DeepFilter. A machine learning technique for removing the hot AIA 304 Å channel component for the analysis of coronal rain
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Dateien
Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
2026
Typ der Arbeit
Studiengang
Sammlung
Typ
01A - Beitrag in wissenschaftlicher Zeitschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Astronomy & Astrophysics
Themenheft
DOI der Originalpublikation
Link
Zugehörige Forschungsdaten
Reihe / Serie
Reihennummer
Jahrgang / Band
707
Ausgabe / Nummer
Seiten / Dauer
A237
Patentnummer
Verlag / Herausgebende Institution
EDP Sciences
Verlagsort / Veranstaltungsort
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
The Atmosphere Imaging Assembly (AIA) 304 Å channel aboard the Solar Dynamic Observatory offers an unparalleled full-disk view of cool material at T ≈ 10 5 K emitted by the He II 304 Å spectral line. This opens the possibility for the in-depth and widespread analysis of the formation and evolution of small cool structures seen in the solar atmosphere. Of particular interest is the phenomenon of coronal rain, which has been linked to the overarching heating and cooling cycles of the solar corona. However, within the channel’s passband, hot diffuse emission from several ions is also included, leading to comparable intensity levels to the cool emission, particularly off-limb. This makes it very difficult to disentangle cool coronal rain from this hotter material. In this paper a novel morphological approach to separating these components called DeepFilter is investigated. This approach utilises a generative machine learning algorithm that can learn how to convert the AIA 304 Å images into the style of images obtained with the Interface Region Imaging Spectrograph (IRIS) 1400 Å, which has a similar temperature formation peak as for He II 304 Å but lacks this hot-component contamination. We find that the method produces good results, showing a clear reduction in the amount of hot-component material present in the final images while preserving the majority of the underlying cool structures. DeepFilter is compared to the recent physics-based RFit algorithm and is found to produce comparable results. Although the DeepFilter method is shown to perform worse at removing hot emission and material far from the limb, it performs comparably on other data – with the advantage of being far less data intensive – which makes it more effective for large-scale statistical analysis.
Schlagwörter
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
0004-6361
1432-0746
1432-0746
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
peer-reviewed
Open Access-Status
Gold
Lizenz
Zitation
McMullan, L., Antolin, P., Kleint, L., & Panos, B. (2026). DeepFilter. A machine learning technique for removing the hot AIA 304 Å channel component for the analysis of coronal rain. Astronomy & Astrophysics, 707, A237. https://doi.org/10.1051/0004-6361/202557855