{"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/on-the-detectability-and-parameterisation-of","title":"On the detectability and parameterisation of binary stars through spectral energy distributions","arxiv_id":"2412.05606","date":"2024-12-07","proceeding":null,"authors":["Vikrant V. Jadhav"],"abstract":"This study examines the characterization of binary star systems using Spectral Energy Distributions (SEDs), a technique increasingly essential with the rise of large-scale astronomical surveys. Binaries can emit flux at different regions of the electromagnetic spectrum, making SEDs a valuable tool in identifying and characterising unresolved binary systems. However, fitting multi-component models to SEDs and recovering accurate stellar parameters remains challenging due to nonlinear fitting methods and inherent uncertainties in the data and the spectral models. In this work, a simplified approach was used to model stars as blackbodies and we tested the accuracy of parameter recovery from SEDs, particularly focusing on secondary stars. We explored a range of primary properties, filter sets and noise models. Special attention was given to two case studies: one examining the detection of unresolved binaries using Gaia XP spectra, and the other focusing on identifying hotter companions in binary systems using UV-IR SEDs. Although an analytic prescription for recoverability is not possible, we present a simplified model and the necessary Python tools to analyse any potential binary system. Finally, we propose using blackbody models as a baseline for error estimation in SED fitting, offering a potential method for measuring fitting errors and improving the precision of binary star characterisations.","url_abs":"https://arxiv.org/abs/2412.05606v1","url_pdf":"https://arxiv.org/pdf/2412.05606v1.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":"on-the-detectability-and-parameterisation-of","repo_url":"https://github.com/jikrant3/sed-analysis-tools","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}