M. Banerji

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.

The SCUBA-2 Cosmology Legacy Survey: 850 μm maps, catalogues and number counts

We present a catalogue of similar to 3000 submillimetre sources detected (>= 3.5 sigma) at 850 mu m over similar to 5 deg(2) surveyed as part of the James Clerk Maxwell Telescope (JCMT) SCUBA-2 Cosmology Legacy Survey (S2CLS). This is the largest survey of its kind at 850 mu m, increasing the sample size of 850 mu m selected submillimetre galaxies by an order of magnitude. The wide 850 mu m survey component of S2CLS covers the extragalactic fields: UKIDSS-UDS, COSMOS, Akari-NEP, Extended Groth Strip, Lockman Hole North, SSA22 and GOODS-North. The average 1s depth of S2CLS is 1.2 mJy beam(-1), approaching the SCUBA-2 850 mu m confusion limit, which we determine to be sigma(c) approximate to 0.8 mJy beam(-1). We measure the 850 mu m number counts, reducing the Poisson errors on the differential counts to approximately 4 per cent at S-850 approximate to 3 mJy. With several independent fields, we investigate field-to-field variance, finding that the number counts on 0.5 degrees-1 degrees scales are generally within 50 per cent of the S2CLS mean for S-850 > 3 mJy, with scatter consistent with the Poisson and estimated cosmic variance uncertainties, although there is a marginal (2 sigma) density enhancement in GOODS-North. The observed counts are in reasonable agreement with recent phenomenological and semi-analytic models, although determining the shape of the faint-end slope (S-850 10 mJy there are approximately 10 sources per square degree, and we detect the distinctive up-turn in the number counts indicative of the detection of local sources of 850 mu m emission

THE IDENTIFICATION OF z -DROPOUTS IN PAN-STARRS1: THREE QUASARS AT 6.5< z < 6.7

Luminous distant quasars are unique probes of the high redshift intergalactic medium (IGM) and of the growth of massive galaxies and black holes in the early universe. Absorption due to neutral Hydrogen in the IGM makes quasars beyond a redshift of z~6.5 very faint in the optical

Automated transient identification in the Dark Energy Survey

z

Discovery of two gravitationally lensed quasars in the Dark Energy Survey

-band, thus locating quasars at higher redshifts require large surveys that are sensitive above 1 micron. We report the discovery of three new z>6.5 quasars, corresponding to an age of the universe of 6.5 quasars from 4 to 7. The quasars have redshifts of z=6.50, 6.52, and 6.66, and include the brightest z-dropout quasar reported to date, PSO J036.5078+03.0498 with M_1450=-27.4. We obtained near-infrared spectroscopy for the quasars and from the MgII line we estimate that the central black holes have masses between 5x10^8 and 4x10^9 M_sun, and are accreting close to the Eddington limit (L_Bol/L_Edd=0.13-1.2). We investigate the ionized regions around the quasars and find near zone radii of R_NZ=1.5-5.2 proper Mpc, confirming the trend of decreasing near zone sizes with increasing redshift found for quasars at 5.7<z<6.4. By combining R_NZ of the PS1 quasars with those of 5.7<z<7.1 quasars in the literature, we derive a luminosity corrected redshift evolution of R_NZ,corrected=(7.2+/-0.2)-(6.1+/-0.7)x(z-6) Mpc. However, the large spread in R_NZ in the new quasars implies a wide range in quasar ages and/or a large variation in the neutral Hydrogen fraction along different lines of sight.

The SCUBA-2 Cosmology Legacy Survey : galaxies in the deep 850 μm survey, and the star-forming `main sequence'

We describe an algorithm for identifying point-source transients and moving objects on reference-subtracted optical images containing artifacts of processing and instrumentation. The algorithm makes use of the supervised machine learning technique known as Random Forest. We present results from its use in the Dark Energy Survey Supernova program (DES-SN), where it was trained using a sample of 898,963 signal and background events generated by the transient detection pipeline. After reprocessing the data collected during the first DES-SN observing season (2013 September through 2014 February) using the algorithm, the number of transient candidates eligible for human scanning decreased by a factor of 13.4, while only 1.0% of the artificial Type Ia supernovae (SNe) injected into search images to monitor survey efficiency were lost, most of which were very faint events. Here we characterize the algorithms performance in detail, and we discuss how it can inform pipeline design decisions for future time-domain imaging surveys, such as the Large Synoptic Survey Telescope and the Zwicky Transient Facility. An implementation of the algorithm and the training data used in this paper are available at at http://portal.nersc.gov/project/dessn/autoscan.

