{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/classifying-x-ray-binaries-a-probabilistic","title":"Classifying X-ray Binaries: A Probabilistic Approach","arxiv_id":"1507.03538","date":"2015-07-13","proceeding":null,"authors":["Giri Gopalan","Saeqa Dil Vrtilek","Luke Bornn"],"abstract":"In X-ray binary star systems consisting of a compact object that accretes\nmaterial from an orbiting secondary star, there is no straightforward means to\ndecide if the compact object is a black hole or a neutron star. To assist this\nclassification, we develop a Bayesian statistical model that makes use of the\nfact that X-ray binary systems appear to cluster based on their compact object\ntype when viewed from a 3-dimensional coordinate system derived from X-ray\nspectral data. The first coordinate of this data is the ratio of counts in mid\nto low energy band (color 1), the second coordinate is the ratio of counts in\nhigh to low energy band (color 2), and the third coordinate is the sum of\ncounts in all three bands. We use this model to estimate the probabilities that\nan X-ray binary system contains a black hole, non-pulsing neutron star, or\npulsing neutron star. In particular, we utilize a latent variable model in\nwhich the latent variables follow a Gaussian process prior distribution, and\nhence we are able to induce the spatial correlation we believe exists between\nsystems of the same type. The utility of this approach is evidenced by the\naccurate prediction of system types using Rossi X-ray Timing Explorer All Sky\nMonitor data, but it is not flawless. In particular, non-pulsing neutron\nsystems containing \"bursters\" that are close to the boundary demarcating\nsystems containing black holes tend to be classified as black hole systems. As\na byproduct of our analyses, we provide the astronomer with public R code that\ncan be used to predict the compact object type of X-ray binaries given training\ndata.","url_abs":"http://arxiv.org/abs/1507.03538v3","url_pdf":"http://arxiv.org/pdf/1507.03538v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"classifying-x-ray-binaries-a-probabilistic","repo_url":"https://github.com/ggopalan/XRay-Binary-Classification","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"object","task_name":"Object"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}