J. J. Thaler

EIGHT NEW MILKY WAY COMPANIONS DISCOVERED IN FIRST-YEAR DARK ENERGY SURVEY DATA

We report the discovery of eight new Milky Way companions in ~1,800 deg^2 of optical imaging data collected during the first year of the Dark Energy Survey (DES). Each system is identified as a statistically significant over-density of individual stars consistent with the expected isochrone and luminosity function of an old and metal-poor stellar population. The objects span a wide range of absolute magnitudes (M_V from -2.2 mag to -7.4 mag), physical sizes (10 pc to 170 pc), and heliocentric distances (30 kpc to 330 kpc). Based on the low surface brightnesses, large physical sizes, and/or large Galactocentric distances of these objects, several are likely to be new ultra-faint satellite galaxies of the Milky Way and/or Magellanic Clouds. We introduce a likelihood-based algorithm to search for and characterize stellar over-densities, as well as identify stars with high satellite membership probabilities. We also present completeness estimates for detecting ultra-faint galaxies of varying luminosities, sizes, and heliocentric distances in the first-year DES data.

The DES Science Verification weak lensing shear catalogues

We present weak lensing shear catalogues for 139 square degrees of data taken during the Science Verification (SV) time for the new Dark Energy Camera (DECam) being used for the Dark Energy Survey (DES). We describe our object selection, point spread function estimation and shear measurement procedures using two independent shear pipelines, IM3SHAPE and NGMIX, which produce catalogues of 2.12 million and 3.44 million galaxies respectively. We detail a set of null tests for the shear measurements and find that they pass the requirements for systematic errors at the level necessary for weak lensing science applications using the SV data. We also discuss some of the planned algorithmic improvements that will be necessary to produce sufficiently accurate shear catalogues for the full 5-year DES, which is expected to cover 5000 square degrees.

Stellar Kinematics and Metallicities in the Ultra-Faint Dwarf Galaxy Reticulum II

We present Magellan/M2FS, VLT/GIRAFFE, and Gemini South/GMOS spectroscopy of the newly discovered Milky Way satellite Reticulum II. Based on the spectra of 25 Ret II member stars selected from Dark Energy Survey imaging, we measure a mean heliocentric velocity of 62.8 +/- 0.5 km/s and a velocity dispersion of 3.3 +/- 0.7 km/s. The mass-to-light ratio of Ret II within its half-light radius is 470 +/- 210 Msun/Lsun, demonstrating that it is a strongly dark matter-dominated system. Despite its spatial proximity to the Magellanic Clouds, the radial velocity of Ret II differs from that of the LMC and SMC by 199 and 83 km/s, respectively, suggesting that it is not gravitationally bound to the Magellanic system. The likely member stars of Ret II span 1.3 dex in metallicity, with a dispersion of 0.28 +/- 0.09 dex, and we identify several extremely metal-poor stars with [Fe/H] < -3. In combination with its luminosity, size, and ellipticity, these results confirm that Ret II is an ultra-faint dwarf galaxy. With a mean metallicity of [Fe/H] = -2.65 +/- 0.07, Ret II matches Segue~1 as the most metal-poor galaxy known. Although Ret II is the third-closest dwarf galaxy to the Milky Way, the line-of-sight integral of the dark matter density squared is log J = 18.8 +/- 0.6 Gev^2/cm^5 within 0.2 degrees, indicating that the predicted gamma-ray flux from dark matter annihilation in Ret II is lower than that of several other dwarf galaxies.

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.

Resonant substructure in Kππ decays of charmed D mesons

Abstract Dalitz plot analyses of four Kππ decays of the D 0 and D + mesons are presented. The relative amounts of K ∗ π, Kϱ and non-resonant Kππ in each decay mode are determined, and isospin amplitudes and phases are derived. These results are compared with predictions from QCD. The K − π + π + mode has a non-uniform, non-resonant contribution; attempts to fit this distribution are described.

Automated transient identification in the Dark Energy Survey

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.

Mass and galaxy distributions of four massive galaxy clusters from Dark Energy Survey Science Verification data

We measure the weak-lensing masses and galaxy distributions of four massive galaxy clusters observed during the Science Verification phase of the Dark Energy Survey. This pathfinder study is meant to 1) validate the DECam imager for the task of measuring weak-lensing shapes, and 2) utilize DECams large field of view to map out the clusters and their environments over 90 arcmin. We conduct a series of rigorous tests on astrometry, photometry, image quality, PSF modeling, and shear measurement accuracy to single out flaws in the data and also to identify the optimal data processing steps and parameters. We find Science Verification data from DECam to be suitable for the lensing analysis described in this paper. The PSF is generally well-behaved, but the modeling is rendered difficult by a flux-dependent PSF width and ellipticity. We employ photometric redshifts to distinguish between foreground and background galaxies, and a red-sequence cluster finder to provide cluster richness estimates and cluster-galaxy distributions. By fitting NFW profiles to the clusters in this study, we determine weak-lensing masses that are in agreement with previous work. For Abell 3261, we provide the first estimates of redshift, weak-lensing mass, and richness. In addition, the cluster-galaxy distributions indicate the presence of filamentary structures attached to 1E 0657-56 and RXC J2248.7-4431, stretching out as far as 1 degree (approximately 20 Mpc), showcasing the potential of DECam and DES for detailed studies of degree-scale features on the sky.

Branching fractions of τ Leptons to three charged Hadrons

From electron-positron collision data collected with the CLEO detector operating at Cornell Electron Storage Ring near sqrt[s]=10.6 GeV, improved measurements of the branching fractions for tau decays into three explicitly identified hadrons and a neutrino are presented as B(tau(-)-->pi(-)pi(+)pi(-)nu(tau))=(9.13+/-0.05+/-0.46)%, B(tau(-)-->K-pi(+)pi(-)nu(tau))=(3.84+/-0.14+/-0.38) x 10(-3), B(tau(-)-->K-K+pi(-)nu(tau))=(1.55+/-0.06+/-0.09) x 10(-3), and B(tau(-)-->K-K+K-nu(tau))<3.7 x 10(-5) at 90% C.L., where the uncertainties are statistical and systematic, respectively.

Discovery of two gravitationally lensed quasars in the Dark Energy Survey

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.

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

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.