No Arabic abstract
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 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.
The whole sky differential star counts (DSC) with 1 degree resolution are retrieved from 2MASS online data service. Galaxy with double exponential thin and thick disks and a single power law luminosity function (LF) is used to interpret the 2MASS data. The slope of the DSC appears roughly isotropic over the whole sky, the average value is ~0.32, which corresponds to a power law index ~1.8 of the LF. We find that the scale-length and scale-height the thin disk are ~3.0 kpc and ~245 pc, and those of the thick disk are ~3.0 kpc and ~780 pc. The ratio of the thick disk to the thin disk is ~7%. The location of Sun above the disk is ~15 pc. A comparison of the data and model and their discrepancy are also provided.
Supernova (SN) classification and redshift estimation using photometric data only have become very important for the Large Synoptic Survey Telescope (LSST), given the large number of SNe that LSST will observe and the impossibility of spectroscopically following up all the SNe. We investigate the performance of a SN classifier that uses SN colors to classify LSST SNe with the Random Forest classification algorithm. Our classifier results in an AUC of 0.98 which represents excellent classification. We are able to obtain a photometric SN sample containing 99$%$ SNe Ia by choosing a probability threshold. We estimate the photometric redshifts (photo-z) of SNe in our sample by fitting the SN light curves using the SALT2 model with nested sampling. We obtain a mean bias ($left<z_mathrm{phot}-z_mathrm{spec}right>$) of 0.012 with $sigmaleft( frac{z_mathrm{phot}-z_mathrm{spec}}{1+z_mathrm{spec}}right) = 0.0294$ without using a host-galaxy photo-z prior, and a mean bias ($left<z_mathrm{phot}-z_mathrm{spec}right>$) of 0.0017 with $sigmaleft( frac{z_mathrm{phot}-z_mathrm{spec}}{1+z_mathrm{spec}}right) = 0.0116$ using a host-galaxy photo-z prior. Assuming a flat $Lambda CDM$ model with $Omega_m=0.3$, we obtain $Omega_m$ of $0.305pm0.008$ (statistical errors only), using the simulated LSST sample of photometric SNe Ia (with intrinsic scatter $sigma_mathrm{int}=0.11$) derived using our methodology without using host-galaxy photo-z prior. Our method will help boost the power of SNe from the LSST as cosmological probes.
In a flat universe dominated by dark energy, the Integrated Sachs-Wolfe (ISW) effect can be detected as a large-angle cross-correlation between the CMB and a tracer of large scale structure. We investigate whether the inconclusive ISW signal derived from 2MASS galaxy maps can be improved upon by including photometric redshifts for the 2MASS galaxies. These redshifts are derived by matching the 2MASS data with optical catalogues generated from SuperCOSMOS scans of major photographic sky surveys. We find no significant ISW signal in this analysis; an ISW effect of the form expected in a LambdaCDM universe is only weakly preferred over no correlation, with a likelihood ratio of 1.5:1. We consider ISW detection prospects for future large scale structure surveys with fainter magnitude limits and greater survey depth; even with the best possible data, the ISW cross-correlation signal would be expected to evade detection in >~ 10% of cases.
The peculiar velocity of a mass tracer is on average aligned with the dipole modulation of the surrounding mass density field. We present a first measurement of the correlation between radial peculiar velocities of objects in the cosmicflows-3 catalog and the dipole moment of the 2MRS galaxy distribution in concentric spherical shells centered on these objects. Limiting the analysis to cosmicflows-3 objects with distances of $100 rm Mpc h^{-1}$, the correlation function is detected at a confidence level $> 4sigma$. The measurement is found consistent with the standard $Lambda$CDM model at $< 1.7sigma$ level. We formally derive the constraints $0.32<Omega^{0.55}sigma_8<0.48$ ($68% $ confidence level) or equivalently $0.34<Omega^{0.55}/b<0.52$, where $b$ is the galaxy bias factor. Deeper and improved peculiar velocity catalogs will substantially reduce the uncertainties, allowing tighter constraints from this type of correlations.