Max Tegmark, Michael A. Strauss, Michael R. Blanton, Kevork Abazajian, Scott Dodelson, Havard Sandvik, Xiaomin Wang, David H. Weinberg, Idit Zehavi, Neta A. Bahcall, Fiona Hoyle, David Schlegel, Roman Scoccimarro, Michael S. Vogeley, Andreas Berlind, Tamás Budavari, Andrew Connolly, Daniel J. Eisenstein, Douglas Finkbeiner, Joshua A. Frieman, James E. Gunn, Lam Hui, Bhuvnesh Jain, David Johnston, Stephen Kent, Huan Lin, Reiko Nakajima, Robert C. Nichol, Jeremiah P. Ostriker, Adrian Pope, Ryan Scranton, Uroš Seljak, Ravi K. Sheth, Albert Stebbins, Alexander S. Szalay, István Szapudi, Yongzhong Xu, James Annis, J. Brinkmann, Scott Burles, Francisco J. Castander, Istvan Csabai, Jon Loveday, Mamoru Doi, Masataka Fukugita, Bruce Gillespie, Greg Hennessy, David W. Hogg, Željko Ivezić, Gillian R. Knapp, Don Q. Lamb, Brian C. Lee, Robert H. Lupton, Timothy A. McKay, Peter Kunszt, Jeffrey A. Munn, Liam O’Connell, John Peoples, Jeffrey R. Pier, Michael Richmond, Constance Rockosi, Donald P. Schneider, Christopher Stoughton, Douglas L. Tucker, Daniel E. Vanden Berk, Brian Yanny, Donald G. York Department of Physics, University of Pennsylvania, Philadelphia, PA 19104, USA; Dept. of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139; Center for Cosmology and Particle Physics, Department of Physics, New York University, 4 Washington Place, New York, NY 10003; Princeton University Observatory, Princeton, NJ 08544, USA; Department of Physics, Drexel University, Philadelphia, PA 19104, USA; Department of Astronomy, Ohio State University, Columbus, OH 43210, USA; Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL 60510, USA; Center for Cosmological Physics and Department of Astronomy & Astrophysics, University of Chicago, Chicago, IL 60637, USA; Department of Physics and Astronomy, The Johns Hopkins University, 3701 San Martin Drive, Baltimore, MD 21218, USA; University of Pittsburgh, Department of Physics and Astronomy, 3941 O’Hara Street, Pittsburgh, PA 15260, USA; Department of Astronomy, University of Arizona, Tucson, AZ 85721, USA; Department of Physics, 5000 Forbes Avenue, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Institute for Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822, USA; Apache Point Observatory, 2001 Apache Point Rd, Sunspot, NM 88349-0059, USA; Institut d’Estudis Espacials de Catalunya/CSIC, Gran Capita 2-4, 08034 Barcelona, Spain; Sussex Astronomy Centre, University of Sussex, Falmer, Brighton BN1 9QJ, UK; Institute of Astronomy, Univ. of Tokyo, Kashiwa 277-8582, Japan; U.S. Naval Observatory, Flagstaff Station, Flagstaff, AZ 86002-1149, USA; Dept. of Physics, Univ. of Michigan, Ann Arbor, MI 48109-1120, USA; Physics Dept., Rochester Inst. of Technology, 1 Lomb Memorial Dr, Rochester, NY 14623, USA; Dept. of Astronomy and Astrophysics, Pennsylvania State University, University Park, PA 16802, USA; Enrico Fermi Institute, University of Chicago, Chicago, IL 60637, USA; Theoretical Division, MS B285, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA;

We apply clustering-based redshift inference to all extended sources from the Sloan Digital Sky Survey photometric catalogue, down to magnitude r = 22. We map the relationships between colours and redshift, without assumption of the sources' spectral energy distributions (SED). We identify and locate star-forming, quiescent galaxies, and AGN, as well as colour changes due to spectral features, such as the 4000 \AA{} break, redshifting through specific filters. Our mapping is globally in good agreement with colour-redshift tracks computed with SED templates, but reveals informative differences, such as the need for a lower fraction of M-type stars in certain templates. We compare our clustering-redshift estimates to photometric redshifts and find these two independent estimators to be in good agreement at each limiting magnitude considered. Finally, we present the global clustering-redshift distribution of all Sloan extended sources, showing objects up to z ~ 0.8. While the overall shape agrees with that inferred from photometric redshifts, the clustering redshift technique results in a smoother distribution, with no indication of structure in redshift space suggested by the photometric redshift estimates (likely artifacts imprinted by their spectroscopic training set). We also infer a higher fraction of high redshift objects. The mapping between the four observed colours and redshift can be used to estimate the redshift probability distribution function of individual galaxies. This work is an initial step towards producing a general mapping between redshift and all available observables in the photometric space, including brightness, size, concentration, and ellipticity.

