A PCA approach to stellar abundances I. testing of the method validity

The derivation of element abundances of stars is a key step in detailed spectroscopic analysis. A spectroscopic method may suffer from errors associated with model simplifications. We have developed a new method of deriving the various element abundances of stars based on the calibration established from a group of standard stars. We perform principal component analysis (PCA) on a homogeneous library of stellar spectra, and then use machine learning to calibrate the relationship between principal components and element abundances. By testing with spectral libraries S4N and MILES, we find that our procedure provides good consistency when spectra from a homogeneous set of observations are used, and it could be expanded to stars with quite a wide range of stellar parameters, with both dwarfs and giants. Moreover, we discuss the four key factors that have a significant impact on the results of derived element abundances, including the resolution of the spectra, wavelength range, the signal-to-noise ratio (S/N) of spectra and the number of principal components adopted.

Searching for Multiple Populations in Star Clusters Using the China Space Station Telescope

Multiple stellar populations(MPs) in most star clusters older than 2 Gyr, as seen by lots of spectroscopic and photometric studies, have led to a significant challenge to the traditional view of star formation. In this field, spacebased instruments, in particular the Hubble Space Telescope(HST), have made a breakthrough as they significantly improved the efficiency of detecting MPs in crowded stellar fields by images. The China Space Station Telescope(CSST) and the HST are sensitive to a similar wavelength interval, but the CSST covers a field of view which is about 5–8 times wider than that of HST. One of its instruments, the Multi-Channel Imager(MCI),will have multiple filters covering a wide wavelength range from NUV to NIR, making the CSST a potentially powerful tool for studying MPs in clusters. In this work, we evaluate the efficiency of the designed filters for the MCI/CSST in revealing MPs in different color–magnitude diagrams(CMDs). We find that CMDs made with MCI/CSST photometry in appropriate UV filters are powerful tools to disentangle stellar populations with different abundances of He, C, N, O and Mg. On the contrary, the traditional CMDs are blind to multiple populations in globular clusters(GCs). We show that CSST has the potential of being the spearhead instrument for investigating MPs in GCs in the next decades.