Mapping and simulating systematics due to spatially varying observing conditions in DES Science Verification data

We present spectroscopic confirmation of two new gravitationally lensed quasars, discovered in the Dark Energy Survey (DES) and Wide-field Infrared Survey Explorer (WISE) based on their multiband photometry and extended morphology in DES images. Images of DES J0115-5244 show a red galaxy with two blue point sources at either side, which are images of the same quasar at zs = 1.64 as obtained by our long-slit spectroscopic data. The Einstein radius estimated from the DES images is 0.51 arcsec. DES J2146-0047 is in the area of overlap between DES and the Sloan Digital Sky Survey (SDSS). Two blue components are visible in the DES and SDSS images. The SDSS fibre spectrum shows a quasar component at zs = 2.38 and absorption by Mg II and Fe II at zl = 0.799, which we tentatively associate with the foreground lens galaxy. Our long-slit spectra show that the blue components are resolved images of the same quasar. The Einstein radius is 0.68 arcsec, corresponding to an enclosed mass of 1.6 × 1011 Ms. Three other candidates were observed and rejected, two being low-redshift pairs of starburst galaxies, and one being a quasar behind a blue star. These first confirmation results provide an important empirical validation of the data mining and model-based selection that is being applied to the entire DES data set.

MODELING THE TRANSFER FUNCTION FOR THE DARK ENERGY SURVEY

The final, definitive version of this paper has been published in Monthly Notices of the Royal Astronomical Society, Vol. 458 ( 4 ): 4321-4344, March 2016, DOI: 10.1093/mnras/stw564, first published on line March 10, 2016, by Oxford University Press on behalf of MNRAS.

DES J0454−4448: discovery of the first luminous z ≥ 6 quasar from the Dark Energy Survey

Spatially varying depth and the characteristics of observing conditions, such as seeing, airmass, or sky background, are major sources of systematic uncertainties in modern galaxy survey analyses, particularly in deep multi-epoch surveys. We present a framework to extract and project these sources of systematics onto the sky, and apply it to the Dark Energy Survey (DES) to map the observing conditions of the Science Verification (SV) data. The resulting distributions and maps of sources of systematics are used in several analyses of DES–SV to perform detailed null tests with the data, and also to incorporate systematics in survey simulations. We illustrate the complementary nature of these two approaches by comparing the SV data with BCC-UFig, a synthetic sky catalog generated by forward-modeling of the DES–SV images. We analyze the BCC-UFig simulation to construct galaxy samples mimicking those used in SV galaxy clustering studies. We show that the spatially varying survey depth imprinted in the observed galaxy densities and the redshift distributions of the SV data are successfully reproduced by the simulation and are well-captured by the maps of observing conditions. The combined use of the maps, the SV data, and the BCC-UFig simulation allows us to quantify the impact of spatial systematics on N(z), the redshift distributions inferred using photometric redshifts. We conclude that spatial systematics in the SV data are mainly due to seeing fluctuations and are under control in current clustering and weak-lensing analyses. However, they will need to be carefully characterized in upcoming phases of DES in order to avoid biasing the inferred cosmological results. The framework presented here is relevant to all multi-epoch surveys and will be essential for exploiting future surveys such as the Large Synoptic Survey Telescope, which will require detailed null tests and realistic end-to-end image simulations to correctly interpret the deep, high-cadence observations of the sky.

Dark energy survey year 1 results: the photometric data set for cosmology

We present a forward-modeling simulation framework designed to model the data products from the Dark Energy Survey (DES). This forward-model process can be thought of as a transfer function—a mapping from cosmological/astronomical signals to the final data products used by the scientists. Using output from the cosmological simulations (the Blind Cosmology Challenge), we generate simulated images (the Ultra Fast Image Simulator) and catalogs representative of the DES data. In this work we demonstrate the framework by simulating the 244 deg2 coadd images and catalogs in five bands for the DES Science Verification data. The simulation output is compared with the corresponding data to show that major characteristics of the images and catalogs can be captured. We also point out several directions of future improvements. Two practical examples—star-galaxy classification and proximity effects on object detection—are then used to illustrate how one can use the simulations to address systematics issues in data analysis. With clear understanding of the simplifications in our model, we show that one can use the simulations side-by-side with data products to interpret the measurements. This forward modeling approach is generally applicable for other upcoming and future surveys. It provides a powerful tool for systematics studies that is sufficiently realistic and highly controllable.