First Measurement of the Clustering Evolution of Photometrically-Classified Quasars

We present new measurements of the quasar autocorrelation from a sample of \~80,000 photometrically-classified quasars taken from SDSS DR1. We find a best-fit model of $\omega(\theta) = (0.066\pm^{0.026}_{0.024})\theta^{-(0.98\pm0.15)}$ for the angular autocorrelation, consistent with estimates from spectroscopic quasar surveys. We show that only models with little or no evolution in the clustering of quasars in comoving coordinates since z~1.4 can recover a scale-length consistent with local galaxies and Active Galactic Nuclei (AGNs). A model with little evolution of quasar clustering in comoving coordinates is best explained in the current cosmological paradigm by rapid evolution in quasar bias. We show that quasar biasing must have changed from b_Q~3 at a (photometric) redshift of z=2.2 to b_Q~1.2-1.3 by z=0.75. Such a rapid increase with redshift in biasing implies that quasars at z~2 cannot be the progenitors of modern L* objects, rather they must now reside in dense environments, such as clusters. Similarly, the duration of the UVX quasar phase must be short enough to explain why local UVX quasars reside in essentially unbiased structures. Our estimates of b_Q are in good agreement with recent spectroscopic results, which demonstrate the implied evolution in b_Q is consistent with quasars inhabiting halos of similar mass at every redshift. Treating quasar clustering as a function of both redshift and luminosity, we find no evidence for luminosity dependence in quasar clustering, and that redshift evolution thus affects quasar clustering more than changes in quasars' luminosity. We provide a new method for quantifying stellar contamination in photometrically-classified quasar catalogs via the correlation function.Comment: 34 pages, 10 figures, 1 table, Accepted to ApJ after: (i) Minor textual changes; (ii) extra points added to Fig.

The Statistical Approach to Quantifying Galaxy Evolution

Studies of the distribution and evolution of galaxies are of fundamental importance to modern cosmology; these studies, however, are hampered by the complexity of the competing effects of spectral and density evolution. Constructing a spectroscopic sample that is able to unambiguously disentangle these processes is currently excessively prohibitive due to the observational requirements. This paper extends and applies an alternative approach that relies on statistical estimates for both distance (z) and spectral type to a deep multi-band dataset that was obtained for this exact purpose. These statistical estimates are extracted directly from the photometric data by capitalizing on the inherent relationships between flux, redshift, and spectral type. These relationships are encapsulated in the empirical photometric redshift relation which we extend to z ~ 1.2, with an intrinsic dispersion of dz = 0.06. We also develop realistic estimates for the photometric redshift error for individual objects, and introduce the utilization of the galaxy ensemble as a tool for quantifying both a cosmological parameter and its measured error. We present deep, multi-band, optical number counts as a demonstration of the integrity of our sample. Using the photometric redshift and the corresponding redshift error, we can divide our data into different redshift intervals and spectral types. As an example application, we present the number redshift distribution as a function of spectral type.Comment: 40 pages (LaTex), 21 Figures, requires aasms4.sty; Accepted by the Astrophysical Journa

Self-perceived physical health predicts cardiovascular disease incidence and death among postmenopausal women

BACKGROUND: Physical and Mental Component Summary (PCS, MCS, respectively) scales of SF- 36 health-related-quality-of-life have been associated with all-cause and cardiovascular disease (CVD) mortality. Their relationships with CVD incidence are unclear. This study purpose was to test whether PCS and/or MCS were associated with CVD incidence and death. METHODS: Postmenopausal women (aged 50–79 years) in control groups of the Women’s Health Initiative clinical trials (n = 20,308) completed the SF-36 and standardized questionnaires at trial entry. Health outcomes, assessed semi-annually, were verified with medical records. Cox regressions assessed time to selected outcomes during the trial phase (1993–2005). RESULTS: A total of 1075 incident CVD events, 204 CVD-specific deaths, and 1043 total deaths occurred during the trial phase. Women with low versus high baseline PCS scores had less favorable health profiles at baseline. In multivariable models adjusting for baseline confounders, participants in the lowest PCS quintile (reference = highest quintile) exhibited 1.8 (95%CI: 1.4, 2.3), 4.7 (95%CI: 2.3, 9.4), and 2.1 (95%CI: 1.7, 2.7) times greater risk of CVD incidence, CVD-specific death, and total mortality, respectively, by trial end; whereas, MCS was not significantly associated with CVD incidence or death. CONCLUSION: Physical health, assessed by self-report of physical functioning, is a strong predictor of CVD incidence and death in postmenopausal women; similar self-assessment of mental health is not. PCS should be evaluated as a screening tool to identify older women at high risk for CVD development and death

Clustering Analyses of 300,000 Photometrically Classified Quasars--I. Luminosity and Redshift Evolution in Quasar Bias

