Alexandra Abate

redMaGiC : selecting luminous red galaxies from the DES Science Verification data

We introduce redMaGiC, an automated algorithm for selecting luminous red galaxies (LRGs). The algorithm was specifically developed to minimize photometric redshift uncertainties in photometric large-scale structure studies. redMaGiC achieves this by self-training the colour cuts necessary to produce a luminosity-thresholded LRG sample of constant comoving density. We demonstrate that redMaGiC photo-zs are very nearly as accurate as the best machine learning-based methods, yet they require minimal spectroscopic training, do not suffer from extrapolation biases, and are very nearly Gaussian. We apply our algorithm to Dark Energy Survey (DES) Science Verification (SV) data to produce a redMaGiC catalogue sampling the redshift range z is an element of [0.2, 0.8]. Our fiducial sample has a comoving space density of 10(-3) (h(-1) Mpc)(-3), and a median photo-z bias (z(spec) - z(photo)) and scatter (sigma(z)/(1 + z)) of 0.005 and 0.017, respectively. The corresponding 5 sigma outlier fraction is 1.4 per cent. We also test our algorithm with Sloan Digital Sky Survey Data Release 8 and Stripe 82 data, and discuss how spectroscopic training can be used to control photo-z biases at the 0.1 per cent level.

Peculiar velocities into the next generation: cosmological parameters from the SFI++ survey

We present cosmological parameter constraints from the SFI++ galaxy peculiar velocity survey, the largest galaxy peculiar velocity sample to date. The analysis is performed by using the gridding method developed in Abate et al. We concentrate on constraining parameters which are affected by the clustering of matter: σ 8 and the growth index γ. Assuming a concordance A cold dark matter (ACDM) model, we find σ 8 = 0.91 +0.22 ―0.18 and γ = 0.55 +0.13 ―0.14 after marginalizing over Ω m . These constraints are consistent with, and have constraining power similar to, the same constraints from other current data sets which use different methods. Recently, there have been several claims that the peculiar velocity measurements do not agree with ACDM. Instead, we find that, although a higher value of σ 8 and a lower value of Ω m are preferred, the values are still consistent when compared with Wilkinson Microwave Anisotropy Probe 5 results. We note that although our analysis probes a variety of scales, the constraints will be dominated by the smaller scales, which have the smallest uncertainties. These results show that peculiar velocity analysis is a vital probe of cosmology, providing competitive constraints on parameters such as σ 8 . Its sensitivity to the derivative of growth function, particularly down to redshift zero, means that it can provide a vital low redshift anchor on the evolution of structure formation. The importance of utilizing different probes with varying systematics is also an essential requirement for providing a consistency check on the best-fitting cosmological model.

Detected fluctuations in Sloan Digital Sky Survey luminous red galaxy magnitudes: bulk flow signature or systematic?

In this paper we search for a signature of a large scale bulk flow by looking for fluctuations in the magnitudes of distant LRGs. We take a sample of LRGs from the Sloan Digital Sky Survey with redshifts of z>0.08 over a contiguous area of sky. Neighboring LRG magnitudes are averaged together to find the fluctuation in magnitudes as a function of R.A.. The result is a fluctuation of a few percent in flux across roughly 100 degrees. The source of this fluctuation could be from a large scale bulk flow or a systematic in our treatment of the data set, or the data set itself. A bulk flow model is fitted to the observed fluctuation, and the three bulk flow parameters, its direction and magnitude: alpha_b, delta_b, v_b are constrained. We find that the bulk flow direction is consistent with the direction found by other authors, with alpha_b 180, delta_b -50. The bulk flow magnitude however was found to be anomalously large with v_b>4000km/s. The LRG angular selection function cannot be sufficiently taken into account in our analysis with the available data, and may be the source of either the anomalous magnitude of the bulk flow signal, or possibly the entire fluctuation. However, the fluctuation indicates a bulk flow direction very close to those found using other data sets and analyses. Further investigation with upcoming data is required to confirm this detection.

Detected fluctuations in SDSS LRG magnitudes: Bulk flow signature or systematic?

