Automated Separation of Stars and Normal Galaxies Based on Statistical Mixture Modeling with RBF Neural Networks

For LAMOST, the largest sky survey program in China, the solution of the problem of automatic discrimination of stars from galaxies by spectra has shown that the results of the PSF test can be significantly refined. However, the problem is made worse when the redshifts of galaxies are not available. We present a new automatic method of star/(normal) galaxy separation, which is based on Statistical Mixture Modeling with Radial Basis Function Neural Networks (SMM-RBFNN). This work is a continuation of our previous one, where active and non-active celestial objects were successfully segregated. By combining the method in this paper and the previous one, stars can now be effectively separated from galaxies and AGNs by their spectra-a major goal of LAMOST, and an indispensable step in any automatic spectrum classification system. In our work, the training set includes standard stellar spectra from Jacoby's spectrum library and simulated galaxy spectra of EO, SO, Sa, Sb types with redshift ranging from 0 to 1     

On the self-consistency of evolutionary synthesis models

Evolutionary synthesis models have been used to study the physical properties of unresolved populations in a wide range of scenarios. Unfortunately, their self-consistency is difficult to test and there are some theoretical open questions without an answer: (1) The change of the homology relations assumed in the computation of isochrones due to the effect of stellar winds (or rotation) and the discontinuities in the stellar evolution are not considered. (2) There is no consensus about how the isochrones must be integrated. (3) The discreteness of the stellar populations (that produce an intrinsic statistical dispersion) usually are not taken into account, and model results are interpreted in a deterministic way instead of a statistical one. The objective of this contribution is to present some inconsistencies in the computation and some cautions in the application of the results of such codes. This revised version was published online in August 2006 with corrections to the Cover Date.     

Searching for an intrinsic stellar population in compact high-velocity clouds

We are investigating the hypothesis that Compact High-Velocity Clouds (CHVCs) are the left-over building blocks of Local Group galaxies. To this end, we are searching for their embedded stellar populations using FORS at the VLT. The search is done with single-star photometry in V and I bands, which is sensitive to both, young and old, stellar populations. Five CHVCs of our sample have been observed so far down to I=24. We pointed the VLT towards the highest HI column density regions, as determined in Effelsberg radio data. In an alternate approach, we searched 2MASS public data towards those 5 CHVCs down to K=16. While the VLT data probe the central regions out to distance moduli of about 27, the 2MASS data are sensitive to a population of red giant stars to distance moduli of about 20. The 2MASS data, on the other hand, cover a much wider field of view than the VLT data (radius of 1 degree versus FORS field of 6.8 arcmin). We did not find a stellar population intrinsic to the CHVCs in either data. In this paper, we illustrate our search methods. This revised version was published online in August 2006 with corrections to the Cover Date.