The Difference between the α-disks of Seyfert 1 Galaxiesand Quasars

In a previous paper, it was suggested that contamination of the nuclear luminosity by the host galaxy plays an important role in determining the parameters of the standard a disk of AGNs. Using the nuclear absolute B band magnitude instead of the total absolute B band magnitude, we have recalculated the central black hole masses, accretion rates and disk inclinations for 20 Seyfert 1 galaxies and 17 Palomar-Green (PG) quasars. It is found that a small value of a is needed for the Seyfert 1 galaxies than for the PG quasars. This difference in a possibly leads to the different properties of Seyfert 1 galaxies and quasars. Furthermore, we find most of the objects in this sample are not accreting at super-Eddington rates if we adopt the nuclear optical luminosity in our calculation.     

Steps Towards a Fully Automated Classification and Redshiftmeasurement Pipeline for LAMOST Spectra. I. Continuum level and wavelength estimation for galaxies

1 INTRoDUCTIONThere are aiready several wide-field multi-fiber spectrograPhic surveys in various stages ofdevelopment, most notably the stellar popu1ation, galaxy and QSO suxveys using the 2dF faciLity as described by Lewis et al. (1998) and the Sloan Digital Sky Survey (SDSS) aJs summarizedby York et al. (2000), both of which are already underway, and those surveys planning to usethe 6dF facility as described by Wason et al. (2000) and the LAMOST facility as outllnedby Wang et a…     

Gamma-ray Bursts： a Probe of Black Holes

There is strong evidence for the existence of black holes (BHs) in some X-ray binaries and in most galactic nuclei based on different types of measurement,but black holes have not been definitely identified for the lack of very firm observational evidence up to now. Because direct evidence for BHs should come from determination of strong gravitational redshift, we expect an object can fall into the region near the BH horizon where radiation can be detected. Therefore the object must be a compact star such as a neutron star (NS), and intense astrophysical processes will release highly energetic radiation that is transient and fast-varying.These characteristics may point to the observed gamma-ray bursts (GRBs). Recent observations of iron lines suggest that afterglows of GRBs show properties similar to those observed in active galactic nuclei (AGNs), implying that the GRBs may originate from intense events related to black holes. A model for GRBs and afterglows is proposed here to obtain the range of gravitational redshifts (Zg) of GRBs with known cosmological redshifts. Here, we provide a new method that, with a search for high-energy emission lines (X- or γ-rays) in GRBs, one can determine the gravitational redshift. We expect Zg to be 0.5 or even larger, so we can rule out the possibility of other compact objects such as NSs, and identify the central progenitors of GRBs as black holes.     

Automated Separation os Stars and Normal Galaxies Based on Statistical Mixture Modeling with RBF Neural Net—works

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 netrworks(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 1.2,and the test set of stellar spectra from Pickles‘atlas and SDSS spectra of normal galaxies with SNR of 13.Experiments show that our SMM-RBFNN is more efficient in both the training and testing stages than the BPNN(back propagation neural networks),and more importantly,it can achieve a good classification accuracy of 99.22% and 96.52%,respectively for stars and normal galaxies.