The Evolution of Post-Starburst Galaxies from z∼1z\sim1 to the Present

Post-starburst galaxies are in the transitional stage between blue, star-forming galaxies and red, quiescent galaxies, and therefore hold important clues for our understanding of galaxy evolution. In this paper, we systematically searched for and identified a large sample of post-starburst galaxies from the spectroscopic dataset of the Sloan Digital Sky Survey (SDSS) Data Release 9. In total, we found more than 6000 objects with redshifts between $z\sim0.05$ and $z\sim1.3$, making this the largest sample of post-starburst galaxies in the literature. We calculated the luminosity function of the post-starburst galaxies using two uniformly selected subsamples: the SDSS Main Galaxy Sample and the Baryon Oscillation Spectroscopic Survey CMASS Sample. The luminosity functions are reasonably fit by half-Gaussian functions. The peak magnitudes shift as a function of redshift from $M\sim-23.5$ at $z\sim0.8$ to $M\sim-20.3$ at $z\sim0.1$. This is consistent with the downsizing trend, whereby more massive galaxies form earlier than low-mass galaxies. We compared the mass of the post-starburst stellar population found in our sample to the decline of the global star-formation rate and found that only a small amount ($\sim1\%$) of all star-formation quenching in the redshift range $z=0.2-0.7$ results in post-starburst galaxies in the luminosity range our sample is sensitive to. Therefore, luminous post-starburst galaxies are not the place where most of the decline in star-formation rate of the universe is happening.Comment: 26 pages, 24 figures, 8 tables. Accepted for publication in The Astrophysical Journa

The Clustering of High-Redshift (2.9 ≤\leq z ≤\leq 5.1) Quasars in SDSS Stripe 82

We present a measurement of the two-point autocorrelation function of photometrically-selected, high-$z$ quasars over $\sim$ 100 deg$^2$ on the Sloan Digitial Sky Survey Stripe 82 field. Selection is performed using three machine-learning algorithms, trained on known high-$z$ quasar colors, in a six-dimensional, optical/mid-infrared color space. Optical data from the Sloan Digitial Sky Survey is combined with overlapping deep mid-infrared data from the \emph{Spitzer} IRAC Equatorial Survey and the \emph{Spitzer}-HETDEX Exploratory Large-area survey. The selected quasar sample consists of 1378 objects and contains both spectroscopically-confirmed quasars and photometrically-selected quasar candidates. These objects span a redshift range of $2.9 \leq z \leq 5.1$ and are generally fainter than $i=20.2$; a regime which has lacked sufficient number density to perform autocorrelation function measurements of photometrically-classified quasars. We compute the angular correlation function of these data, marginally detecting quasar clustering. We fit a single power-law with an index of $\delta = 1.39 \pm 0.618$ and amplitude of $\theta_0 = 0.71 \pm 0.546$ arcmin. A dark-matter model is fit to the angular correlation function to estimate the linear bias. At the average redshift of our survey ($\langle z \rangle = 3.38$) the bias is $b = 6.78 \pm 1.79$. Using this bias, we calculate a characteristic dark-matter halo mass of 1.70--9.83$\times 10^{12}h^{-1} M_{\odot}$. Our bias estimate suggests that quasar feedback intermittently shuts down the accretion of gas onto the central super-massive black hole at early times. If confirmed, these results hint at a level of luminosity dependence in the clustering of quasars at high-$z$.Comment: 23 Pages, 17 Figure

An upper limit to the dry merger rate at <z> ~ 0.55

We measure the fraction of Luminous Red Galaxies (LRGs) in dynamically close pairs (with projected separation less than 20 $h^{-1}$ kpc and velocity difference less than 500 km s$^{-1}$) to estimate the dry merger rate for galaxies with $-23 < M(r)_{k+e,z=0.2} +5 \log h < -21.5$ and $0.45 < z < 0.65$ in the 2dF-SDSS LRG and QSO (2SLAQ) redshift survey. For galaxies with a luminosity ratio of $1:4$ or greater we determine a $5\sigma$ upper limit to the merger fraction of 1.0% and a merger rate of $< 0.8 \times 10^{-5}$ Mpc$^{-3}$ Gyr$^{-1}$ (assuming that all pairs merge on the shortest possible timescale set by dynamical friction). This is significantly smaller than predicted by theoretical models and suggests that major dry mergers do not contribute to the formation of the red sequence at $z < 0.7$.Comment: 8 pages emulateapj style, 3 figures, accepted by AJ (March 2010

A Simple Likelihood Method for Quasar Target Selection

We present a new method for quasar target selection using photometric fluxes and a Bayesian probabilistic approach. For our purposes we target quasars using Sloan Digital Sky Survey (SDSS) photometry to a magnitude limit of g=22. The efficiency and completeness of this technique is measured using the Baryon Oscillation Spectroscopic Survey (BOSS) data, taken in 2010. This technique was used for the uniformly selected (CORE) sample of targets in BOSS year one spectroscopy to be realized in the 9th SDSS data release. When targeting at a density of 40 objects per sq-deg (the BOSS quasar targeting density) the efficiency of this technique in recovering z>2.2 quasars is 40%. The completeness compared to all quasars identified in BOSS data is 65%. This paper also describes possible extensions and improvements for this techniqueComment: Updated to accepted version for publication in the Astrophysical Journal. 10 pages, 10 figures, 3 table