The Two-Micron All-Sky Survey (2MASS) has mapped out the low-redshift Universe down to K-S similar to 14 mag. As its near-infrared photometry primarily probes the featureless Rayleigh-Jeans tail of galaxy spectral energy distributions, colour-based redshift estimation is rather uninformative. Until now, redshift estimates for this data set have relied on optical follow-up suffering from selection biases. Here, we use the newly developed technique of clustering-based redshift estimation to infer the redshift distribution of the 2MASS sources regardless of their optical properties. We characterize redshift distributions of objects from the Extended Source Catalogue as a function of near-infrared colours and brightness and report some observed trends. We also apply the clustering redshift technique to dropout populations, sources with non-detections in one or more near-infrared bands, and present their redshift distributions. Combining all extended sources, we confirm with clustering redshifts that the distribution of this sample extends up to z similar to 0.35. We perform a similar analysis with the Point Source Catalogue and show that it can be separated into stellar and extragalactic contributions with galaxies reaching z similar to 0.7. We estimate that the Point Source Catalogue contains 1.6 million extragalactic objects: as many as in the Extended Source Catalogue but probing a cosmic volume 10 times larger.

We investigate the potential and accuracy of clustering-based redshift estimation using the method proposed by M\'enard et al. (2013). This technique enables the inference of redshift distributions from measurements of the spatial clustering of arbitrary sources, using a set of reference objects for which redshifts are known. We apply it to a sample of spectroscopic galaxies from the Sloan Digital Sky Survey and show that, after carefully controlling the sampling efficiency over the sky, we can estimate redshift distributions with high accuracy. Probing the full colour space of the SDSS galaxies, we show that we can recover the corresponding mean redshifts with an accuracy ranging from $\delta$z=0.001 to 0.01. We indicate that this mapping can be used to infer the redshift probability distribution of a single galaxy. We show how the lack of information on the galaxy bias limits the accuracy of the inference and show comparisons between clustering redshifts and photometric redshifts for this dataset. This analysis demonstrates, using real data, that clustering-based redshift inference provides a powerful data-driven technique to explore the redshift distribution of arbitrary datasets, without any prior knowledge on the spectral energy distribution of the sources.

Cross-correlating the Planck High Frequency Instrument (HFI) maps against quasars from the Sloan Digital Sky Survey (SDSS) DR7, we estimate the intensity distribution of the Cosmic Infrared Background (CIB) over the redshift range 0 < z < 5.We detect redshift-dependent spatial cross-correlations between the two datasets using the 857, 545 and 353 GHz channels and we obtain upper limits at 217 GHz consistent with expectations. At all frequencies with detectable signal we infer a redshift distribution peaking around z ~ 1.2 and find the recovered spectrum to be consistent with emission arising from star forming galaxies. By assuming simple modified blackbody and Kennicutt relations, we estimate dust and star formation rate density as a function of redshift, finding results consistent with earlier multiwavelength measurements over a large portion of cosmic history. However, we note that, lacking mid-infrared coverage, we are not able to make an accurate determination of the mean temperature for the dust responsible for the CIB. Our results demonstrate that clustering-based redshift inference is a valuable tool for measuring the entire evolution history of the cosmic star formation rate from a single and homogeneous dataset.

