Engineering topics

Sheldon, Erin S

Publications and source records attributed to Sheldon, Erin S.

5 records

DOE OSTI2012

PHOTOMETRIC REDSHIFT PROBABILITY DISTRIBUTIONS FOR GALAXIES IN THE SDSS DR8

We present redshift probability distributions for galaxies in the Sloan Digital Sky Survey (SDSS) Data Release 8 imaging data. We used the nearest-neighbor weighting algorithm to derive the ensemble redshift distribution N(z), and individual redshift probability distributions P(z) for galaxies with r < 21.8 and u < 29.0. As part of this technique, we calculated weights for a set of training galaxies with known redshifts such that their density distribution in five-dimensional color-magnitude space was proportional to that of the photometry-only sample, producing a nearly fair sample in that space. We estimated the ensemble N(z) of the photometric sample by constructing a weighted histogram of the training-set redshifts. We derived P(z)'s for individual objects by using training-set objects from the local color-magnitude space around each photometric object. Using the P(z) for each galaxy can reduce the statistical error in measurements that depend on the redshifts of individual galaxies. The spectroscopic training sample is substantially larger than that used for the DR7 release. The newly added PRIMUS catalog is now the most important training set used in this analysis by a wide margin. We expect the primary sources of error in the N(z) reconstruction to be sample variance and spectroscopic failures: The training sets are drawn from relatively small volumes of space, and some samples have large incompleteness. Using simulations we estimated the uncertainty in N(z) due to sample variance at a given redshift to be {approx}10%-15%. The uncertainty on calculations incorporating N(z) or P(z) depends on how they are used; we discuss the case of weak lensing measurements. The P(z) catalog is publicly available from the SDSS Web site.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2012

PHOTOMETRIC REDSHIFTS AND QUASAR PROBABILITIES FROM A SINGLE, DATA-DRIVEN GENERATIVE MODEL

We describe a technique for simultaneously classifying and estimating the redshift of quasars. It can separate quasars from stars in arbitrary redshift ranges, estimate full posterior distribution functions for the redshift, and naturally incorporate flux uncertainties, missing data, and multi-wavelength photometry. We build models of quasars in flux-redshift space by applying the extreme deconvolution technique to estimate the underlying density. By integrating this density over redshift, one can obtain quasar flux densities in different redshift ranges. This approach allows for efficient, consistent, and fast classification and photometric redshift estimation. This is achieved by combining the speed obtained by choosing simple analytical forms as the basis of our density model with the flexibility of non-parametric models through the use of many simple components with many parameters. We show that this technique is competitive with the best photometric quasar classification techniques-which are limited to fixed, broad redshift ranges and high signal-to-noise ratio data-and with the best photometric redshift techniques when applied to broadband optical data. We demonstrate that the inclusion of UV and NIR data significantly improves photometric quasar-star separation and essentially resolves all of the redshift degeneracies for quasars inherent to the ugriz filter system, even when included data have a low signal-to-noise ratio. For quasars spectroscopically confirmed by the SDSS 84% and 97% of the objects with Galaxy Evolution Explorer UV and UKIDSS NIR data have photometric redshifts within 0.1 and 0.3, respectively, of the spectroscopic redshift; this amounts to about a factor of three improvement over ugriz-only photometric redshifts. Our code to calculate quasar probabilities and redshift probability distributions is publicly available.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2012

