We show that a large-area imaging survey using narrow-band filters could detect quasars in sufficiently high number densities, and with more than sufficient accuracy in their photometric redshifts, to turn them into suitable tracers of large-scale st
ructure. If a narrow-band optical survey can detect objects as faint as i=23, it could reach volumetric number densities as high as 10^{-4} h^3 Mpc^{-3} (comoving) at z~1.5 . Such a catalog would lead to precision measurements of the power spectrum up to z~3-4. We also show that it is possible to employ quasars to measure baryon acoustic oscillations at high redshifts, where the uncertainties from redshift distortions and nonlinearities are much smaller than at z<1. As a concrete example we study the future impact of J-PAS, which is a narrow-band imaging survey in the optical over 1/5 of the unobscured sky with 42 filters of ~100 A full-width at half-maximum. We show that J-PAS will be able to take advantage of the broad emission lines of quasars to deliver excellent photometric redshifts, sigma_{z}~0.002(1+z), for millions of objects.
In Lima et al. 2008 we presented a new method for estimating the redshift distribution, N(z), of a photometric galaxy sample, using photometric observables and weighted sampling from a spectroscopic subsample of the data. In this paper, we extend thi
s method and explore various applications of it, using both simulations of and real data from the SDSS. In addition to estimating the redshift distribution for an entire sample, the weighting method enables accurate estimates of the redshift probability distribution, p(z), for each galaxy in a photometric sample. Use of p(z) in cosmological analyses can substantially reduce biases associated with traditional photometric redshifts, in which a single redshift estimate is associated with each galaxy. The weighting procedure also naturally indicates which galaxies in the photometric sample are expected to have accurate redshift estimates, namely those that lie in regions of photometric-observable space that are well sampled by the spectroscopic subsample. In addition to providing a method that has some advantages over standard photo-z estimates, the weights method can also be used in conjunction with photo-z estimates, e.g., by providing improved estimation of N(z) via deconvolution of N(photo-z) and improved estimates of photo-z scatter and bias. We present a publicly available p(z) catalog for ~78 million SDSS DR7 galaxies.
The statistical properties of dark matter halos, the building blocks of cosmological observables associated with structure in the universe, offer many opportunities to test models for cosmic acceleration, especially those that seek to modify gravitat
ional forces. We study the abundance, bias and profiles of halos in cosmological simulations for one such model: the modified action f(R) theory. In the large field regime that is accessible to current observations, enhanced gravitational forces raise the abundance of rare massive halos and decrease their bias but leave their (lensing) mass profiles largely unchanged. This regime is well described by scaling relations based on a modification of spherical collapse calculations. In the small field regime, enhanced forces are suppressed inside halos and the effects on halo properties are substantially reduced for the most massive halos. Nonetheless, the scaling relations still retain limited applicability for the purpose of establishing conservative upper limits on the modification to gravity.
We carry out a suite of cosmological simulations of modified action f(R) models where cosmic acceleration arises from an alteration of gravity instead of dark energy. These models introduce an extra scalar degree of freedom which enhances the force o
f gravity below the inverse mass or Compton scale of the scalar. The simulations exhibit the so-called chameleon mechanism, necessary for satisfying local constraints on gravity, where this scale depends on environment, in particular the depth of the local gravitational potential. We find that the chameleon mechanism can substantially suppress the enhancement of power spectrum in the non-linear regime if the background field value is comparable to or smaller than the depth of the gravitational potentials of typical structures. Nonetheless power spectrum enhancements at intermediate scales remain at a measurable level for models even when the expansion history is indistinguishable from a cosmological constant, cold dark matter model. Simple scaling relations that take the linear power spectrum into a non-linear spectrum fail to capture the modifications of f(R) due to the change in collapsed structures, the chameleon mechanism, and the time evolution of the modifications.
We present an empirical method for estimating the underlying redshift distribution N(z) of galaxy photometric samples from photometric observables. The method does not rely on photometric redshift (photo-z) estimates for individual galaxies, which ty
pically suffer from biases. Instead, it assigns weights to galaxies in a spectroscopic subsample such that the weighted distributions of photometric observables (e.g., multi-band magnitudes) match the corresponding distributions for the photometric sample. The weights are estimated using a nearest-neighbor technique that ensures stability in sparsely populated regions of color-magnitude space. The derived weights are then summed in redshift bins to create the redshift distribution. We apply this weighting technique to data from the Sloan Digital Sky Survey as well as to mock catalogs for the Dark Energy Survey, and compare the results to those from the estimation of photo-zs derived by a neural network algorithm. We find that the weighting method accurately recovers the underlying redshift distribution, typically better than the photo-z reconstruction, provided the spectroscopic subsample spans the range of photometric observables covered by the photometric sample.
We present and describe a catalog of galaxy photometric redshifts (photo-zs) for the Sloan Digital Sky Survey (SDSS) Data Release 6 (DR6). 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 ~ 77 million objects classified as galaxies in DR6 with r < 22. The photo-z and photo-z error estimators are trained and validated on a sample of ~ 640,000 galaxies that have SDSS photometry and spectroscopic redshifts measured by SDSS, 2SLAQ, CFRS, CNOC2, TKRS, DEEP, and DEEP2. For the two 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} =0.021 or $0.024. After presenting our results and quality tests, we provide a short guide for users accessing the public data.
