Chemodynamics of the Milky Way. I. The first year of APOGEE data

We investigate the chemo-kinematic properties of the Milky Way disc by exploring the first year of data from the Apache Point Observatory Galactic Evolution Experiment (APOGEE), and compare our results to smaller optical high-resolution samples in the literature, as well as results from lower resolution surveys such as GCS, SEGUE and RAVE. We start by selecting a high-quality sample in terms of chemistry (____sim 20.000 stars) and, after computing distances and orbital parameters for this sample, we employ a number of useful subsets to formulate constraints on Galactic chemical and chemodynamical evolution processes in the Solar neighbourhood and beyond (e.g., metallicity distributions -- MDFs, [____alpha/Fe] vs. [Fe/H] diagrams, and abundance gradients). Our red giant sample spans distances as large as 10 kpc from the Sun. We find remarkable agreement between the recently published local (d $<$ 100 pc) high-resolution high-S/N HARPS sample and our local HQ sample (d $<$ 1 kpc). The local MDF peaks slightly below solar metallicity, and exhibits an extended tail towards [Fe/H] $= -$1, whereas a sharper cut-off is seen at larger metallicities. The APOGEE data also confirm the existence of a gap in the [____alpha/Fe] vs. [Fe/H] abundance diagram. When expanding our sample to cover three different Galactocentric distance bins, we find the high-[____alpha/Fe] stars to be rare towards the outer zones, as previously suggested in the literature. For the gradients in [Fe/H] and [____alpha/Fe], measured over a range of 6 $<$ R $<$ 11 kpc in Galactocentric distance, we find a good agreement with the gradients traced by the GCS and RAVE dwarf samples. For stars with 1.5 $<$ z $<$ 3 kpc, we find a positive metallicity gradient and a negative gradient in [____alpha/Fe]

VDES J2325-5229 a z=2.7 gravitationally lensed quasar discovered using morphology independent supervised machine learning

We present the discovery and preliminary characterization of a gravitationally lensed quasar with a source redshift $\textit{zs}$ = 2.74 and image separation of 2.9 arcsec lensed by a foreground $\textit{zl}$ = 0.40 elliptical galaxy. Since optical observations of gravitationally lensed quasars show the lens system as a superposition of multiple point sources and a foreground lensing galaxy, we have developed a morphology-independent multi-wavelength approach to the photometric selection of lensed quasar candidates based on Gaussian Mixture Models (GMM) supervised machine learning. Using this technique and $\textit{gi}$ multicolour photometric observations from the Dark Energy Survey (DES), near-IR $\textit{JK}$ photometry from the VISTA Hemisphere Survey (VHS) and WISE mid-IR photometry, we have identified a candidate system with two catalogue components with $\textit{iAB}$ = 18.61 and $\textit{iAB}$ = 20.44 comprising an elliptical galaxy and two blue point sources. Spectroscopic follow-up with NTT and the use of an archival AAT spectrum show that the point sources can be identified as a lensed quasar with an emission line redshift of $\textit{z}$ = 2.739 ± 0.003 and a foreground early-type galaxy with $\textit{z}$ = 0.400 ± 0.002. We model the system as a single isothermal ellipsoid and find the Einstein radius θE ∼ 1.47 arcsec, enclosed mass $\textit{M}$enc ∼ 4 × 10$^{11}$$\textit{M}$⊙ and a time delay of ∼52 d. The relatively wide separation, month scale time delay duration and high redshift make this an ideal system for constraining the expansion rate beyond a redshift of 1.FO is supported jointly by CAPES (the Science without Borders programme) and the Cambridge Commonwealth Trust. RGM, CAL, MWA, MB, SLR acknowledge the support of UK Science and Technology Research Council (STFC). AJC acknowledges the support of a Raymond and Beverly Sackler visiting fellowship at the Institute of Astronomy. For further information regarding funding please visit the publisher's website

DeepZipper. II. Searching for Lensed Supernovae in Dark Energy Survey Data with Deep Learning

Gravitationally lensed supernovae (LSNe) are important probes of cosmic expansion, but they remain rare and difficult to find. Current cosmic surveys likely contain 5-10 LSNe in total while next-generation experiments are expected to contain several hundred to a few thousand of these systems. We search for these systems in observed Dark Energy Survey (DES) five year SN fields—10 3 sq. deg. regions of sky imaged in the griz bands approximately every six nights over five years. To perform the search, we utilize the DeepZipper approach: a multi-branch deep learning architecture trained on image-level simulations of LSNe that simultaneously learns spatial and temporal relationships from time series of images. We find that our method obtains an LSN recall of 61.13% and a false-positive rate of 0.02% on the DES SN field data. DeepZipper selected 2245 candidates from a magnitude-limited (m i < 22.5) catalog of 3,459,186 systems. We employ human visual inspection to review systems selected by the network and find three candidate LSNe in the DES SN fields