Using ~300,000 photometrically classified quasars, by far the largest quasar sample ever used for such analyses, we study the redshift and luminosity evolution of quasar clustering on scales of ~50 kpc/h to ~20 Mpc/h from redshifts of z~0.75 to z~2.28. We parameterize our clustering amplitudes using realistic dark matter models, and find that a LCDM power spectrum provides a superb fit to our data with a redshift-averaged quasar bias of b_Q = 2.41+/-0.08 ($P_{<\chi^2}=0.847$) for $\sigma_8=0.9$. This represents a better fit than the best-fit power-law model ($\omega = 0.0493\pm0.0064\theta^ {-0.928\pm0.055}$; $P_{<\chi^2}=0.482$). We find b_Q increases with redshift. This evolution is significant at >99.6% using our data set alone, increasing to >99.9999% if stellar contamination is not explicitly parameterized. We measure the quasar classification efficiency across our full sample as a = 95.6 +/- ^{4.4}_{1.9}%, a star-quasar separation comparable with the star-galaxy separation in many photometric studies of galaxy clustering. We derive the mean mass of the dark matter halos hosting quasars as MDMH=(5.2+/-0.6)x10^{12} M_solar/h. At z~1.9 we find a $1.5\sigma$ deviation from luminosity-independent quasar clustering; this suggests that increasing our sample size by a factor of 1.8 could begin to constrain any luminosity dependence in quasar bias at z~2. Our results agree with recent studies of quasar environments at z < 0.4, which detected little luminosity dependence to quasar clustering on proper scales >50 kpc/h. At z < 1.6, our analysis suggests that b_Q is constant with luminosity to within ~0.6, and that, for g < 21, angular quasar autocorrelation measurements are unlikely to have sufficient statistical power at z < 1.6 to detect any luminosity dependence in quasars' clustering.Comment: 13 pages, 9 figures, 2 tables; uses amulateapj; accepted to Ap

High-Redshift Quasars Found in Sloan Digital Sky Survey Commissioning Data IV: Luminosity Function from the Fall Equatorial Stripe Sampl

This is the fourth paper in a series aimed at finding high-redshift quasars from five-color imaging data taken along the Celestial Equator by the SDSS. during its commissioning phase. In this paper, we use the color-selected sample of 39 luminous high-redshift quasars presented in Paper III to derive the evolution of the quasar luminosity function over the range of 3.6<z<5.0, and -27.5<M_1450<-25.5 (Omega=1, H_0=50 km s^-1 Mpc^-1). We use the selection function derived in Paper III to correct for sample incompleteness. The luminosity function is estimated using three different methods: (1) the 1/V_a estimator; (2) a maximum likelihood solution, assuming that the density of quasars depends exponentially on redshift and as a power law in luminosity and (3) Lynden-Bell's non-parametric C^- estimator. All three methods give consistent results. The luminous quasar density decreases by a factor of ~ 6 from z=3.5 to z=5.0, consistent with the decline seen from several previous optical surveys at z<4.5. The luminosity function follows psi(L) ~ L^{-2.5} for z~4 at the bright end, significantly flatter than the bright end luminosity function psi(L) \propto L^{-3.5} found in previous studies for z<3, suggesting that the shape of the quasar luminosity function evolves with redshift as well, and that the quasar evolution from z=2 to 5 cannot be described as pure luminosity evolution. Possible selection biases and the effect of dust extinction on the redshift evolution of the quasar density are also discussed.Comment: AJ accepted, with minor change

The Sloan Digital Sky Survey Quasar Lens Search. II. Statistical lens sample from the third data release

We report the first results of our systematic search for strongly lensed quasars using the spectroscopically confirmed quasars in the Sloan Digital Sky Survey (SDSS). Among 46,420 quasars from the SDSS Data Release 3 (~4188 deg^2), we select a subsample of 22,683 quasars that are located at redshifts between 0.6 and 2.2 and are brighter than the Galactic extinction-corrected i-band magnitude of 19.1. We identify 220 lens candidates from the quasar subsample, for which we conduct extensive and systematic follow-up observations in optical and near-infrared wavebands, in order to construct a complete lensed quasar sample at image separations between 1" and 20" and flux ratios of faint to bright lensed images larger than 10^(−0.5). We construct a statistical sample of 11 lensed quasars. Ten of these are galaxy-scale lenses with small image separations (~ 1"-2") and one is a large separation (15") system which is produced by a massive cluster of galaxies, representing the first statistical sample of lensed quasars including both galaxy- and cluster-scale lenses. The Data Release 3 spectroscopic quasars contain an additional 11 lensed quasars outside the statistical sample

Data Mining and Machine Learning in Astronomy

We review the current state of data mining and machine learning in astronomy. 'Data Mining' can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black-box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those where data mining techniques directly resulted in improved science, and important current and future directions, including probability density functions, parallel algorithms, petascale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm, and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.Comment: Published in IJMPD. 61 pages, uses ws-ijmpd.cls. Several extra figures, some minor additions to the tex