ABSTRACT
We present a description of the Australian Dark Energy Survey (OzDES) and summarize the results from its 6 years of operations. Using the 2dF fibre positioner and AAOmega spectrograph on the ...3.9-m Anglo-Australian Telescope, OzDES has monitored 771 active galactic nuclei, classified hundreds of supernovae, and obtained redshifts for thousands of galaxies that hosted a transient within the 10 deep fields of the Dark Energy Survey. We also present the second OzDES data release, containing the redshifts of almost 30 000 sources, some as faint as rAB = 24 mag, and 375 000 individual spectra. These data, in combination with the time-series photometry from the Dark Energy Survey, will be used to measure the expansion history of the Universe out to z ∼ 1.2 and the masses of hundreds of black holes out to z ∼ 4. OzDES is a template for future surveys that combine simultaneous monitoring of targets with wide-field imaging cameras and wide-field multi-object spectrographs.
The cosmological utility of galaxy cluster catalogues is primarily limited by our ability to calibrate the relation between halo mass and observable mass proxies such as cluster richness, X-ray ...luminosity or the Sunyaev-Zeldovich signal. Projection effects are a particularly pernicious systematic effect that can impact observable mass proxies; structure along the line of sight can both bias and increase the scatter of the observable mass proxies used in cluster abundance studies. In this work, we develop an empirical method to characterize the impact of projection effects on redMaPPer cluster catalogues. We use numerical simulations to validate our method and illustrate its robustness. We demonstrate that modeling of projection effects is a necessary component for cluster abundance studies capable of reaching $\approx 5\%$ mass calibration uncertainties (e.g. the Dark Energy Survey Year 1 sample). Specifically, ignoring the impact of projection effects in the observable--mass relation --- i.e. marginalizing over a log-normal model only --- biases the posterior of the cluster normalization condition $S_8 \equiv \sigma_8 (\Omega_{\rm m}/0.3)^{1/2}$ by $\Delta S_8 =0.05$, more than twice the uncertainty in the posterior for such an analysis.
Abstract
We present a spectroscopic study of the tidal tails and core of the Milky Way satellite Tucana III, collectively referred to as the Tucana III stream, using the 2dF+AAOmega spectrograph on ...the Anglo-Australian Telescope and the IMACS spectrograph on the Magellan Baade Telescope. In addition to recovering the brightest nine previously known member stars in the Tucana III core, we identify 22 members in the tidal tails. We observe strong evidence for a velocity gradient of
over at least 3° on the sky. Based on the continuity in velocity, we confirm that the Tucana III tails are real tidal extensions of Tucana III. The large velocity gradient of the stream implies that Tucana III is likely on a radial orbit. We successfully obtain metallicities for four members in the core and 12 members in the tails. We find that members close to the ends of the stream tend to be more metal-poor than members in the core, indicating a possible metallicity gradient between the center of the progenitor halo and its edge. The spread in metallicity suggests that the progenitor of the Tucana III stream is likely a dwarf galaxy rather than a star cluster. Furthermore, we find that with the precise photometry of the Dark Energy Survey data, there is a discernible color offset between metal-rich disk stars and metal-poor stream members. This metallicity-dependent color offers a more efficient method to recognize metal-poor targets and will increase the selection efficiency of stream members for future spectroscopic follow-up programs on stellar streams.