The Evolution of Active Galactic Nuclei in Clusters of Galaxies from the Dark Energy Survey

The correlation between active galactic nuclei (AGN) and environment provides important clues to AGN fueling and the relationship of black hole growth to galaxy evolution. In this paper, we analyze the fraction of galaxies in clusters hosting AGN as a function of redshift and cluster richness for X-ray detected AGN associated with clusters of galaxies in Dark Energy Survey (DES) Science Verification data. The present sample includes 33 AGN with L_X > 10^43 ergs s^-1 in non-central, host galaxies with luminosity greater than 0.5 L* from a total sample of 432 clusters in the redshift range of 0.10.7. This result is in good agreement with previous work and parallels the increase in star formation in cluster galaxies over the same redshift range. However, the AGN fraction in clusters is observed to have no significant correlation with cluster mass. Future analyses with DES Year 1 through Year 3 data will be able to clarify whether AGN activity is correlated to cluster mass and will tightly constrain the relationship between cluster AGN populations and redshift

Testing the lognormality of the galaxy and weak lensing convergence distributions from Dark Energy Survey maps

It is well known that the probability distribution function (PDF) of galaxy density contrast is approximately lognormal; whether the PDF of mass fluctuations derived from weak lensing convergence (kappa_WL) is lognormal is less well established. We derive PDFs of the galaxy and projected matter density distributions via the Counts in Cells (CiC) method. We use maps of galaxies and weak lensing convergence produced from the Dark Energy Survey (DES) Science Verification data over 139 deg^2. We test whether the underlying density contrast is well described by a lognormal distribution for the galaxies, the convergence and their joint PDF. We confirm that the galaxy density contrast distribution is well modeled by a lognormal PDF convolved with Poisson noise at angular scales from 10-40 arcmin (corresponding to physical scales of 3-10 Mpc). We note that as kappa_WL is a weighted sum of the mass fluctuations along the line of sight, its PDF is expected to be only approximately lognormal. We find that the kappa_WL distribution is well modeled by a lognormal PDF convolved with Gaussian shape noise at scales between 10 and 20 arcmin, with a best-fit chi^2/DOF of 1.11 compared to 1.84 for a Gaussian model, corresponding to p-values 0.35 and 0.07 respectively, at a scale of 10 arcmin. Above 20 arcmin a simple Gaussian model is sufficient. The joint PDF is also reasonably fitted by a bivariate lognormal. As a consistency check we compare the variances derived from the lognormal modelling with those directly measured via CiC. Our methods are validated against maps from the MICE Grand Challenge N-body simulation

Imprint of DES super-structures on the Cosmic Microwave Background

Small temperature anisotropies in the Cosmic Microwave Background can be sourced by density perturbations via the late-time integrated Sachs-Wolfe effect. Large voids and superclusters are excellent environments to make a localized measurement of this tiny imprint. In some cases excess signals have been reported. We probed these claims with an independent data set, using the first year data of the Dark Energy Survey in a different footprint, and using a different super-structure finding strategy. We identified 52 large voids and 102 superclusters at redshifts $0.2 < z < 0.65$. We used the Jubilee simulation to a priori evaluate the optimal ISW measurement configuration for our compensated top-hat filtering technique, and then performed a stacking measurement of the CMB temperature field based on the DES data. For optimal configurations, we detected a cumulative cold imprint of voids with $\Delta T_{f} \approx -5.0\pm3.7~\mu K$ and a hot imprint of superclusters $\Delta T_{f} \approx 5.1\pm3.2~\mu K$ ; this is $\sim1.2\sigma$ higher than the expected $|\Delta T_{f}| \approx 0.6~\mu K$ imprint of such super-structures in $\Lambda$CDM. If we instead use an a posteriori selected filter size ($R/R_{v}=0.6$), we can find a temperature decrement as large as $\Delta T_{f} \approx -9.8\pm4.7~\mu K$ for voids, which is $\sim2\sigma$ above $\Lambda$CDM expectations and is comparable to previous measurements made using SDSS super-structure data

Environmental dependence of the galaxy stellar mass function in the Dark Energy Survey Science Verification Data

