1,429 research outputs found
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 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 () for . This represents a better
fit than the best-fit power-law model (; ). 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 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
- …