Glauch, Theo und Tchiorniy, Kristian (2025) flashcurve: A machine-learning approach for the simple and fast generation of adaptive-binning light curves with Fermi-LAT data. Astronomy and Computing. Elsevier. doi: 10.1016/j.ascom.2025.100937. ISSN 2213-1337.
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Offizielle URL: https://www.sciencedirect.com/science/article/pii/S2213133725000101
Kurzfassung
Gamma rays measured by the Large Area Telescope (LAT) on board the Fermi Gamma-ray Space Telescope tell us a lot about the processes taking place in high-energetic astrophysical objects. The fluxes coming from these objects are, however, extremely variable. Hence, gamma-ray light curves optimally use adaptive bin sizes in order to retrieve most information about the source dynamics and to combine gamma-ray observations in a multi-messenger perspective. However, standard adaptive binning approaches are slow, expensive and inaccurate in highly populated regions. Here, we present a novel, powerful, deep-learning-based approach to estimate the necessary time windows for adaptive binning light curves in Fermi-LAT data using raw photon data. The approach is shown to be fast and accurate. It can also be seen as a prototype to train machine-learning models for adaptive binning light curves for other astrophysical messengers.
| elib-URL des Eintrags: | https://elib.dlr.de/216302/ | ||||||||||||
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| Dokumentart: | Zeitschriftenbeitrag | ||||||||||||
| Titel: | flashcurve: A machine-learning approach for the simple and fast generation of adaptive-binning light curves with Fermi-LAT data | ||||||||||||
| Autoren: | 
 | ||||||||||||
| Datum: | April 2025 | ||||||||||||
| Erschienen in: | Astronomy and Computing | ||||||||||||
| Referierte Publikation: | Ja | ||||||||||||
| Open Access: | Ja | ||||||||||||
| Gold Open Access: | Nein | ||||||||||||
| In SCOPUS: | Ja | ||||||||||||
| In ISI Web of Science: | Ja | ||||||||||||
| DOI: | 10.1016/j.ascom.2025.100937 | ||||||||||||
| Verlag: | Elsevier | ||||||||||||
| ISSN: | 2213-1337 | ||||||||||||
| Status: | veröffentlicht | ||||||||||||
| Stichwörter: | Gamma-ray, machine learning, light curve, astronomy, Fermi | ||||||||||||
| HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||
| HGF - Programm: | Raumfahrt | ||||||||||||
| HGF - Programmthema: | keine Zuordnung | ||||||||||||
| DLR - Schwerpunkt: | Raumfahrt | ||||||||||||
| DLR - Forschungsgebiet: | R - keine Zuordnung | ||||||||||||
| DLR - Teilgebiet (Projekt, Vorhaben): | R - keine Zuordnung | ||||||||||||
| Standort: | Oberpfaffenhofen | ||||||||||||
| Institute & Einrichtungen: | Institut für Physik der Atmosphäre > Atmosphärische Spurenstoffe | ||||||||||||
| Hinterlegt von: | Glauch, Theo | ||||||||||||
| Hinterlegt am: | 10 Sep 2025 11:49 | ||||||||||||
| Letzte Änderung: | 11 Sep 2025 10:28 | 
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