The Sloan Digital Sky Survey has validated and made publicly available its First Data Release. This consists of 2099 square degrees of five-band (u g r i z) imaging data, 186,240 spectra of galaxies, quasars, stars and calibrating blank sky patches selected over 1360 square degrees of this area, and tables of measured parameters from these data. The imaging data go to a depth of r ≈ 22.6 and are photometrically and astrometrically calibrated to 2% rms and 100 milli-arcsec rms per coordinate, respectively. The spectra cover the range 3800–9200Å, with a resolution of 1800–2100. Further characteristics of the data are described, as are the data products themselves. Subject headings: Atlases—Catalogs—Surveys Enrico Fermi Institute, The University of Chicago, 5640 S. Ellis Ave., Chicago, IL 60637 Lawrence Berkeley National Laboratory, One Cyclotron Rd., Berkeley CA 94720-8160 Astronomy Centre, University of Sussex, Falmer, Brighton BN1 9QJ, United Kingdom Department of Physics, University of Michigan, 500 East University Ave., Ann Arbor, MI 48109 Institute for Astronomy Royal Observatory Blackford Hill Edinburgh EH9 3HJ Scotland Department of Physics, University of Pennsylvania, Philadelphia, PA 19104 Department of Physics, Applied Physics, and Astronomy, Rensselaer Polytechnic Institute, Troy, NY 12180 Lucent Technologies, 2701 Lucent Lane, Lisle, IL 60532 Department of Astronomy and Research Center for the Early Universe, School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-0033, Japan Joseph Henry Laboratories, Princeton University, Princeton, NJ 08544 School of Natural Sciences, Institute for Advanced Study, Einstein Drive, Princeton, NJ 08540 Physics Department, Rochester Institute of Technology, 85 Lomb Memorial Drive, Rochester, NY 14623-5603 Department of Astronomy and Astrophysics, the Pennsylvania State University, University Park, PA 16802 University of Zagreb, Department of Physics, Bijenička cesta 32, 10000 Zagreb, Croatia Institute for Astronomy, 2680 Woodlawn Road, Honolulu, HI 96822 University of Wyoming, Dept. of Physics & Astronomy, Laramie, WY 82071 Department of Physics, Drexel University, Philadelphia, PA 19104 Max-Planck-Institut für extraterrestrische Physik, Giessenbachstrasse 1, D-85741 Garching, Germany Department of Astronomy, Ohio State University, Columbus, OH 43210

Ryan Scranton, Andrew J.Connolly, RobertC.Nichol, AlbertStebbins, Istv an Szapudi, DanielJ. Eisenstein, Niayesh Afshordi, Tam asBudavari, Istv an Csabai, Joshua A.Friem an, Jam esE.G unn, David Johnston, Yeong-Shang Loh, RobertH.Lupton, ChristopherJ.M iller, Erin ScottSheldon, RaviK .Sheth, AlexanderS.Szalay, M ax Tegm ark, Yongzhong Xu, ScottF.Anderson, Jim Annis, John Brinkm ann, Neta A.Bahcall, RobertJ.Brunner, M asataka Fukugita, Zeljko Ivezi c, Stephen K ent, Don Q .Lam b, Brian C.Lee, Jon Loveday, Bruce M argon, Tim othy M cK ay, Je rey A.M unn, David Schlegel, Donald P.Schneider, ChrisStoughton, and M ichaelS.Vogeley

We present a data-driven method to infer the redshift distribution of an arbitrary dataset based on spatial cross-correlation with a reference population and we apply it to various datasets across the electromagnetic spectrum to show its potential and limitations. Our approach advocates the use of clustering measurements on all available scales, in contrast to previous works focusing only on linear scales. We also show how its accuracy can be enhanced by optimally sampling a dataset within its photometric space rather than applying the estimator globally. We show that the ultimate goal of this technique is to characterize the mapping between the space of photometric observables and redshift space as this characterization then allows us to infer the clustering-redshift p.d.f. of a single galaxy. We apply this technique to estimate the redshift distributions of luminous red galaxies and emission line galaxies from the SDSS, infrared sources from WISE and radio sources from FIRST. We show that consistent redshift distributions are found using both quasars and absorber systems as reference populations. This technique brings valuable information on the third dimension of astronomical datasets. It is widely applicable to a large range of extra-galactic surveys.

We present a data-driven method to infer the redshift distribution of an arbitrary dataset based on spatial cross-correlation with a reference population and we apply it to various datasets across the electromagnetic spectrum to show its potential and limitations. Our approach advocates the use of clustering measurements on all available scales, in contrast to previous works focusing only on linear scales. We also show how its accuracy can be enhanced by optimally sampling a dataset within its photometric space rather than applying the estimator globally. We show that the ultimate goal of this technique is to characterize the mapping between the space of photometric observables and redshift space as this characterization then allows us to infer the clustering-redshift p.d.f. of a single galaxy. We apply this technique to estimate the redshift distributions of luminous red galaxies and emission line galaxies from the SDSS, infrared sources from WISE and radio sources from FIRST. We show that consistent redshift distributions are found using both quasars and absorber systems as reference populations. This technique brings valuable information on the third dimension of astronomical datasets. It is widely applicable to a large range of extra-galactic surveys.

