{"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/reconstructing-video-from-interferometric","title":"Reconstructing Video from Interferometric Measurements of Time-Varying Sources","arxiv_id":"1711.01357","date":"2017-11-03","proceeding":null,"authors":["Katherine L. Bouman","Michael D. Johnson","Adrian V. Dalca","Andrew A. Chael","Freek Roelofs","Sheperd S. Doeleman","William T. Freeman"],"abstract":"Very long baseline interferometry (VLBI) makes it possible to recover images\nof astronomical sources with extremely high angular resolution. Most recently,\nthe Event Horizon Telescope (EHT) has extended VLBI to short millimeter\nwavelengths with a goal of achieving angular resolution sufficient for imaging\nthe event horizons of nearby supermassive black holes. VLBI provides\nmeasurements related to the underlying source image through a sparse set\nspatial frequencies. An image can then be recovered from these measurements by\nmaking assumptions about the underlying image. One of the most important\nassumptions made by conventional imaging methods is that over the course of a\nnight's observation the image is static. However, for quickly evolving sources,\nsuch as the galactic center's supermassive black hole (Sgr A*) targeted by the\nEHT, this assumption is violated and these conventional imaging approaches\nfail. In this work we propose a new way to model VLBI measurements that allows\nus to recover both the appearance and dynamics of an evolving source by\nreconstructing a video rather than a static image. By modeling VLBI\nmeasurements using a Gaussian Markov Model, we are able to propagate\ninformation across observations in time to reconstruct a video, while\nsimultaneously learning about the dynamics of the source's emission region. We\ndemonstrate our proposed Expectation-Maximization (EM) algorithm, StarWarps, on\nrealistic synthetic observations of black holes, and show how it substantially\nimproves results compared to conventional imaging algorithms. Additionally, we\ndemonstrate StarWarps on real VLBI data of the M87 Jet from the VLBA.","url_abs":"http://arxiv.org/abs/1711.01357v2","url_pdf":"http://arxiv.org/pdf/1711.01357v2.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":"reconstructing-video-from-interferometric","repo_url":"https://github.com/achael/eht-imaging","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-imputation","task_name":"Image Imputation"},{"task_slug":"radio-interferometry","task_name":"Radio Interferometry"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}