{"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/scaling-k2-ii-assembly-of-a-fully-automated","title":"Scaling K2. II. Assembly of a Fully Automated C5 Planet Candidate Catalog Using EDI-Vetter","arxiv_id":"2001.11515","date":"2020-01-30","proceeding":null,"authors":["Jon K. Zink","Kevin K. Hardegree-Ullman","Jessie L. Christiansen","Courtney D. Dressing","Ian J. M. Crossfield","Erik A. Petigura","Joshua E. Schlieder","David R. Ciardi"],"abstract":"We present a uniform transiting exoplanet candidate list for Campaign 5 of the K2 mission. This catalog contains 75 planets with 7 multi-planet systems (5 double, 1 triple, and 1 quadruple planet system). Within the range of our search, we find 8 previously undetected candidates with the remaining 66 candidates overlapping 51% of the Kruse et al. study that manually vet Campaign 5 candidates. In order to vet our potential transit signals, we introduce the Exoplanet Detection Identification Vetter (EDI-Vetter), which is a fully automated program able to determine if a transit signal should be labeled as a false positive or a planet candidate. This automation allows us to create a statistically uniform catalog, ideal for planet occurrence rate measurements. When tested, the vetting software is able to ensure our sample is 94.2% reliable against systematic false positives. Additionally, we inject artificial transits at the light-curve-level of the raw K2 data and find the maximum completeness of our pipeline is 70% before vetting and 60% after vetting. For convenience of future occurrence rate studies, we include measurements of stellar noise (CDPP) and the three-transit window function for each target. This study is part of a larger survey of the K2 data set and the methodology which will be applied to the entirety of the K2 data set.","url_abs":"http://arxiv.org/abs/2001.11515v1","url_pdf":"http://arxiv.org/pdf/2001.11515v1.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":"scaling-k2-ii-assembly-of-a-fully-automated","repo_url":"https://github.com/jonzink/EDI-Vetter","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}