{"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/varstar-detect-a-python-library-dedicated-to","title":"VarStar Detect, a Python library dedicated to the semi-automatic detection of stellar variability","arxiv_id":"2109.06347","date":"2021-09-01","proceeding":null,"authors":["Jorge Perez Gonzalez","Nicolas Carrizosa Arias","Andres Cadenas Blanco"],"abstract":"VarStar Detect is a Python package available on PyPI optimized for the detection of variability inside photometric measurements. Based off of the Least Squares method of regression, VarStar Detect calculates the amplitude of a Fourier Polynomial fit of the data as a measure of variability to assess if the star is indeed variable. This work shows the mathematical background of the package and an analysis of the code's functionality on TESS Sector 1 Data.","url_abs":"https://arxiv.org/abs/2109.06347v1","url_pdf":"https://arxiv.org/pdf/2109.06347v1.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":"varstar-detect-a-python-library-dedicated-to","repo_url":"https://github.com/varstardetect/varstardetect","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}