Deuterated hydrogen molecule and search for early structure-formation signatures in the Universe

Possible detection of signatures of structure formation at the end of the 'dark age' epoch  ( z ∼ 40–20)  is examined. We discuss the spectral–spatial fluctuations in the cosmic microwave background radiation (CMBR) temperature produced by elastic resonant scattering of CMBR photons on deuterated hydrogen (HD) molecules located in protostructures moving with peculiar velocity. Detailed chemical kinematic evolution of HD molecules in the expanding homogeneous medium is calculated. Then, the HD abundances are linked to protostructures at their maximum expansion, whose properties are estimated by using the top-hat spherical approach and the Λ cold dark matter (ΛCDM) cosmology. We find that the optical depths in the HD three lowest pure rotational lines for high-peak protohaloes at their maximum expansion are much higher than those in LiH molecule. The corresponding spectral–spatial fluctuation amplitudes, however, are probably too weak to be detected by current and forthcoming millimetre telescope facilities. We extend our estimates of spectral–spatial fluctuations to gas clouds inside collapsed CDM haloes by using results from a crude model of HD production in these clouds. The fluctuations for the highest peak CDM haloes at redshifts ∼20–30 could be detected in the future. Observations will be important to test model predictions of early structure formation in the Universe.     

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     

Cosmological constraints from Chandra X-ray observations of galaxy clusters

Chandra X-ray observations of rich, dynamically relaxed galaxy clusters allow the properties of the X-ray gas and the total gravitating mass to be determined precisely. Here, we discuss how Chandra observations may be used as a powerful tool for cosmological studies. By combining Chandra X-ray results on the X-ray gas mass fractions in clusters with independent measurements of the Hubble constant and the mean baryonic matter density of the universe, we obtain a tight constraint on the mean total matter density of the universe, Ο_m, and an interesting constraint on the cosmological constant, Ο_Λ. Using these results, together with the observed local X-ray luminosity function of the most X-ray luminous galaxy clusters, a mass-luminosity relation determined from Chandra and ROSAT X-ray data and weak gravitational lensing observations, and the mass function predicted by numerical simulations, we obtain a precise constraint on the normalization of the power spectrum of density fluctuations in the nearby universe,σ₈. We compare our results with those obtained from other, independent methods. This revised version was published online in July 2006 with corrections to the Cover Date.