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Register a new userThe SDSS Coadd: A Galaxy Photometric Redshift Catalog
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We present and describe a catalog of galaxy photometric redshifts (photo-zs) for the Sloan Digital Sky Survey (SDSS) Coadd Data. We use the Artificial Neural Network (ANN) technique to calculate photo-zs and the Nearest Neighbor Error (NNE) method to estimate photo-z errors for $sim$ 13 million objects classified as galaxies in the coadd with $r < 24.5$. The photo-z and photo-z error estimators are trained and validated on a sample of $sim 83,000$ galaxies that have SDSS photometry and spectroscopic redshifts measured by the SDSS Data Release 7 (DR7), the Canadian Network for Observational Cosmology Field Galaxy Survey (CNOC2), the Deep Extragalactic Evolutionary Probe Data Release 3(DEEP2 DR3), the VIsible imaging Multi-Object Spectrograph - Very Large Telescope Deep Survey (VVDS) and the WiggleZ Dark Energy Survey. For the best ANN methods we have tried, we find that 68% of the galaxies in the validation set have a photo-z error smaller than $sigma_{68} =0.031$. After presenting our results and quality tests, we provide a short guide for users accessing the public data.
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Stripe 82 in the Sloan Digital Sky Survey was observed multiple times, allowing deeper images to be constructed by coadding the data. Here we analyze the ellipticities of background galaxies in this 275 square degree region, searching for evidence of distortions due to cosmic shear. The E-mode is detected in both real and Fourier space with $>5$-$sigma$ significance on degree scales, while the B-mode is consistent with zero as expected. The amplitude of the signal constrains the combination of the matter density $Omega_m$ and fluctuation amplitude $sigma_8$ to be $Omega_m^{0.7}sigma_8 = 0.252^{+0.032}_{-0.052}$.
We present details of the construction and characterization of the coaddition of the Sloan Digital Sky Survey Stripe 82 ugriz imaging data. This survey consists of 275 deg$^2$ of repeated scanning by the SDSS camera of $2.5arcdeg$ of $delta$ over $-50arcdeg le alpha le 60arcdeg$ centered on the Celestial Equator. Each piece of sky has $sim 20$ runs contributing and thus reaches $sim2$ magnitudes fainter than the SDSS single pass data, i.e. to $rsim 23.5$ for galaxies. We discuss the image processing of the coaddition, the modeling of the PSF, the calibration, and the production of standard SDSS catalogs. The data have $r$-band median seeing of 1.1arcsec, and are calibrated to $le 1%$. Star color-color, number counts, and psf size vs modelled size plots show the modelling of the PSF is good enough for precision 5-band photometry. Structure in the psf-model vs magnitude plot show minor psf mis-modelling that leads to a region where stars are being mis-classified as galaxies, and this is verified using VVDS spectroscopy. As this is a wide area deep survey there are a variety of uses for the data, including galactic structure, photometric redshift computation, cluster finding and cross wavelength measurements, weak lensing cluster mass calibrations, and cosmic shear measurements.
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Adriana Durbala (Department of Physics
, Astronomy
, University ofn Wisconsin - Stevens Point
2020
We present an HI-optical catalog of ~ 30,000 galaxies based on the 100% complete Arecibo Legacy Fast Arecibo L-band Feed Array (ALFALFA) survey combined with data from the Sloan Digital Sky Survey (SDSS). Our goal is to facilitate public use of the completed ALFALFA catalog by providing carefully determined matches to SDSS counterparts, including matches for ~ 12,000 galaxies that do not have SDSS spectra. These identifications can provide a basis for further cross-matching with other surveys using SDSS photometric IDs as a reference point. We derive absolute magnitudes and stellar masses for each galaxy using optical colors combined with an internal reddening correction designed for small- and intermediate-mass galaxies with active star formation. We also provide measures of stellar masses and star formation rates based on infrared and/or ultraviolet photometry for galaxies that are detected by the Wide-field Infrared Survey Explorer (WISE) and/or the Galaxy Evolution Explorer (GALEX). Finally, we compare the galaxy population in the ALFALFA-SDSS sample with the populations in several other publicly-available galaxy catalogs, and confirm that ALFALFA galaxies typically have lower masses and bluer colors.
Key cosmological applications require the three-dimensional galaxy distribution on the entire celestial sphere. These include measuring the gravitational pull on the Local Group, estimating the large-scale bulk flow and testing the Copernican principle. However, the largest all-sky redshift surveys -- the 2MRS and IRAS PSCz -- have median redshifts of only z=0.03 and sample the very local Universe. There exist all-sky galaxy catalogs reaching much deeper -- SuperCOSMOS in the optical, 2MASS in the near-IR and WISE in the mid-IR -- but these lack complete redshift information. At present, the only rapid way towards larger 3D catalogs covering the whole sky is through photometric redshift techniques. In this paper we present the 2MASS Photometric Redshift catalog (2MPZ) containing 1 million galaxies, constructed by cross-matching 2MASS XSC, WISE and SuperCOSMOS all-sky samples and employing the artificial neural network approach (the ANNz algorithm), trained on such redshift surveys as SDSS, 6dFGS and 2dFGRS. The derived photometric redshifts have errors nearly independent of distance, with an all-sky accuracy of sigma_z=0.015 and a very small percentage of outliers. In this way, we obtain redshift estimates with a typical precision of 12% for all the 2MASS XSC galaxies that lack spectroscopy. In addition, we have made an early effort towards probing the entire 3D sky beyond 2MASS, by pairing up WISE with SuperCOSMOS and training the ANNz on GAMA redshift data reaching currently to z_med~0.2. This has yielded photo-z accuracies comparable to those in the 2MPZ. These all-sky photo-z catalogs, with a median z~0.1 for the 2MPZ, and significantly deeper for future WISE-based samples, will be the largest and most complete of their kind for the foreseeable future.
We present redshift probability distributions for galaxies in the SDSS DR8 imaging data. We used the nearest-neighbor weighting algorithm presented in Lima et al. 2008 and Cunha et al. 2009 to derive the ensemble redshift distribution N(z), and individual redshift probability distributions P(z) for galaxies with r < 21.8. As part of this technique, we calculated weights for a set of training galaxies with known redshifts such that their density distribution in five dimensional color-magnitude space was proportional to that of the photometry-only sample, producing a nearly fair sample in that space. We then estimated the ensemble N(z) of the photometric sample by constructing a weighted histogram of the training set redshifts. We derived P(z) s for individual objects using the same technique, but limiting to training set objects from the local color-magnitude space around each photometric object. Using the P(z) for each galaxy, rather than an ensemble N(z), can reduce the statistical error in measurements that depend on the redshifts of individual galaxies. The spectroscopic training sample is substantially larger than that used for the DR7 release, and the newly added PRIMUS catalog is now the most important training set used in this analysis by a wide margin. We expect the primary source of error in the N(z) reconstruction is sample variance: the training sets are drawn from relatively small volumes of space. Using simulations we estimated the uncertainty in N(z) at a given redshift is 10-15%. The uncertainty on calculations incorporating N(z) or P(z) depends on how they are used; we discuss the case of weak lensing measurements. The P(z) catalog is publicly available from the SDSS website.
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