{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T05:07:55Z","timestamp":1751519275446,"version":"3.32.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685694","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,20]]},"abstract":"<jats:p>This paper is devoted to the development of an astrometric solver aiming to reveal precise stellar parameters by mining the data provided by a satellite telescope. First, we focus on the self-calibration model of the satellite telescope intended to absorb the telescope\u2019s optical and mechanical distortions and the satellite\u2019s imprecise attitude. Our experiments reveal that the calibration model based on the Legendre polynomials can cope with different types of input data distortions. Second, we demonstrate the performance of the solver for different test cases on the CPU cluster. So far we showed that 275 millions of stellar observations can be processed on 64 cluster nodes within 2.5 hours. The viability of this approach is proven along with the indication of bottlenecks and challenges for future work.<\/jats:p>","DOI":"10.3233\/faia241425","type":"book-chapter","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T09:48:40Z","timestamp":1734947320000},"source":"Crossref","is-referenced-by-count":1,"title":["Satellite Telescope Self-Calibration Through Precise Stellar Data Mining"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8353-7641","authenticated-orcid":false,"given":"Konstantin","family":"Ryabinin","sequence":"first","affiliation":[{"name":"Astronomisches Rechen-Institut, Center for Astronomy of Heidelberg University"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5316-4062","authenticated-orcid":false,"given":"Gerasimos","family":"Sarras","sequence":"additional","affiliation":[{"name":"Astronomisches Rechen-Institut, Center for Astronomy of Heidelberg University"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1319-5601","authenticated-orcid":false,"given":"Wolfgang","family":"L\u00f6ffler","sequence":"additional","affiliation":[{"name":"Astronomisches Rechen-Institut, Center for Astronomy of Heidelberg University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2207-7979","authenticated-orcid":false,"given":"Olga","family":"Erokhina","sequence":"additional","affiliation":[{"name":"Astronomisches Rechen-Institut, Center for Astronomy of Heidelberg University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5791-9056","authenticated-orcid":false,"given":"Michael","family":"Biermann","sequence":"additional","affiliation":[{"name":"Astronomisches Rechen-Institut, Center for Astronomy of Heidelberg University"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining X"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA241425","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T09:48:40Z","timestamp":1734947320000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA241425"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,20]]},"ISBN":["9781643685694"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia241425","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,20]]}}}