Determining accurate redshift distributions for very large samples of objects has become increasingly important in cosmology. We investigate the impact of extending cross-correlation based redshift distribution recovery methods to include small scale clustering information. The major concern in such work is the ability to disentangle the amplitude of the underlying redshift distribution from the influence of evolving galaxy bias. Using multiple simulations covering a variety of galaxy bias evolution scenarios, we demonstrate reliable redshift recoveries using linear clustering assumptions well into the non-linear regime for redshift distributions of narrow redshift width. Including information from intermediate physical scales balances the increased information available from clustering and the residual bias incurred from relaxing of linear constraints. We discuss how breaking a broad sample into tomographic bins can improve estimates of the redshift distribution, and present a simple bias removal technique using clustering information from the spectroscopic sample alone.

Using about 450?000 galaxies in the Deep Lens Survey, we present a detection of the gravitational magnification of z > 4 Lyman-break galaxies by massive foreground galaxies with 0.4 < z < 1.0, grouped by redshift. The magnification signal is detected at a signal-to-noise ratio greater than 20, and rigorous checks confirm that it is not contaminated by any galaxy sample overlap in redshift. The inferred galaxy mass profiles are consistent with earlier lensing analyses at lower redshift. We then explore the tomographic lens magnification signal by splitting our foreground galaxy sample into seven redshift bins. Combining galaxy-magnification cross-correlations and galaxy angular autocorrelations, we develop a bias-independent estimator of the tomographic signal. As a diagnostic of magnification tomography, the measurement of this estimator rejects a flat, dark matter-dominated universe at >7.5s with a fixed s8 and is found to be consistent with the expected redshift dependence of the Wilkinson Microwave Anisotropy Probe 7 ? cold dark matter cosmology.

We constrain the linear and quadratic bias parameters from the configuration dependence of the three-point correlation function (3PCF) in both redshift and projected space, utilizing measurements of spectroscopic galaxies in the Sloan Digital Sky Survey (SDSS) Main Galaxy Sample. We show that bright galaxies (M_r < -21.5) are biased tracers of mass, measured at a significance of 4.5 sigma in redshift space and 2.5 sigma in projected space by using a thorough error analysis in the quasi-linear regime (9-27 Mpc/h). Measurements on a fainter galaxy sample are consistent with an unbiased model. We demonstrate that a linear bias model appears sufficient to explain the galaxy-mass bias of our samples, although a model using both linear and quadratic terms results in a better fit. In contrast, the bias values obtained from the linear model appear in better agreement with the data by inspection of the relative bias, and yield implied values of sigma_8 that are more consistent with current constraints. We investigate the covariance of the 3PCF, which itself is a measurement of galaxy clustering. We assess the accuracy of our error estimates by comparing results from mock galaxy catalogs to jackknife re-sampling methods. We identify significant differences in the structure of the covariance. However, the impact of these discrepancies appears to be mitigated by an eigenmode analysis that can account for the noisy, unresolved modes. Our results demonstrate that using this technique is sufficient to remove potential systematics even when using less-than-ideal methods to estimate errors.

We present an analysis of bar length measurements of 3150 local galaxies in a volume limited sample of low redshift (z < 0.06) disk galaxies. Barred galaxies were initially selected from the Galaxy Zoo 2 project, and the lengths and widths of the bars were manually drawn by members of the Galaxy Zoo community using a Google Maps interface. Bars were measured independently by different observers, multiple times per galaxy (>=3), and we find that observers were able to reproduce their own bar lengths to 3% and each others' to better than 20%. We find a "color bimodality" in our disk galaxy population with bar length, i.e., longer bars inhabit redder disk galaxies and the bars themselves are redder, and that the bluest galaxies host the smallest galactic bars (< 5 kpc/h). We also find that bar and disk colors are clearly correlated, and for galaxies with small bars, the disk is, on average, redder than the bar colors, while for longer bars the bar then itself is redder, on average, than the disk. We further find that galaxies with a prominent bulge are more likely to host longer bars than those without bulges. We categorise our galaxy populations by how the bar and/or ring are connected to the spiral arms. We find that galaxies whose bars are directly connected to the spiral arms are preferentially bluer and that these galaxies host typically shorter bars. Within the scatter, we find that stronger bars are found in galaxies which host a ring (and only a ring). The bar length and width measurements used herein are made publicly available for others to use (http://data.galaxyzoo.org).