Abstract
We present Magellan/IMACS spectroscopy of three recently discovered ultra-faint Milky Way satellites, Grus II, Tucana IV, and Tucana V. We measure systemic velocities of
,
, and
for the ...three objects, respectively. Their large relative velocities demonstrate that the satellites are unrelated despite their close physical proximity. We determine a velocity dispersion for Tuc IV of
, but we cannot resolve the velocity dispersions of the other two systems. For Gru II, we place an upper limit (90% confidence) on the dispersion of
σ
< 1.9
, and for Tuc V, we do not obtain any useful limits. All three satellites have metallicities below
, but none has a detectable metallicity spread. We determine proper motions for each satellite based on Gaia astrometry and compute their orbits around the Milky Way. Gru II is on a tightly bound orbit with a pericenter of
kpc and orbital eccentricity of
. Tuc V likely has an apocenter beyond 100 kpc and could be approaching the Milky Way for the first time. The current orbit of Tuc IV is similar to that of Gru II, with a pericenter of
kpc and an eccentricity of
. However, a backward integration of the position of Tuc IV demonstrates that it collided with the Large Magellanic Cloud at an impact parameter of 4 kpc ∼120 Myr ago, deflecting its trajectory and possibly altering its internal kinematics. Based on their sizes, masses, and metallicities, we classify Gru II and Tuc IV as likely dwarf galaxies, but the nature of Tuc V remains uncertain.
We describe catalog-level simulations of Type Ia Supernova (SN Ia) light curves in the Dark Energy Survey Supernova Program (DES-SN), and in low-redshift samples from the Center for Astrophysics ...(CfA) and the Carnegie Supernova Project (CSP). These simulations are used to model biases from selection effects and light curve analysis, and to determine bias corrections for SN Ia distance moduli that are used to measure cosmological parameters. To generate realistic light curves the simulation uses a detailed SN Ia model, incorporates information from observations (PSF, sky noise, zero point), and uses summary information (e.g., detection efficiency vs. signal to noise ratio) based on 10,000 fake SN light curves whose fluxes were overlaid on images and processed with our analysis pipelines. The quality of the simulation is illustrated by predicting distributions observed in the data. Averaging within redshift bins, we find distance modulus biases up to 0.05 mag over the redshift ranges of the low-z and DES-SN samples. For individual events, particularly those with extreme red or blue color, distance biases can reach 0.4 mag. Therefore, accurately determining bias corrections is critical for precision measurements of cosmological parameters. Files used to make these corrections are available at https://des.ncsa.illinois.edu/releases/sn.
We perform a systematic search for long-term extreme variability quasars (EVQs) in the overlapping Sloan Digital Sky Survey and 3 Year Dark Energy Survey imaging, which provide light curves spanning ...more than 15 years. We identified ∼1000 EVQs with a maximum change in g-band magnitude of more than 1 mag over this period, about 10% of all quasars searched. The EVQs have Lbol ∼ 1045-1047 erg s−1 and L/LEdd ∼ 0.01-1. Accounting for selection effects, we estimate an intrinsic EVQ fraction of ∼30%-50% among all quasars over a baseline of ∼15 yr. We performed detailed multi-wavelength, spectral, and variability analyses for the EVQs and compared them to their parent quasar sample. We found that EVQs are distinct from a control sample of quasars matched in redshift and optical luminosity: (1) their UV broad emission lines have larger equivalent widths; (2) their Eddington ratios are systematically lower; and (3) they are more variable on all timescales. The intrinsic difference in quasar properties for EVQs suggests that internal processes associated with accretion are the main driver for the observed extreme long-term variability. However, despite their different properties, EVQs seem to be in the tail of a continuous distribution of quasar properties, rather than standing out as a distinct population. We speculate that EVQs are normal quasars accreting at relatively low rates, where the accretion flow is more likely to experience instabilities that drive the changes in flux by a factor of a few on multi-year timescales.
Abstract
We use numerical simulations to characterize the performance of a clustering-based method to calibrate photometric redshift biases. In particular, we cross-correlate the weak lensing source ...galaxies from the Dark Energy Survey Year 1 sample with redMaGiC galaxies (luminous red galaxies with secure photometric redshifts) to estimate the redshift distribution of the former sample. The recovered redshift distributions are used to calibrate the photometric redshift bias of standard photo-z methods applied to the same source galaxy sample. We apply the method to two photo-z codes run in our simulated data: Bayesian Photometric Redshift and Directional Neighbourhood Fitting. We characterize the systematic uncertainties of our calibration procedure, and find that these systematic uncertainties dominate our error budget. The dominant systematics are due to our assumption of unevolving bias and clustering across each redshift bin, and to differences between the shapes of the redshift distributions derived by clustering versus photo-zs. The systematic uncertainty in the mean redshift bias of the source galaxy sample is Δz ≲ 0.02, though the precise value depends on the redshift bin under consideration. We discuss possible ways to mitigate the impact of our dominant systematics in future analyses.
