{"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/the-zwicky-transient-facility-bright-1","title":"The Zwicky Transient Facility Bright Transient Survey. III. $\\texttt{BTSbot}$: Automated Identification and Follow-up of Bright Transients with Deep Learning","arxiv_id":"2401.15167","date":"2024-01-26","proceeding":null,"authors":["Nabeel Rehemtulla","Adam A. Miller","Theophile Jegou Du Laz","Michael W. Coughlin","Christoffer Fremling","Daniel A. Perley","Yu-Jing Qin","Jesper Sollerman","Ashish A. Mahabal","Russ R. Laher","Reed Riddle","Ben Rusholme","Shrinivas R. Kulkarni"],"abstract":"The Bright Transient Survey (BTS) aims to obtain a classification spectrum for all bright ($m_\\mathrm{peak}\\,\\leq\\,18.5\\,$mag) extragalactic transients found in the Zwicky Transient Facility (ZTF) public survey. BTS critically relies on visual inspection (\"scanning\") to select targets for spectroscopic follow-up, which, while effective, has required a significant time investment over the past $\\sim5$ yr of ZTF operations. We present $\\texttt{BTSbot}$, a multi-modal convolutional neural network, which provides a bright transient score to individual ZTF detections using their image data and 25 extracted features. $\\texttt{BTSbot}$ is able to eliminate the need for daily human scanning by automatically identifying and requesting spectroscopic follow-up observations of new bright transient candidates. $\\texttt{BTSbot}$ recovers all bright transients in our test split and performs on par with scanners in terms of identification speed (on average, $\\sim$1 hour quicker than scanners). We also find that $\\texttt{BTSbot}$ is not significantly impacted by any data shift by comparing performance across a concealed test split and a sample of very recent BTS candidates. $\\texttt{BTSbot}$ has been integrated into Fritz and $\\texttt{Kowalski}$, ZTF's first-party marshal and alert broker, and now sends automatic spectroscopic follow-up requests for the new transients it identifies. During the month of October 2023, $\\texttt{BTSbot}$ selected 296 sources in real-time, 93% of which were real extragalactic transients. With $\\texttt{BTSbot}$ and other automation tools, the BTS workflow has produced the first fully automatic end-to-end discovery and classification of a transient, representing a significant reduction in the human-time needed to scan. Future development has tremendous potential for creating similar models to identify and request follow-up observations for specific types of transients.","url_abs":"https://arxiv.org/abs/2401.15167v1","url_pdf":"https://arxiv.org/pdf/2401.15167v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"the-zwicky-transient-facility-bright-1","repo_url":"https://github.com/nabeelre/btsbot","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2401.15167","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}