Photometric Catalogue of Quasars and Other Point Sources in the Sloan Digital Sky Survey

We present a catalogue of about 6 million unresolved photometric detections in the Sloan Digital Sky Survey Seventh Data Release classifying them into stars, galaxies and quasars. We use a machine learning classifier trained on a subset of spectroscopically confirmed objects from 14th to 22nd magnitude in the SDSS {\it i}-band. Our catalogue consists of 2,430,625 quasars, 3,544,036 stars and 63,586 unresolved galaxies from 14th to 24th magnitude in the SDSS {\it i}-band. Our algorithm recovers 99.96% of spectroscopically confirmed quasars and 99.51% of stars to i $\sim$21.3 in the colour window that we study. The level of contamination due to data artefacts for objects beyond $i=21.3$ is highly uncertain and all mention of completeness and contamination in the paper are valid only for objects brighter than this magnitude. However, a comparison of the predicted number of quasars with the theoretical number counts shows reasonable agreement.Comment: 16 pages, Ref. No. MN-10-2382-MJ.R2, accepted for publication in MNRAS Main Journal, April 201

Correlation function of quasars in real and redshift space from the Sloan Digital Sky Survey Data Release 7

We analyze the quasar two-point correlation function (2pCF) within the redshift interval $0.8<z<2.2$ using a sample of 52303 quasars selected from the recent 7th Data Release of the Sloan Digital Sky Survey. Our approach to 2pCF uses a concept of locally Lorentz (Fermi) frame for determination of the distance between objects and permutation method of the random catalogue generation. Assuming the spatially flat cosmological model with given $\Omega_{\Lambda}=0.726$, we found that the real-space 2pCF is fitted well with the power-low model within the distance range $1<\sigma<35$ $h^{-1}$ Mpc with the correlation length $r_{0}=5.85\pm0.33$ $h^{-1}$ Mpc and the slope $\gamma=1.87\pm0.07$. The redshift-space 2pCF is approximated with $s_{0}=6.43\pm0.63$ $h^{-1}$ Mpc and $\gamma=1.21\pm0.24$ for $1<s<10$ $h^{-1}$ Mpc, and $s_{0}=7.37\pm0.81$ $h^{-1}$ Mpc and $\gamma=1.90\pm0.24$ for $1010\,h^{-1}$ Mpc the parameter describing the large-scale infall to density inhomogeneities is $\beta=0.63\pm0.10$ with the linear bias $b=1.44\pm0.22$ that marginally (within 2$\sigma$) agrees with the linear theory of cosmological perturbations. We discuss possibilities to obtain a statistical estimate of the random component of quasars velocities (different from the large-scale infall). We note rather slight dependence of quasars velocity dispersion upon the 2pCF parameters in the region $r<2$ Mpc.Comment: 15 pages, 17 figures, online published in MNRAS; final version to match the published versio

Near-Infrared Colours of Active Galactic Nuclei

We propose near-infrared colour selection criteria to extract Active Galactic Nuclei (AGNs) using the near-infrared Colour-Colour Diagram (CCD) and predict near-infrared colour evolution with respect to redshift. First, we cross-identify two AGN catalogues with the 2MASS Point Source Catalogue, and confirm both the loci of quasars/AGNs in the near-infrared CCD and redshift-colour relations. In the CCD, the loci of over 70 - 80% of AGNs can be distinguished from the stellar locus. To examine the colours of quasars, we simulate near-infrared colours using Hyperz code. Assuming a realistic quasar SED, we derive simulated near-infrared colours of quasars with redshift (up to z ~ 11). The simulated colours can reproduce not only the redshift-colour relations but also the loci of quasars/AGNs in the near-infrared CCD. We finally discuss the possibility of contamination by other types of objects. We compare the locus of AGNs with the other four types of objects (namely, microquasars, CVs, LMXBs, and MYSOs) which have a radiation mechanism similar to that of AGNs. In the near-infrared CCD, each type of object is located at a position similar to the stellar locus. Accordingly, it is highly probable that the four types of objects can be distinguished on the basis of the locus in a near-infrared CCD. We additionally consider contamination by distant normal galaxies. The near-infrared colours of several types of galaxies are also simulated using the Hyperz code. Although galaxies with z ~ 1 have near-infrared colours similar to those of AGNs, these galaxies are unlikely to be detected because they are very faint. In other words, few galaxies should contaminate the locus of AGNs in the near-infrared CCD. Consequently, we can extract reliable AGN candidates on the basis of the near-infrared CCD.Comment: 7 pages, 6 figures, Accepted for publication in A&

Estimating Photometric Redshifts of Quasars via K-nearest Neighbor Approach Based on Large Survey Databases

We apply one of lazy learning methods named k-nearest neighbor algorithm (kNN) to estimate the photometric redshifts of quasars, based on various datasets from the Sloan Digital Sky Survey (SDSS), UKIRT Infrared Deep Sky Survey (UKIDSS) and Wide-field Infrared Survey Explorer (WISE) (the SDSS sample, the SDSS-UKIDSS sample, the SDSS-WISE sample and the SDSS-UKIDSS-WISE sample). The influence of the k value and different input patterns on the performance of kNN is discussed. kNN arrives at the best performance when k is different with a special input pattern for a special dataset. The best result belongs to the SDSS-UKIDSS-WISE sample. The experimental results show that generally the more information from more bands, the better performance of photometric redshift estimation with kNN. The results also demonstrate that kNN using multiband data can effectively solve the catastrophic failure of photometric redshift estimation, which is met by many machine learning methods. By comparing the performance of various methods for photometric redshift estimation of quasars, kNN based on KD-Tree shows its superiority with the best accuracy for our case.Comment: 28 pages, 4 figures, 3 tables, accepted for publication in A

ASPECT: A spectra clustering tool for exploration of large spectral surveys

We present the novel, semi-automated clustering tool ASPECT for analysing voluminous archives of spectra. The heart of the program is a neural network in form of Kohonen's self-organizing map. The resulting map is designed as an icon map suitable for the inspection by eye. The visual analysis is supported by the option to blend in individual object properties such as redshift, apparent magnitude, or signal-to-noise ratio. In addition, the package provides several tools for the selection of special spectral types, e.g. local difference maps which reflect the deviations of all spectra from one given input spectrum (real or artificial). ASPECT is able to produce a two-dimensional topological map of a huge number of spectra. The software package enables the user to browse and navigate through a huge data pool and helps him to gain an insight into underlying relationships between the spectra and other physical properties and to get the big picture of the entire data set. We demonstrate the capability of ASPECT by clustering the entire data pool of 0.6 million spectra from the Data Release 4 of the Sloan Digital Sky Survey (SDSS). To illustrate the results regarding quality and completeness we track objects from existing catalogues of quasars and carbon stars, respectively, and connect the SDSS spectra with morphological information from the GalaxyZoo project.Comment: 15 pages, 14 figures; accepted for publication in Astronomy and Astrophysic