki, T.; Kerszberg, D.; Kluge, G.W.; Kobayashi, Y.; Kouch, P.M.; Kubo, H.; Kushida, J.; Láinez, M.; Lamastra, A.; Leone, F.; Lindfors, E.; Lombardi, S.; Longo, F.; López-Coto, R.; López-Moya, M.; López-Oramas, A.; Loporchio, S.; Lorini, A.; Lyard, E.; Machado de Oliveira Fraga, B.; Majumdar, P.; Makariev, M.; Maneva, G.; Manganaro, M.; Mangano, S.; Mannheim, K.; Mariotti, M.; Martínez, M.; Martínez-Chicharro, M.; Mas-Aguilar, A.; Mazin, D.; Menchiari, S.; Mender, S.; Miceli, D.; Miener, T.; Miranda, J.M.; Mirzoyan, R.; Molero González, M.; Molina, E.; Mondal, H.A.; Moralejo, A.; Morcuende, D.; Nakamori, T.; Nanci, C.; Neustroev, V.; Nickel, L.; Nievas Rosillo, M.; Nigro, C.; Nikolić, L.; Nishijima, K.; Njoh Ekoume, T.; Noda, K.; Nozaki, S.; Ohtani, Y.; Okumura, A.; Otero-Santos, J.; Paiano, S.; Paneque, D.; Paoletti, R.; Paredes, J.M.; Peresano, M.; Persic, M.; Pihet, M.; Pirola, G.; Podobnik, F.; Prada Moroni, P.G.; Prandini, E.; Principe, G.; Priyadarshi, C.; Rhode, W.; Ribó, M.; Rico, J.; Righi, C.; Sahakyan, N.; Saito, T.; Saturni, F.G.; Schmidt, K.; Schmuckermaier, F.; Schubert, J.L.; Schweizer, T.; Sciaccaluga, A.; Silvestri, G.; Sitarek, J.; Sliusar, V.; Sobczynska, D.; Spolon, A.; Stamerra, A.; Strišković, J.; Strom, D.; Strzys, M.; Suda, Y.; Suutarinen, S.; Tajima, H.; Takahashi, M.; Takeishi, R.; Temnikov, P.; Terauchi, K.; Terzić, T.; Teshima, M.; Truzzi, S.; Tutone, A.; Ubach, S.; van Scherpenberg, J.; Vazquez Acosta, M.; Ventura, S.; Viale, I.; Vigorito, C.F.; Vitale, V.; Vovk, I.; Walter, R.; Will, M.; Wunderlich, C.; Yamamoto, T.; Jouvin, L.; Linhoff, L.; Linhoff, M.; The MAGIC Collaboration
Kustantaja: Elsevier B.V.
Julkaisuvuosi: 2024
Journal: Journal of High Energy Astrophysics
Tietokannassa oleva lehden nimi: Journal of High Energy Astrophysics
Vuosikerta: 44
Aloitussivu: 266
Lopetussivu: 278
ISSN: 2214-4048
eISSN: 2214-4056
DOI: https://doi.org/10.1016/j.jheap.2024.09.011
Verkko-osoite: https://doi.org/10.1016/j.jheap.2024.09.011
Rinnakkaistallenteen osoite: https://arxiv.org/abs/2409.18823
Preprintin osoite: https://arxiv.org/abs/2409.18823v1
Instruments for gamma-ray astronomy at Very High Energies (E>100GeV) have traditionally derived their scientific results through proprietary data and software. Data standardisation has become a prominent issue in this field both as a requirement for the dissemination of data from the next generation of gamma-ray observatories and as an effective solution to realise public data legacies of current-generation instruments. Specifications for a standardised gamma-ray data format have been proposed as a community effort and have already been successfully adopted by several instruments. We present the first production of standardised data from the Major Atmospheric Gamma-ray Imaging Cherenkov (MAGIC) telescopes. We converted 166h of observations from different sources and validated their analysis with the open-source software Gammapy. We consider six data sets representing different scientific and technical analysis cases and compare the results obtained analysing the standardised data with open-source software against those produced with the MAGIC proprietary data and software. Aiming at a systematic production of MAGIC data in this standardised format, we also present the implementation of a database-driven pipeline automatically performing the MAGIC data reduction from the calibrated down to the standardised data level. In all the cases selected for the validation, we obtain results compatible with the MAGIC proprietary software, both for the manual and for the automatic data productions. Part of the validation data set is also made publicly available, thus representing the first large public release of MAGIC data. This effort and this first data release represent a technical milestone toward the realisation of a public MAGIC data legacy.
Julkaisussa olevat rahoitustiedot:
We would like to thank the Instituto de Astrofísica de Canarias for the excellent working conditions at the Observatorio del Roque de los Muchachos in La Palma. 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, PID2019-105510 GB-C31, PID2019-107847RB-C41, PID2019-107847RB-C42, PID2019-107847RB-C44, PID2019-107988 GB-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.