Similar to the magnification of the galaxies' fluxes by gravitational lensing, the extinction of the fluxes by comic dust, whose existence is recently detected by [B. Menard, R. Scranton, M. Fukugita, and G. Richards, Mon. Not. R. Astron. Soc. 405, 1025 (2010).], also modifies the distribution of a flux-selected galaxy sample. We study the anisotropic distortion by dust extinction to the 3D galaxy correlation function, including magnification bias and redshift distortion at the same time. We find the extinction distortion is most significant along the line of sight and at large separations, similar to that by magnification bias. The correction from dust extinction is negative except at sufficiently large transverse separations, which is almost always opposite to that from magnification bias (we consider a number count slope s > 0.4). Hence, the distortions from these two effects tend to reduce each other. At low z (less than or similar to 1), the distortion by extinction is stronger than that by magnification bias, but at high z, the reverse holds. We also study how dust extinction affects probes in real space of the baryon acoustic oscillations (BAO) and the linear redshift distortion parameter beta. We find its effect on BAO is negligible. However, it introduces a positive scale-dependent correction to beta that can be as large as a few percent. At the same time, we also find a negative scale-dependent correction from magnification bias, which is up to percent level at low z, but to similar to 40% at high z. These corrections are non-negligible for precision cosmology, and should be considered when testing General Relativity through the scale-dependence of beta

We measure the UV-optical colour dependence of galaxy clustering in the local Universe. Using the clean separation of the red and blue sequences made possible by the NUV - r colour-magnitude diagram, we segregate the galaxies into red, blue and intermediate 'green' classes. We explore the clustering as a function of this segregation by removing the dependence on luminosity and by excluding edge-on galaxies as a means of a non-model dependent veto of highly extincted galaxies. We find that xi(r(p), pi) for both red and green galaxies shows strong redshift-space distortion on small scales - the 'finger-of-God' effect, with green galaxies having a lower amplitude than is seen for the red sequence, and the blue sequence showing almost no distortion. On large scales, xi(r(p), pi) for all three samples show the effect of large-scale streaming from coherent infall. On scales of 1 h-1 Mpc < r(p) < 10 h-1 Mpc, the projected auto-correlation function w(p)(r(p)) for red and green galaxies fits a power law with slope gamma similar to 1.93 and amplitude r(0) similar to 7.5 and 5.3, compared with gamma similar to 1.75 and r(0) similar to 3.9 h-1 Mpc for blue sequence galaxies. Compared to the clustering of a fiducial L* galaxy, the red, green and blue have a relative bias of 1.5, 1.1 and 0.9, respectively. The w(p)(r(p)) for blue galaxies display an increase in convexity at similar to 1 h-1 Mpc, with an excess of large-scale clustering. Our results suggest that the majority of blue galaxies are likely central galaxies in less massive haloes, while red and green galaxies have larger satellite fractions, and preferentially reside in virialized structures. If blue sequence galaxies migrate to the red sequence via processes like mergers or quenching that take them through the green valley, such a transformation may be accompanied by a change in environment in addition to any change in luminosity and colour.

Supernova measurements have become a key ingredient in current determinations of cosmological parameters. These sources can however be used as standard candles only after correcting their apparent brightness for a number of effects. In this paper we discuss some limitations imposed by the formalism currently used for such corrections and investigate the impact on cosmological constraints. We show that color corrections are, in general, expected to be biased. In addition, color excesses which do not add a significant scatter to the observed SN brightnesses affect the value of cosmological parameters but leave the slope of the color-luminosity relation unchanged. We quantify these biases in the context of the redshift-dependent dust extinction suggested by the recent detection of intergalactic dust by Menard et al. (2009). Using a range of models for the opacity of the Universe as a function of redshift, we find that color-magnitude-stretch scaling relations are virtually insensitive to the presence of cosmic dust while cosmological parameters such as Omega_M and w are biased at the level of a few percent, i.e. offsets comparable to the current statistical errors. Future surveys will be able to limit the impact of intergalactic extinction by observing at larger wavelengths. In addition such datasets will provide direct detections of intergalactic dust by cross-correlating SN colors and the density of foreground galaxies, which can be used as a consistency check on the cosmic dust extinction correction. Alternatively, such biases could be avoided by correcting the colors of supernovae on an object-by-object basis with accurate photometry.