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 color-cuts necessary to produce a luminosity-thresholded LRG sample of constant comoving density. We demonstrate that redMaGiC photozs 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 catalog sampling the redshift range $zin[0.2,0.8]$. Our fiducial sample has a comoving space density of $10^{-3} (h^{-1} Mpc)^{-3}$, and a median photoz bias ($z_{spec}-z_{photo}$) and scatter $(sigma_z/(1+z))$ of 0.005 and 0.017 respectively. The corresponding $5sigma$ outlier fraction is 1.4%. We also test our algorithm with Sloan Digital Sky Survey (SDSS) Data Release 8 (DR8) and Stripe 82 data, and discuss how spectroscopic training can be used to control photoz biases at the 0.1% level.
We describe redMaPPer, a new red-sequence cluster finder specifically designed to make optimal use of ongoing and near-future large photometric surveys. The algorithm has multiple attractive features: (1) It can iteratively self-train the red-sequenc
e model based on minimal spectroscopic training sample, an important feature for high redshift surveys; (2) It can handle complex masks with varying depth; (3) It produces cluster-appropriate random points to enable large-scale structure studies; (4) All clusters are assigned a full redshift probability distribution P(z); (5) Similarly, clusters can have multiple candidate central galaxies, each with corresponding centering probabilities; (6) The algorithm is parallel and numerically efficient: it can run a Dark Energy Survey-like catalog in ~500 CPU hours; (7) The algorithm exhibits excellent photometric redshift performance, the richness estimates are tightly correlated with external mass proxies, and the completeness and purity of the corresponding catalogs is superb. We apply the redMaPPer algorithm to ~10,000 deg^2 of SDSS DR8 data, and present the resulting catalog of ~25,000 clusters over the redshift range 0.08<z<0.55. The redMaPPer photometric redshifts are nearly Gaussian, with a scatter sigma_z ~ 0.006 at z~0.1, increasing to sigma_z~0.02 at z~0.5 due to increased photometric noise near the survey limit. The median value for |Delta z|/(1+z) for the full sample is 0.006. The incidence of projection effects is low (<=5%). Detailed performance comparisons of the redMaPPer DR8 cluster catalog to X-ray and SZ catalogs are presented in a companion paper (Rozo & Rykoff 2014).
Reducing the scatter between cluster mass and optical richness is a key goal for cluster cosmology from photometric catalogs. We consider various modifications to the red-sequence matched filter richness estimator of Rozo et al. (2009), and evaluate
their impact on the scatter in X-ray luminosity at fixed richness. Most significantly, we find that deeper luminosity cuts can reduce the recovered scatter, finding that sigma_lnLX|lambda=0.63+/-0.02 for clusters with M_500c >~ 1.6e14 h_70^-1 M_sun. The corresponding scatter in mass at fixed richness is sigma_lnM|lambda ~ 0.2-0.3 depending on the richness, comparable to that for total X-ray luminosity. We find that including blue galaxies in the richness estimate increases the scatter, as does weighting galaxies by their optical luminosity. We further demonstrate that our richness estimator is very robust. Specifically, the filter employed when estimating richness can be calibrated directly from the data, without requiring a-priori calibrations of the red-sequence. We also demonstrate that the recovered richness is robust to up to 50% uncertainties in the galaxy background, as well as to the choice of photometric filter employed, so long as the filters span the 4000 A break of red-sequence galaxies. Consequently, our richness estimator can be used to compare richness estimates of different clusters, even if they do not share the same photometric data. Appendix 1 includes easy-bake instructions for implementing our optimal richness estimator, and we are releasing an implementation of the code that works with SDSS data, as well as an augmented maxBCG catalog with the lambda richness measured for each cluster.
We report on a complete set of early optical afterglows of gamma-ray bursts (GRBs) obtained with the ROTSE-III telescope network from March 2005 through June 2007. This set is comprised of 12 afterglows with early optical and Swift/XRT observations,
with a median ROTSE-III response time of 45 s after the start of gamma-ray emission (8 s after the GCN notice time). These afterglows span four orders of magnitude in optical luminosity, and the contemporaneous X-ray detections allow multi-wavelength spectral analysis. Excluding X-ray flares, the broadband synchrotron spectra show that the optical and X-ray emission originate in a common region, consistent with predictions of the external forward shock in the fireball model. However, the fireball model is inadequate to predict the temporal decay indices of the early afterglows, even after accounting for possible long-duration continuous energy injection. We find that the optical afterglow is a clean tracer of the forward shock, and we use the peak time of the forward shock to estimate the initial bulk Lorentz factor of the GRB outflow, and find 100<Gamma_0<1000, consistent with expectations.
We present a new measurement of the scaling relation between X-ray luminosity and total mass for 17,000 galaxy clusters in the maxBCG cluster sample. Stacking sub-samples within fixed ranges of optical richness, N_200, we measure the mean 0.1-2.4 keV
X-ray luminosity, <L_X>, from the ROSAT All-Sky Survey. The mean mass, <M_200>, is measured from weak gravitational lensing of SDSS background galaxies (Johnston et al. 2007). For 9 <= N_200 < 200, the data are well fit by a power-law, <L_X>/10^42 h^-2 erg/s = (12.6+1.4-1.3 (stat) +/- 1.6 (sys)) (<M_200>/10^14 h^-1 M_sun)^1.65+/-0.13. The slope agrees to within 10% with previous estimates based on X-ray selected catalogs, implying that the covariance in L_X and N_200 at fixed halo mass is not large. The luminosity intercent is 30%, or 2sigma, lower than determined from the X-ray flux-limited sample of Reiprich & Bohringer (2002), assuming hydrostatic equilibrium. This difference could arise from a combination of Malmquist bias and/or systematic error in hydrostatic mass estimates, both of which are expected. The intercept agrees with that derived by Stanek et al. (2006) using a model for the statistical correspondence between clusters and halos in a WMAP3 cosmology with power spectrum normalization sigma_8 = 0.85. Similar exercises applied to future data sets will allow constraints on the covariance among optical and hot gas properties of clusters at fixed mass.
Determining the scaling relations between galaxy cluster observables requires large samples of uniformly observed clusters. We measure the mean X-ray luminosity--optical richness (L_X--N_200) relation for an approximately volume-limited sample of mor
e than 17,000 optically-selected clusters from the maxBCG catalog spanning the redshift range 0.1<z<0.3. By stacking the X-ray emission from many clusters using ROSAT All-Sky Survey data, we are able to measure mean X-ray luminosities to ~10% (including systematic errors) for clusters in nine independent optical richness bins. In addition, we are able to crudely measure individual X-ray emission from ~800 of the richest clusters. Assuming a log-normal form for the scatter in the L_X--N_200 relation, we measure sigma_ln{L}=0.86+/-0.03 at fixed N_200. This scatter is large enough to significantly bias the mean stacked relation. The corrected median relation can be parameterized by L_X = (e^alpha)(N_200/40)^beta 10^42 h^-2 ergs/s, where alpha = 3.57+/-0.08 and beta = 1.82+/-0.05. We find that X-ray selected clusters are significantly brighter than optically-selected clusters at a given optical richness. This selection bias explains the apparently X-ray underluminous nature of optically-selected cluster catalogs.