ABSTRACT
We present a detection of the splashback feature around galaxy clusters selected using the Sunyaev–Zel’dovich (SZ) signal. Recent measurements of the splashback feature around optically ...selected galaxy clusters have found that the splashback radius, rsp, is smaller than predicted by N-body simulations. A possible explanation for this discrepancy is that rsp inferred from the observed radial distribution of galaxies is affected by selection effects related to the optical cluster-finding algorithms. We test this possibility by measuring the splashback feature in clusters selected via the SZ effect in data from the South Pole Telescope SZ survey and the Atacama Cosmology Telescope Polarimeter survey. The measurement is accomplished by correlating these cluster samples with galaxies detected in the Dark Energy Survey Year 3 data. The SZ observable used to select clusters in this analysis is expected to have a tighter correlation with halo mass and to be more immune to projection effects and aperture-induced biases, potentially ameliorating causes of systematic error for optically selected clusters. We find that the measured rsp for SZ-selected clusters is consistent with the expectations from simulations, although the small number of SZ-selected clusters makes a precise comparison difficult. In agreement with previous work, when using optically selected redMaPPer clusters with similar mass and redshift distributions, rsp is ∼2σ smaller than in the simulations. These results motivate detailed investigations of selection biases in optically selected cluster catalogues and exploration of the splashback feature around larger samples of SZ-selected clusters. Additionally, we investigate trends in the galaxy profile and splashback feature as a function of galaxy colour, finding that blue galaxies have profiles close to a power law with no discernible splashback feature, which is consistent with them being on their first infall into the cluster.
Abstract
We use weak-lensing shear measurements to determine the mean mass of optically selected galaxy clusters in Dark Energy Survey Science Verification data. In a blinded analysis, we split the ...sample of more than 8000 redMaPPer clusters into 15 subsets, spanning ranges in the richness parameter 5 ≤ λ ≤ 180 and redshift 0.2 ≤ z ≤ 0.8, and fit the averaged mass density contrast profiles with a model that accounts for seven distinct sources of systematic uncertainty: shear measurement and photometric redshift errors; cluster-member contamination; miscentring; deviations from the NFW halo profile; halo triaxiality and line-of-sight projections. We combine the inferred cluster masses to estimate the joint scaling relation between mass, richness and redshift,
${\cal M}(\lambda ,z) \propto M_0 \lambda ^{F} (1+z)^{G}$
. We find
$M_0 \equiv \langle M_{200\mathrm{m}}\,|\,\lambda =30,z=0.5 \rangle = 2.35 \pm 0.22\ \rm {(stat)} \pm 0.12\ \rm {(sys)} \times \ 10^{14}\ \mathrm{M}_{{\odot }}$
, with
$F = 1.12\,\pm \,0.20\ \rm {(stat)}\, \pm \, 0.06\ \rm {(sys)}$
and
$G = 0.18\,\pm \, 0.75\ \rm {(stat)}\, \pm \, 0.24\ \rm {(sys)}$
. The amplitude of the mass–richness relation is in excellent agreement with the weak-lensing calibration of redMaPPer clusters in SDSS by Simet et al. and with the Saro et al. calibration based on abundance matching of SPT-detected clusters. Our results extend the redshift range over which the mass–richness relation of redMaPPer clusters has been calibrated with weak lensing from z ≤ 0.3 to z ≤ 0.8. Calibration uncertainties of shear measurements and photometric redshift estimates dominate our systematic error budget and require substantial improvements for forthcoming studies.
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