dc.contributorUniversidade Estadual Paulista (UNESP)
dc.contributorLab Juterinst e Astron LIneA & INGT e Universo
dc.contributorUniv PSL
dc.contributorObserv Nacl MCTIC
dc.contributorAkdeniz Univ
dc.contributorTUBITAK Natl Observ
dc.contributorFed Univ Technol Parana UTFPR DAFIS
dc.date.accessioned2022-04-28T17:21:50Z
dc.date.accessioned2022-12-20T00:37:37Z
dc.date.available2022-04-28T17:21:50Z
dc.date.available2022-12-20T00:37:37Z
dc.date.created2022-04-28T17:21:50Z
dc.date.issued2022-02-04
dc.identifierMonthly Notices Of The Royal Astronomical Society. Oxford: Oxford Univ Press, v. 511, n. 1, p. 1167-1181, 2022.
dc.identifier0035-8711
dc.identifierhttp://hdl.handle.net/11449/218583
dc.identifier10.1093/mnras/stac032
dc.identifierWOS:000770034100013
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5398717
dc.description.abstractThe stellar occultation technique provides competitive accuracy in determining the sizes, shapes, astrometry, etc., of the occulting body, comparable to in-situ observations by spacecraft. With the increase in the number of known Solar system objects expected from the LSST, the highly precise astrometric catalogs, such as Gaia, and the improvement of ephemerides, occultations observations will become more common with a higher number of chords in each observation. In the context of the Big Data era, we developed sora, an open-source python library to reduce and analyse stellar occultation data efficiently. It includes routines from predicting such events up to the determination of Solar system bodies' sizes, shapes, and positions.
dc.languageeng
dc.publisherOxford Univ Press
dc.relationMonthly Notices Of The Royal Astronomical Society
dc.sourceWeb of Science
dc.subjectmethods: data analysis
dc.subjectsoftware: data analysis
dc.subjectoccultations
dc.titleSORA: Stellar occultation reduction and analysis
dc.typeArtículos de revistas


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