Search for: All records

Abstract The stellar atmospheric parameters and physical properties of stars in the Kepler Input Catalog (KIC) are of great significance for the study of exoplanets, stellar activity, and asteroseismology. However, despite extensive effort over the past decades, accurate spectroscopic estimates of these parameters are available for only about half of the stars in the full KIC. In our work, by training relationships between photometric colors and spectroscopic stellar parameters from Gaia DR3, the Kepler-INT Survey, Large Sky Area Multi-Object Fiber Spectroscopic Telescope DR10, and Galactic Evolution Experiment at Apache Point Observatory DR17, we have obtained atmospheric parameter estimates for over 195,000 stars, accounting for 97% of the total sample of KIC stars. We obtain 1σuncertainties of 0.1 dex on metallicity [Fe/H], 100 K on effective temperatureT_eff, and 0.2 dex on surface gravity logg. In addition, based on these atmospheric parameters, we estimated the ages, masses, radii, and surface gravities of these stars using the commonly adopted isochrone-fitting approach. External comparisons indicate that the resulting precision for turnoff stars is 20% in age; for dwarf stars, it is 0.07M_⊙in mass, 0.05R_⊙in radius, and 0.12 dex in surface gravity; and for giant stars, it is 0.14M_⊙in mass, 0.73R_⊙in radius, and 0.11 dex in surface gravity. 

Abstract We present a pioneering achievement in the high-precision photometric calibration of CMOS-based photometry, by application of the Gaia Blue Photometer or Red Photometer (XP) spectra–based synthetic photometry method to the mini-SiTian array (MST) photometry. Through 79 repeated observations of thef02field on the night, we find good internal consistency in the calibrated MSTG_MST-band magnitudes for relatively bright stars, with a precision of about 4 mmag forG_MST ∼ 13. Results from more than 30 different nights (over 3100 observations) further confirm this internal consistency, indicating that the 4 mmag precision is stable and achievable over timescales of months. An independent external validation using spectroscopic data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope DR10 and high-precision photometric data using CCDs from Gaia DR3 reveals a zero-point consistency better than 1 mmag. Our results clearly demonstrate that CMOS photometry is on par with CCD photometry for high-precision results, highlighting the significant capabilities of CMOS cameras in astronomical observations, especially for large-scale telescope survey arrays. 

Abstract Stellar parameters for large samples of stars play a crucial role in constraining the nature of stars and stellar populations in the Galaxy. An increasing number of medium-band photometric surveys are presently used in estimating stellar parameters. In this study, we present a machine learning approach to derive estimates of stellar parameters, including [Fe/H], logg, andT_eff, based on a combination of medium-band and broadband photometric observations. Our analysis employs data primarily sourced from the Stellar Abundances and Galactic Evolution Survey (SAGES), which aims to observe much of the Northern Hemisphere. We combine theuv-band data from SAGES DR1 with photometric and astrometric data from Gaia EDR3, and apply the random forest method to estimate stellar parameters for approximately 21 million stars. We are able to obtain precisions of 0.09 dex for [Fe/H], 0.12 dex for logg, and 70 K forT_eff. Furthermore, by incorporating Two Micron All Sky Survey and Wide-field Infrared Survey Explorer infrared photometric and Galaxy Evolution Explorer ultraviolet data, we are able to achieve even higher precision estimates for over 2.2 million stars. These results are applicable to both giant and dwarf stars. Building upon this mapping, we construct a foundational data set for research on metal-poor stars, the structure of the Milky Way, and beyond. With the forthcoming release of additional bands from SAGES such DDO51 and Hα, this versatile machine learning approach is poised to play an important role in upcoming surveys featuring expanded filter sets. 

Abstract We present an independent validation and comprehensive recalibration of S-PLUS Ultra-short Survey (USS) DR1 12-band photometry using about 30,000–70,000 standard stars from the Best Star (BEST) database. We identify the spatial variation of zero-point offsets, up to 30–40 mmag for blue filters (u,J0378, andJ0395) and 10 mmag for others, predominantly due to the higher uncertainties of the technique employed in the original USS calibration. Moreover, we detect large- and medium-scale CCD position-dependent systematic errors, up to 50 mmag, primarily caused by different aperture and flat-field corrections. We then recalibrate the USS DR1 photometry by correcting the systematic shifts for each tile using second-order two-dimensional polynomial fitting combined with a numerical stellar flat-field correction method. The recalibrated results from the XP spectrum based synthetic photometry and the stellar color regression standards are consistent within 6 mmag in the USS zero-points, demonstrating both the typical precision of the recalibrated USS photometry and a sixfold improvement in USS zero-point precision. Further validation using the Sloan Digital Sky Survey and Pan-STARRS1, as well as LAMOST DR10 and Gaia photometry, also confirms this precision for the recalibrated USS photometry. Our results clearly demonstrate the capability and efficiency of the BEST database in improving calibration precision to the millimagnitude level for wide-field photometric surveys. The recalibrated USS DR1 photometry is publicly available on ChinaVO at doi:10.12149/101503.