Measurements of the galaxy stellar mass function are crucial to understand the formation of galaxies in the Universe. In a hierarchical clustering paradigm it is plausible that there is a connection between the properties of galaxies and their environments. Evidence for environmental trends has been established in the local Universe. The Dark Energy Survey (DES) provides large photometric datasets that enable further investigation of the assembly of mass. In this study we use ~3.2 million galaxies from the (South Pole Telescope) SPT-East field in the DES science verification (SV) dataset. From grizY photometry we derive galaxy stellar masses and absolute magnitudes, and determine the errors on these properties using Monte-Carlo simulations using the full photometric redshift probability distributions. We compute galaxy environments using a fixed conical aperture for a range of scales. We construct galaxy environment probability distribution functions and investigate the dependence of the environment errors on the aperture parameters. We compute the environment components of the galaxy stellar mass function for the redshift range 0.15<z<1.05. For z<0.75 we find that the fraction of massive galaxies is larger in high density environment than in low density environments. We show that the low density and high density components converge with increasing redshift up to z~1.0 where the shapes of the mass function components are indistinguishable. Our study shows how high density structures build up around massive galaxies through cosmic time

Cosmic Voids and Void Lensing in the Dark Energy Survey Science Verification Data

Galaxies and their dark matter halos populate a complicated filamentary network around large, nearly empty regions known as cosmic voids. Cosmic voids are usually identified in spectroscopic galaxy surveys, where 3D information about the large-scale structure of the Universe is available. Although an increasing amount of photometric data is being produced, its potential for void studies is limited since photometric redshifts induce line-of-sight position errors of $\sim50$ Mpc/$h$ or more that can render many voids undetectable. In this paper we present a new void finder designed for photometric surveys, validate it using simulations, and apply it to the high-quality photo-$z$ redMaGiC galaxy sample of the Dark Energy Survey Science Verification (DES-SV) data. The algorithm works by projecting galaxies into 2D slices and finding voids in the smoothed 2D galaxy density field of the slice. Fixing the line-of-sight size of the slices to be at least twice the photo-$z$ scatter, the number of voids found in these projected slices of simulated spectroscopic and photometric galaxy catalogs is within 20% for all transverse void sizes, and indistinguishable for the largest voids of radius $\sim 70$ Mpc/$h$ and larger. The positions, radii, and projected galaxy profiles of photometric voids also accurately match the spectroscopic void sample. Applying the algorithm to the DES-SV data in the redshift range $0.2<z<0.8$, we identify 87 voids with comoving radii spanning the range 18-120 Mpc/$h$, and carry out a stacked weak lensing measurement. With a significance of $4.4\sigma$, the lensing measurement confirms the voids are truly underdense in the matter field and hence not a product of Poisson noise, tracer density effects or systematics in the data. It also demonstrates, for the first time in real data, the viability of void lensing studies in photometric surveys

Mass Calibration of Optically Selected des Clusters Using a Measurement of CMB-cluster Lensing with SPTpol Data

We use cosmic microwave background (CMB) temperature maps from the 500 deg 2 SPTpol survey to measure the stacked lensing convergence of galaxy clusters from the Dark Energy Survey (DES) Year-3 redMaPPer (RM) cluster catalog. The lensing signal is extracted through a modified quadratic estimator designed to be unbiased by the thermal Sunyaev-Zel'dovich (tSZ) effect. The modified estimator uses a tSZ-free map, constructed from the SPTpol 95 and 150 GHz data sets, to estimate the background CMB gradient. For lensing reconstruction, we employ two versions of the RM catalog: a flux-limited sample containing 4003 clusters and a volume-limited sample with 1741 clusters. We detect lensing at a significance of 8.7σ(6.7σ) with the flux (volume)-limited sample. By modeling the reconstructed convergence using the Navarro-Frenk-White profile, we find the average lensing masses to be M 200m = (1.62 -0.25+0.32 [stat] ± 0.04 [sys.]) and (1.28 -0.18+0.14 [stat] ± 0.03[sys.])× 10 14 M ⊙ for the volume- and flux-limited samples, respectively. The systematic error budget is much smaller than the statistical uncertainty and is dominated by the uncertainties in the RM cluster centroids. We use the volume-limited sample to calibrate the normalization of the mass-richness scaling relation, and find a result consistent with the galaxy weak-lensing measurements from DES

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

Spatially-varying depth and characteristics of observing conditions, such as seeing, airmass, or sky background, are major sources of systematic uncertainties in modern galaxy survey analyses, in particular 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 complementarity of these two approaches by comparing the SV data with the BCC-UFig, a synthetic sky catalogue generated by forward-modelling of the DES SV images. We analyse 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 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. 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 (LSST), which will require detailed null-tests and realistic end-to-end image simulations to correctly interpret the deep, high-cadence observations of the sky