大样本恒星演化研究组知识库

The measurement of atmospheric parameters is fundamental for scientific research using stellar spectra. The Chinese Space Station Telescope (CSST), scheduled to be launched in 2024, will provide researchers with hundreds of millions of slitless spectra for stars during a 10 yr survey. And machine learning has unparalleled efficiency in processing large amounts of data compared to manual processing. Here we studied the stellar parameters of early-type stars (effective temperature T eff > 15,000 K) based on the design indicators of the CSST slitless spectrum and the machine learning algorithm, Stellar LAbel Machine. We used the Potsdam Wolf–Rayet (POWR) synthetic spectra library for cross validation. Then we tested the reliability of machine learning results by using the Next Generation Spectrum Library (NGSL) from Hubble Space Telescope observation data. In an ideal case for full-wavelength spectra without any noise, the average absolute value of the relative deviation is 2.7% (800 K) for T eff, and 3.5% (0.11 c.g.s) for surface gravity log g . We use the spectra with the impact of interstellar extinction (AV = 0, 0.5, 1, 1.5, 2 mag) and radial velocity (RV = ?50, ?30, 0, 30, 50 km s?1) from the POWR library as the test set. When RV = 0 km s?1 and AV = 0 mag, the average value and standard deviation for 3 wavelength ranges (2550–4050 ? (R = 287); 4050–6300 ? (R = 232); 6300–10000 ? (R = 207)) are –66 ± 3351 K, 550 ± 3536 K, and 356 ± 3616 K for T eff, and 0.004 ± 0.224 c.g.s, –0.024 ± 0.246 c.g.s, and 0.01 ± 0.212 c.g.s for log g . When using the observed data from NGSL as the testing samples, the deviation of T eff is less than 5% (1500 K), and the deviation of log g is less than 11% (0.33 c.g.s). In addition, we also test the influence of shifting of spectra on the parameters’ accuracy. The deviation of T eff for the case with a shift of 5 ? and 10 ? are 3.6% (1100 K) and 4.3% (1300 K), respectively; the deviation of log g are 4.2% (0.13 c.g.s) and 5.1% (0.15 c.g.s). These results demonstrate that we can obtain relatively accurate stellar parameters of a population of early-type stars with the CSST slitless spectra and a machine-learning method.