分类: 天文学 >> 天文学 提交时间: 2023-02-19
摘要: Context. In modern astronomy, machine learning has proved to be efficient and effective to mine the big data from the newesttelescopes. Spectral surveys enable us to characterize millions of objects, while long exposure time observations and wide surveysconstrain their strides from millions to billions. Aims.In this study, we construct a supervised machine learning algorithm, to classify the objects in the Javalambre Photometric LocalUniverse Survey first data release (J-PLUS DR1). Methods.The sample set is featured with 12-waveband photometry, and magnitudes are labeled with spectrum-based catalogs, in-cluding Sloan Digital Sky Survey spectroscopic data, Large Sky Area Multi-Object Fiber Spectroscopic Telescope, and VERONCAT- Veron Catalog of Quasars & AGN. The performance of the classifier is presented with applications of blind test validations basedon RAdial Velocity Extension, Kepler Input Catalog, 2 MASS Redshift Survey, and the UV-bright Quasar Survey. A new algorithmis applied to constrain the extrapolation that could decrease accuracies for many machine learning classifiers. Results.The accuracies of the classifier are 96.5% in blind test and 97.0% in training cross validation. The F1-scores for each classare presented to show the precision of the classifier. We also discuss different methods to constrain the po
分类: 天文学 >> 天文学 提交时间: 2023-02-19
摘要: The CARMENES instrument was conceived to deliver high-accuracy radial velocity (RV) measurements with long-term stability to search for temperate rocky planets around a sample of nearby cool stars. The broad wavelength coverage was designed to provide a range of stellar activity indicators to assess the nature of potential RV signals and to provide valuable spectral information to help characterise the stellar targets. The CARMENES Data Release 1 (DR1) makes public all observations obtained during the CARMENES guaranteed time observations, which ran from 2016 to 2020 and collected 19,633 spectra for a sample of 362 targets. The CARMENES survey target selection was aimed at minimising biases, and about 70% of all known M dwarfs within 10 pc and accessible from Calar Alto were included. The data were pipeline-processed, and high-level data products, including 18,642 precise RVs for 345 targets, were derived. Time series data of spectroscopic activity indicators were also obtained. We discuss the characteristics of the CARMENES data, the statistical properties of the stellar sample, and the spectroscopic measurements. We show examples of the use of CARMENES data and provide a contextual view of the exoplanet population revealed by the survey, including 33 new planets, 17 re-analysed planets, and 26 confirmed planets from transiting candidate follow-up. A subsample of 238 targets was used to derive updated planet occurrence rates, yielding an overall average of 1.44+/-0.20 planets with 1 M_Earth < M sin i < 1000 M_Earth and 1 d < P_orb < 1000 d per star, and indicating that nearly every M dwarf hosts at least one planet. CARMENES data have proven very useful for identifying and measuring planetary companions as well as for additional applications, such as the determination of stellar properties, the characterisation of stellar activity, and the study of exoplanet atmospheres.