CLEAN and multiscale CLEAN for STIX in Solar Orbiter
| dc.contributor.author | Catalano, Miriana | |
| dc.contributor.author | Volpara, Anna | |
| dc.contributor.author | Massa, Paolo | |
| dc.contributor.author | Piana, Michele | |
| dc.contributor.author | Massone, Anna Maria | |
| dc.date.accessioned | 2026-02-24T12:26:24Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | CLEAN is a well-established deconvolution approach to Fourier imaging at radio wavelengths and hard X-ray energies. One of the main limitations of CLEAN for hard X-ray imaging is that it requires a final convolution step by means of a convolution kernel whose width is strongly user dependent, and moreover, under-resolution effects are often introduced. This paper describes a multiscale version of CLEAN that is specifically tailored to the reconstruction of images from measurements observed by the Spectrometer/Telescope for Imaging X-rays (STIX) on board Solar Orbiter. Using synthetic STIX data, this study shows that multiscale CLEAN might represent a reliable solution to the two CLEAN limitations described above. Further, we show the performances of CLEAN and its multiscale release in reconstructing experimental real scenarios characterized by complex emission morphologies. | |
| dc.identifier.doi | 10.1051/0004-6361/202557119 | |
| dc.identifier.issn | 1432-0746 | |
| dc.identifier.issn | 0004-6361 | |
| dc.identifier.uri | https://irf.fhnw.ch/handle/11654/55821 | |
| dc.identifier.uri | https://doi.org/10.26041/fhnw-15605 | |
| dc.language.iso | en | |
| dc.publisher | EDP Sciences | |
| dc.relation.ispartof | Astronomy & Astrophysics | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject.ddc | 520 - Astronomie, Kartografie | |
| dc.title | CLEAN and multiscale CLEAN for STIX in Solar Orbiter | |
| dc.type | 01A - Beitrag in wissenschaftlicher Zeitschrift | |
| dc.volume | 706 | |
| dspace.entity.type | Publication | |
| fhnw.InventedHere | Yes | |
| fhnw.ReviewType | Anonymous ex ante peer review of a complete publication | |
| fhnw.affiliation.hochschule | Hochschule für Informatik FHNW | de_CH |
| fhnw.affiliation.institut | Institut für Data Science | de_CH |
| fhnw.oastatus.aurora | Version: Published *** Embargo: None *** Licence: CC BY *** URL: https://v2.sherpa.ac.uk/id/publication/11142 | |
| fhnw.openAccessCategory | Diamond | |
| fhnw.pagination | A360 | |
| fhnw.publicationState | Published | |
| fhnw.targetcollection | b508cce9-5084-49ae-a565-d8e5c348c3ab | |
| relation.isAuthorOfPublication | e23ecbc5-bbc0-4287-a744-84be39550dd0 | |
| relation.isAuthorOfPublication.latestForDiscovery | e23ecbc5-bbc0-4287-a744-84be39550dd0 |
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