The Two-Point Correlation of 2QZ Quasars and 2SLAQ LRGs: From a Quasar Fueling Perspective

Public data from the 2dF quasar survey (2QZ) and 2dF/SDSS LRG & QSO (2SLAQ), with their vast reservoirs of spectroscopically located and identified sources, afford us the chance to more accurately study their real space correlations in the hopes of identifying the physical processes that trigger quasar activity. We have used these two public databases to measure the projected cross correlation, $\omega_p$, between quasars and luminous red galaxies. We find the projected two-point correlation to have a fitted clustering radius of $r_0, = 5.3 \pm 0.6$ and a slope, $\gamma =1.83 \pm 0.42$ on scales from 0.7-27$h^{-1}$Mpc. We attempt to understand this strong correlation by separating the LRG sample into 2 populations of blue and red galaxies. We measure at the cross correlation with each population. We find that these quasars have a stronger correlation amplitude with the bluer, more recently starforming population in our sample than the redder passively evolving population, which has a correlation that is much more noisy and seems to flatten on scales $< 5h^{-1}$Mpc. We compare this result to published work on hierarchical models. The stronger correlation of bright quasars with LRGs that have undergone a recent burst of starformation suggests that the physical mechanisms that produce both activities are related and that minor mergers or tidal effects may be important triggers of bright quasar activity and/or that bright quasars are less highly biased than faint quasars.Comment: Accepted for publication in Ap

Sloan Digital Sky Survey III Photometric Quasar Clustering: Probing the Initial Conditions of the Universe using the Largest Volume

The Sloan Digital Sky Survey has surveyed 14,555 square degrees of the sky, and delivered over a trillion pixels of imaging data. We present the large-scale clustering of 1.6 million quasars between z = 0.5 and z = 2.5 that have been classified from this imaging, representing the highest density of quasars ever studied for clustering measurements. This data set spans ~11,000 square degrees and probes a volume of 80(Gpc/h)^3. In principle, such a large volume and medium density of tracers should facilitate high-precision cosmological constraints. We measure the angular clustering of photometrically classified quasars using an optimal quadratic estimator in four redshift slices with an accuracy of ~25% over a bin width of l ~10 - 15 on scales corresponding to matter-radiation equality and larger (l ~ 2 - 30). Observational systematics can strongly bias clustering measurements on large scales, which can mimic cosmologically relevant signals such as deviations from Gaussianity in the spectrum of primordial perturbations. We account for systematics by employing a new method recently proposed by Agarwal et al. (2014) to the clustering of photometrically classified quasars. We carefully apply our methodology to mitigate known observational systematics and further remove angular bins that are contaminated by unknown systematics. Combining quasar data with the photometric luminous red galaxy (LRG) sample of Ross et al. (2011) and Ho et al. (2012), and marginalizing over all bias and shot noise-like parameters, we obtain a constraint on local primordial non-Gaussianity of fNL = -113+/-154 (1\sigma error). [Abridged]Comment: 35 pages, 15 figure

Bayesian High-Redshift Quasar Classification from Optical and Mid-IR Photometry

We identify 885,503 type 1 quasar candidates to i<22 using the combination of optical and mid-IR photometry. Optical photometry is taken from the Sloan Digital Sky Survey-III: Baryon Oscillation Spectroscopic Survey (SDSS-III/BOSS), while mid-IR photometry comes from a combination of data from the Wide-Field Infrared Survey Explorer (WISE) "ALLWISE" data release and several large-area Spitzer Space Telescope fields. Selection is based on a Bayesian kernel density algorithm with a training sample of 157,701 spectroscopically-confirmed type-1 quasars with both optical and mid-IR data. Of the quasar candidates, 733,713 lack spectroscopic confirmation (and 305,623 are objects that we have not previously classified as photometric quasar candidates). These candidates include 7874 objects targeted as high probability potential quasars with 3.5<z<5 (of which 6779 are new photometric candidates). Our algorithm is more complete to z>3.5 than the traditional mid-IR selection "wedges" and to 2.2<z<3.5 quasars than the SDSS-III/BOSS project. Number counts and luminosity function analysis suggests that the resulting catalog is relatively complete to known quasars and is identifying new high-z quasars at z>3. This catalog paves the way for luminosity-dependent clustering investigations of large numbers of faint, high-redshift quasars and for further machine learning quasar selection using Spitzer and WISE data combined with other large-area optical imaging surveys.Comment: 54 pages, 17 figures; accepted by ApJS Data for tables 1 and 2 available at http://www.physics.drexel.edu/~gtr/outgoing/optirqsos/data/master_quasar_catalogs.011414.fits.bz2 and http://www.physics.drexel.edu/~gtr/outgoing/optirqsos/data/optical_ir_quasar_candidates.052015.fits.bz