THE SDSS-III BARYON OSCILLATION SPECTROSCOPIC SURVEY: QUASAR TARGET SELECTION FOR DATA RELEASE NINE

The SDSS-III Baryon Oscillation Spectroscopic Survey (BOSS), a five-year spectroscopic survey of 10,000 deg{sup 2}, achieved first light in late 2009. One of the key goals of BOSS is to measure the signature of baryon acoustic oscillations (BAOs) in the distribution of Ly{alpha} absorption from the spectra of a sample of {approx}150,000 z > 2.2 quasars. Along with measuring the angular diameter distance at z Almost-Equal-To 2.5, BOSS will provide the first direct measurement of the expansion rate of the universe at z > 2. One of the biggest challenges in achieving this goal is an efficient target selection algorithm for quasars in the redshift range 2.2 < z < 3.5, where their colors tend to overlap those of the far more numerous stars. During the first year of the BOSS survey, quasar target selection (QTS) methods were developed and tested to meet the requirement of delivering at least 15 quasars deg{sup -2} in this redshift range, with a goal of 20 out of 40 targets deg{sup -2} allocated to the quasar survey. To achieve these surface densities, the magnitude limit of the quasar targets was set at g {<=} 22.0 or r {<=} 21.85. While detection of the BAO signature in the distribution of Ly{alpha} absorption in quasar spectra does not require a uniform target selection algorithm, many other astrophysical studies do. We have therefore defined a uniformly selected subsample of 20 targets deg{sup -2}, for which the selection efficiency is just over 50% ({approx}10 z > 2.20 quasars deg{sup -2}). This 'CORE' subsample will be fixed for Years Two through Five of the survey. For the remaining 20 targets deg{sup -2}, we will continue to develop improved selection techniques, including the use of additional data sets beyond the Sloan Digital Sky Survey (SDSS) imaging data. In this paper, we describe the evolution and implementation of the BOSS QTS algorithms during the first two years of BOSS operations (through 2011 July), in support of the science investigations based on these data, and we analyze the spectra obtained during the first year. During this year, 11,263 new z > 2.20 quasars were spectroscopically confirmed by BOSS, roughly double the number of previously known quasars with z > 2.20. Our current algorithms select an average of 15 z > 2.20 quasars deg{sup -2} from 40 targets deg{sup -2} using single-epoch SDSS imaging. Multi-epoch optical data and data at other wavelengths can further improve the efficiency and completeness of BOSS QTS.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2012

COSMOLOGICAL CONSTRAINTS FROM GALAXY CLUSTERING AND THE MASS-TO-NUMBER RATIO OF GALAXY CLUSTERS

We place constraints on the average density ({Omega}{sub m}) and clustering amplitude ({sigma}{sub 8}) of matter using a combination of two measurements from the Sloan Digital Sky Survey: the galaxy two-point correlation function, w{sub p} (r{sub p} ), and the mass-to-galaxy-number ratio within galaxy clusters, M/N, analogous to cluster M/L ratios. Our w{sub p} (r{sub p} ) measurements are obtained from DR7 while the sample of clusters is the maxBCG sample, with cluster masses derived from weak gravitational lensing. We construct nonlinear galaxy bias models using the Halo Occupation Distribution (HOD) to fit both w{sub p} (r{sub p} ) and M/N for different cosmological parameters. HOD models that match the same two-point clustering predict different numbers of galaxies in massive halos when {Omega}{sub m} or {sigma}{sub 8} is varied, thereby breaking the degeneracy between cosmology and bias. We demonstrate that this technique yields constraints that are consistent and competitive with current results from cluster abundance studies, without the use of abundance information. Using w{sub p} (r{sub p} ) and M/N alone, we find {Omega}{sup 0.5}{sub m}{sigma}{sub 8} = 0.465 {+-} 0.026, with individual constraints of {Omega}{sub m} = 0.29 {+-} 0.03 and {sigma}{sub 8} = 0.85 {+-} 0.06. Combined with current cosmic microwave background data, these constraints are {Omega}{sub m} = 0.290 {+-} 0.016 and {sigma}{sub 8} = 0.826 {+-} 0.020. All errors are 1{sigma}. The systematic uncertainties that the M/N technique are most sensitive to are the amplitude of the bias function of dark matter halos and the possibility of redshift evolution between the SDSS Main sample and the maxBCG cluster sample. Our derived constraints are insensitive to the current level of uncertainties in the halo mass function and in the mass-richness relation of clusters and its scatter, making the M/N technique complementary to cluster abundances as a method for constraining cosmology with future galaxy surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2011

A SIMPLE LIKELIHOOD METHOD FOR QUASAR TARGET SELECTION

We present a new method for quasar target selection using photometric fluxes and a Bayesian probabilistic approach. For our purposes, we target quasars using Sloan Digital Sky Survey (SDSS) photometry to a magnitude limit of g = 22. The efficiency and completeness of this technique are measured using the Baryon Oscillation Spectroscopic Survey (BOSS) data taken in 2010. This technique was used for the uniformly selected (CORE) sample of targets in BOSS year-one spectroscopy to be realized in the ninth SDSS data release. When targeting at a density of 40 objects deg{sup -2} (the BOSS quasar targeting density), the efficiency of this technique in recovering z > 2.2 quasars is 40%. The completeness compared to all quasars identified in BOSS data is 65%. This paper also describes possible extensions and improvements for this technique.

79 ASTRONOMY AND ASTROPHYSICS↗