In this paper we search for a signature of a large scale bulk flow by looking for fluctuations in the magnitudes of distant LRGs. We take a sample of LRGs from the Sloan Digital Sky Survey with redshifts of z>0.08 over a contiguous area of sky. Neighboring LRG magnitudes are averaged together to find the fluctuation in magnitudes as a function of R.A.. The result is a fluctuation of a few percent in flux across roughly 100 degrees. The source of this fluctuation could be from a large scale bulk flow or a systematic in our treatment of the data set, or the data set itself. A bulk flow model is fitted to the observed fluctuation, and the three bulk flow parameters, its direction and magnitude: alpha_b, delta_b, v_b are constrained. We find that the bulk flow direction is consistent with the direction found by other authors, with alpha_b 180, delta_b -50. The bulk flow magnitude however was found to be anomalously large with v_b>4000km/s. The LRG angular selection function cannot be sufficiently taken into account in our analysis with the available data, and may be the source of either the anomalous magnitude of the bulk flow signal, or possibly the entire fluctuation. However, the fluctuation indicates a bulk flow direction very close to those found using other data sets and analyses. Further investigation with upcoming data is required to confirm this detection.

Peculiar velocities into the next generation: cosmological parameters from large surveys without bias from non‐linear structure

We investigate methods to best estimate the normalisation of the mass density fluctuation power spectrum (sigma_8) using peculiar velocity data from a survey like the Six degree Field Galaxy Velocity Survey (6dFGSv). We focus on two potential problems (i) biases from nonlinear growth of structure and (ii) the large number of velocities in the survey. Simulations of LambdaCDM-like models are used to test the methods. We calculate the likelihood from a full covariance matrix of velocities averaged in grid cells. This simultaneously reduces the number of data points and smooths out nonlinearities which tend to dominate on small scales. We show how the averaging can be taken into account in the predictions in a practical way, and show the effect of the choice of cell size. We find that a cell size can be chosen that significantly reduces the nonlinearities without significantly increasing the error bars on cosmological parameters. We compare our results with those from a principal components analysis following Watkins et al (2002) and Feldman et al (2003) to select a set of optimal moments constructed from linear combinations of the peculiar velocities that are least sensitive to the nonlinear scales. We conclude that averaging in grid cells performs equally well. We find that for a survey such as 6dFGSv we can estimate sigma_8 with less than 3% bias from nonlinearities. The expected error on sigma_8 after marginalising over Omega_m is approximately 16 percent.

A new method to improve photometric redshift reconstruction - Applications to the Large Synoptic Survey Telescope

In the next decade, the LSST will become a major facility for the astronomical community. However accurately determining the redshifts of the observed galaxies without using spectroscopy is a major challenge. Reconstruction of the redshifts with high resolution and well-understood uncertainties is mandatory for many science goals, including the study of baryonic acoustic oscillations. We investigate different approaches to establish the accuracy that can be reached by the LSST six-band photometry. We construct a realistic mock galaxy catalog, based on the GOODS survey luminosity function, by simulating the expected apparent magnitude distribution for the LSST. To reconstruct the photometric redshifts (photo-zs), we consider a template-fitting method and a neural network method. The photo-z reconstruction from both of these techniques is tested on real CFHTLS data and also on simulated catalogs. We describe a new method to improve photo-z reconstruction that efficiently removes catastrophic outliers via a likelihood ratio statistical test. This test uses the posterior probability functions of the fit parameters and the colors. We show that the photometric redshift accuracy will meet the stringent LSST requirements up to redshift

Spectroscopic needs for imaging dark energy experiments

\sim2.5

Shear-selected clusters from the deep lens survey. III. masses from weak lensing

after a selection that is based on the likelihood ratio test or on the apparent magnitude for galaxies with

The three faces of Ωm: testing gravity with low- and high-redshift SNe Ia surveys

S/N>5

Corrigendum to "spectroscopic needs for imaging dark energy experiments" [Astropart. Phys. 63 (2015) 81-100]

in at least 5 bands. The former selection has the advantage of retaining roughly 35% more galaxies for a similar photo-z performance compared to the latter. Photo-z reconstruction using a neural network algorithm is also described. In addition, we utilize the CFHTLS spectro-photometric catalog to outline the possibility of combining the neural network and template-fitting methods. We conclude that the photo-zs will be accurately estimated with the LSST if a Bayesian prior probability and a calibration sample are used.