利用SVO滤光片档案服务与测光工具对矮星和巨星FGK恒星进行测光区分
Photometric segregation of dwarf and giant FGK stars using the SVO Filter Profile Service and photometric tools
AI总结：
本文利用SVO滤光片服务和J-PAS窄带测光数据，结合高斯混合模型与支持向量机，实现了FGK矮星和巨星的高精度区分，并建立了矮星颜色-温度关系。
摘要：
本文聚焦于利用西班牙虚拟天文台（SVO）滤光片档案服务及相关测光工具，通过窄带测光数据对FGK型矮星和巨星进行区分。我们从MILES、STELIB和ELODIE恒星库中选取光谱，并使用SVO测光工具在15个J-PAS窄带滤光片中合成测光数据，这些滤光片经过专门挑选以覆盖对表面重力变化敏感的光谱特征。利用高斯混合模型和支持向量机等机器学习技术，我们基于J-PAS颜色定义了若干判据，用于区分矮星和巨星。我们选出了五个在两类样本之间呈现最有希望分离效果的颜色-颜色图。研究结果表明，在所研究样本中，FGK恒星的整体准确率约为0.97，但发现该准确率与光度类型和恒星有效温度有关。我们还为有效温度在4000至7000 K之间的矮星定义了一个颜色-温度关系，该关系可利用四个J-PAS滤光片（J0450、J0510、J0550和J0620）估算恒星有效温度。此外，我们将研究扩展到M型巨星和矮星，取得了与FGK恒星相近的准确率。
英文摘要：
This paper is focused on the segregation of FGK dwarf and giant stars through narrow-band photometric data using the Spanish Virtual Observatory (SVO) Filter Profile Service and associated photometric tools. We selected spectra from the MILES, STELIB, and ELODIE stellar libraries, and used SVO photometric tools to derive the synthetic photometry in 15 J-PAS narrow filters, which were especially selected to cover spectral features sensitive to gravity changes. Using machine-learning techniques as the Gaussian mixture model and the support vector machine, we defined several criteria based on J-PAS colours to discriminate between dwarf and giant stars. We selected five colour-colour diagrams that presented the most promising separation between both samples. Our results show an overall accuracy in the studied sample of $\sim$0.97 for FGK stars, although a dependence on the luminosity type and the stellar effective temperature was found. We also defined a colour-temperature relation for dwarf stars with effective temperatures between 4\,000 and 7\,000\,K, which allows one to estimate the stellar effective temperature from four J-PAS filters ($J0450$, $J0510$, $J0550$, and $J0620$). Additionally, we extended the study to M-type giant and dwarf stars, achieving a similar accuracy to that for FGK stars.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
