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With current and upcoming experiments such as the Wide Field Infrared Survey Telescope, Euclid, and Large Synoptic Survey Telescope, we can observe up to billions of galaxies. While such surveys cannot obtain spectra for all observed galaxies, they produce galaxy magnitudes in color filters. This data set behaves like a high-dimensional nonlinear surface, an excellent target for machine learning. In this work, we use a lightcone of semianalytic galaxies tuned to match Cosmic Assembly Near-infrared Deep Legacy Survey (CANDELS) observations from Lu et al. to train a set of neural networks on a set of galaxy physical properties. We add realistic photometric noise and use trained neural networks to predict stellar masses and average star formation rates (SFRs) on real CANDELS galaxies, comparing our predictions to SED-fitting results. On semianalytic galaxies, we are nearly competitive with template-fitting methods, with biases of 0.01 dex for stellar mass, 0.09 dex for SFR, and 0.04 dex for metallicity. For the observed CANDELS data, our results are consistent with template fits on the same data at 0.15 dex bias in and 0.61 dex bias in the SFR. Some of the bias is driven by SED-fitting limitations, rather than limitations on the training set, and some is intrinsic to the neural network method. Further errors are likely caused by differences in noise properties between the semianalytic catalogs and data. Our results show that galaxy physical properties can in principle be measured with neural networks at a competitive degree of accuracy and precision to template-fitting methods. [ABSTRACT FROM AUTHOR]

The shapes of distant galaxies are sheared by intervening galaxy clusters. We examine this effect in Stripe 82, a 275 deg² region observed multiple times in the Sloan Digital Sky Survey (SDSS) and co-added to achieve greater depth. We obtain a mass-richness calibration that is similar to other SDSS analyses, demonstrating that the co-addition process did not adversely affect the lensing signal. We also propose a new parameterization of the effect of tomography on the cluster lensing signal which does not require binning in redshift, and we show that using this parameterization we can detect tomography for stacked clusters at varying redshifts. Finally, due to the sensitivity of the tomographic detection to accurately marginalize over the effect of the cluster mass, we show that tomography at low redshift (where dependence on exact cosmological models is weak) can be used to constrain mass profiles in clusters. [ABSTRACT FROM AUTHOR]

We present and describe a catalog of galaxy photometric redshifts (photo-z) for the Sloan Digital Sky Survey (SDSS) Co-add Data. We use the artificial neural network (ANN) technique to calculate the photo-z and the nearest neighbor error method to estimate photo-z errors for ~13 million objects classified as galaxies in the co-add with r < 24.5. The photo-z and photo-z error estimators are trained and validated on a sample of ~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, the Deep Extragalactic Evolutionary Probe Data Release 3, the VIsible imaging Multi-Object Spectrograph--Very Large Telescope Deep Survey, 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 σ68 = 0.031. After presenting our results and quality tests, we provide a short guide for users accessing the public data. [ABSTRACT FROM AUTHOR]

We present details of the construction and characterization of the coaddition of the Sloan Digital Sky Survey (SDSS) Stripe 82 ugriz imaging data. This survey consists of 275 deg2 of repeated scanning by the SDSS camera over –50° ⩽ α ⩽ 60° and –1.°25 ⩽ δ ⩽ +1.°25 centered on the Celestial Equator. Each piece of sky has ∼20 runs contributing and thus reaches ∼2 mag fainter than the SDSS single pass data, i.e., to r ∼ 23.5 for galaxies. We discuss the image processing of the coaddition, the modeling of the point-spread function (PSF), the calibration, and the production of standard SDSS catalogs. The data have an r-band median seeing of 1.″1 and are calibrated to ⩽1%. Star color-color, number counts, and PSF size versus modeled size plots show that the modeling of the PSF is good enough for precision five-band photometry. Structure in the PSF model versus magnitude plot indicates minor PSF modeling errors, leading to misclassification of stars as galaxies, as verified using VVDS spectroscopy. There are a variety of uses for this wide-angle deep imaging data, including galactic structure, photometric redshift computation, cluster finding and cross wavelength measurements, weak lensing cluster mass calibrations, and cosmic shear measurements. [ABSTRACT FROM AUTHOR]

Stripe 82 in the Sloan Digital Sky Survey was observed multiple times, allowing deeper images to be constructed by co-adding the data. Here, we analyze the ellipticities of background galaxies in this 275 deg2 region, searching for evidence of distortions due to cosmic shear. We do so using measurements of both the shear-shear correlation function and power spectrum, with the latter determined using both “quadratic” and “pseudo” estimation techniques. We show how we verified these methods using mock catalogs. We also describe our methods for modeling and correcting for the effects of the point-spread function (PSF) in our shape measurements, and we also describe our prescription for estimating photometric redshifts (photo-z's) for our galaxy sample. In particular, we assess the impact of potential systematic effects due to the PSF and to photo-z's, and show that these are under control in our analysis. We find consistent correlation function and power spectrum results, where the E-mode cosmic shear signal is detected in both real and Fourier space with >5σ 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 Ωm and fluctuation amplitude σ8 to be Ω0.7mσ8 = 0.252+0.032– 0.052. [ABSTRACT FROM AUTHOR]