Sarah Blunt, Jason Jinfei Wang, Vighnesh Nagpal, Lea Hirsch, Roberto Tejada, Tirth Dharmesh Surti, Sofia Covarrubias, Thea McKenna, Rodrigo Ferrer Chávez, Jorge Llop-Sayson, Mireya Arora, Amanda Chavez, Devin Cody, Saanika Choudhary, Adam Smith, William Balmer, Tomas Stolker, Hannah Gallamore, Clarissa R. Do Ó, Eric L. Nielsen, Robert J. De Rosa
{"title":"orbitize! v3:高对比度成像社区的轨道拟合","authors":"Sarah Blunt, Jason Jinfei Wang, Vighnesh Nagpal, Lea Hirsch, Roberto Tejada, Tirth Dharmesh Surti, Sofia Covarrubias, Thea McKenna, Rodrigo Ferrer Chávez, Jorge Llop-Sayson, Mireya Arora, Amanda Chavez, Devin Cody, Saanika Choudhary, Adam Smith, William Balmer, Tomas Stolker, Hannah Gallamore, Clarissa R. Do Ó, Eric L. Nielsen, Robert J. De Rosa","doi":"arxiv-2409.11573","DOIUrl":null,"url":null,"abstract":"orbitize! is a package for Bayesian modeling of the orbital parameters of\nresolved binary objects from time series measurements. It was developed with\nthe needs of the high-contrast imaging community in mind, and has since also\nbecome widely used in the binary star community. A generic orbitize! use case\ninvolves translating relative astrometric time series, optionally combined with\nradial velocity or astrometric time series, into a set of derived orbital\nposteriors. This paper is published alongside the release of orbitize! version\n3.0, which has seen significant enhancements in functionality and accessibility\nsince the release of version 1.0 (Blunt et al., 2020).","PeriodicalId":501068,"journal":{"name":"arXiv - PHYS - Solar and Stellar Astrophysics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"orbitize! v3: Orbit fitting for the High-contrast Imaging Community\",\"authors\":\"Sarah Blunt, Jason Jinfei Wang, Vighnesh Nagpal, Lea Hirsch, Roberto Tejada, Tirth Dharmesh Surti, Sofia Covarrubias, Thea McKenna, Rodrigo Ferrer Chávez, Jorge Llop-Sayson, Mireya Arora, Amanda Chavez, Devin Cody, Saanika Choudhary, Adam Smith, William Balmer, Tomas Stolker, Hannah Gallamore, Clarissa R. Do Ó, Eric L. Nielsen, Robert J. De Rosa\",\"doi\":\"arxiv-2409.11573\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"orbitize! is a package for Bayesian modeling of the orbital parameters of\\nresolved binary objects from time series measurements. It was developed with\\nthe needs of the high-contrast imaging community in mind, and has since also\\nbecome widely used in the binary star community. A generic orbitize! use case\\ninvolves translating relative astrometric time series, optionally combined with\\nradial velocity or astrometric time series, into a set of derived orbital\\nposteriors. This paper is published alongside the release of orbitize! version\\n3.0, which has seen significant enhancements in functionality and accessibility\\nsince the release of version 1.0 (Blunt et al., 2020).\",\"PeriodicalId\":501068,\"journal\":{\"name\":\"arXiv - PHYS - Solar and Stellar Astrophysics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - PHYS - Solar and Stellar Astrophysics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.11573\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Solar and Stellar Astrophysics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.11573","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
orbitize! v3: Orbit fitting for the High-contrast Imaging Community
orbitize! is a package for Bayesian modeling of the orbital parameters of
resolved binary objects from time series measurements. It was developed with
the needs of the high-contrast imaging community in mind, and has since also
become widely used in the binary star community. A generic orbitize! use case
involves translating relative astrometric time series, optionally combined with
radial velocity or astrometric time series, into a set of derived orbital
posteriors. This paper is published alongside the release of orbitize! version
3.0, which has seen significant enhancements in functionality and accessibility
since the release of version 1.0 (Blunt et al., 2020).