分类: 天文学 >> 天文学 提交时间: 2023-02-19
摘要: Understanding the origins of small-scale flats of CCDs and their wavelength-dependent variations plays an important role in high-precision photometric, astrometric, and shape measurements of astronomical objects. Based on the unique flat data of 47 narrow-band filters provided by JPAS-{\it Pathfinder}, we analyze the variations of small-scale flats as a function of wavelength. We find moderate variations (from about $1.0\%$ at 390 nm to $0.3\%$ at 890 nm) of small-scale flats among different filters, increasing towards shorter wavelengths. Small-scale flats of two filters close in central wavelengths are strongly correlated. We then use a simple physical model to reproduce the observed variations to a precision of about $\pm 0.14\%$, by considering the variations of charge collection efficiencies, effective areas and thicknesses between CCD pixels. We find that the wavelength-dependent variations of small-scale flats of the JPAS-{\it Pathfinder} camera originate from inhomogeneities of the quantum efficiency (particularly charge collection efficiency) as well as the effective area and thickness of CCD pixels. The former dominates the variations in short wavelengths while the latter two dominate at longer wavelengths. The effects on proper flat-fielding as well as on photometric/flux calibrations for photometric/slit-less spectroscopic surveys are discussed, particularly in blue filters/wavelengths. We also find that different model parameters are sensitive to flats of different wavelengths, depending on the relations between the electron absorption depth, the photon absorption length and the CCD thickness. In order to model the wavelength-dependent variations of small-scale flats, a small number (around ten) of small-scale flats with well-selected wavelengths are sufficient to reconstruct small-scale flats in other wavelengths.
分类: 天文学 >> 天文学 提交时间: 2023-02-19
摘要: Context. Stellar parameters are among the most important characteristics in studies of stars, which are based on atmosphere models in traditional methods. However, time cost and brightness limits restrain the efficiency of spectral observations. The J-PLUS is an observational campaign that aims to obtain photometry in 12 bands. Owing to its characteristics, J-PLUS data have become a valuable resource for studies of stars. Machine learning provides powerful tools to efficiently analyse large data sets, such as the one from J-PLUS, and enable us to expand the research domain to stellar parameters. Aims. The main goal of this study is to construct a SVR algorithm to estimate stellar parameters of the stars in the first data release of the J-PLUS observational campaign. Methods. The training data for the parameters regressions is featured with 12-waveband photometry from J-PLUS, and is cross-identified with spectrum-based catalogs. These catalogs are from the LAMOST, the APOGEE, and the SEGUE. We then label them with the stellar effective temperature, the surface gravity and the metallicity. Ten percent of the sample is held out to apply a blind test. We develop a new method, a multi-model approach in order to fully take into account the uncertainties of both the magnitudes and stellar parameters. The method utilizes more than two hundred models to apply the uncertainty analysis. Results. We present a catalog of 2,493,424 stars with the Root Mean Square Error of 160K in the effective temperature regression, 0.35 in the surface gravity regression and 0.25 in the metallicity regression. We also discuss the advantages of this multi-model approach and compare it to other machine-learning methods.