A. Palmese

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.

How Many Kilonovae Can Be Found in Past, Present, and Future Survey Data Sets?

The discovery of a kilonova (KN) associated with the Advanced LIGO (aLIGO)/Virgo event GW170817 opens up new avenues of multi-messenger astrophysics. Here, using realistic simulations, we provide estimates of the number of KNe that could be found in data from past, present, and future surveys without a gravitational-wave trigger. For the simulation, we construct a spectral time-series model based on the DES-GW multi-band light curve from the single known KN event, and we use an average of BNS rates from past studies of 10^3 Gpc^(-3) yr^(-1), consistent with the one event found so far. Examining past and current data sets from transient surveys, the number of KNe we expect to find for ASAS-SN, SDSS, PS1, SNLS, DES, and SMT is between 0 and 0.3. We predict the number of detections per future survey to be 8.3 from ATLAS, 10.6 from ZTF, 5.5/69 from LSST (the Deep Drilling/Wide Fast Deep), and 16.0 from WFIRST. The maximum redshift of KNe discovered for each survey is z = 0.8 for WFIRST, z = 0.25 for LSST, and z = 0.04 for ZTF and ATLAS. This maximum redshift for WFIRST is well beyond the sensitivity of aLIGO and some future GW missions. For the LSST survey, we also provide contamination estimates from Type Ia and core-collapse supernovae: after light curve and template-matching requirements, we estimate a background of just two events. More broadly, we stress that future transient surveys should consider how to optimize their search strategies to improve their detection efficiency and to consider similar analyses for GW follow-up programs.

The Splashback Feature around DES Galaxy Clusters: Galaxy Density and Weak Lensing Profiles

Splashback refers to the process of matter that is accreting onto a dark matter halo reaching its first orbital apocenter and turning around in its orbit. The clustercentric radius at which this process occurs, r(sp), defines a halo boundary that is connected to the dynamics of the cluster. A rapid decline in the halo profile is expected near rsp. We measure the galaxy number density and weak lensing mass profiles around REDMAPPER galaxy clusters in the first-year Dark Energy Survey (DES) data. For a cluster sample with mean M-200m mass approximate to 2.5 x 10(14)M(circle dot), we find strong evidence of a splashback-like steepening of the galaxy density profile and measure r(sp) = 1.13 +/- 0.07 h(-1) Mpc, consistent with the earlier Sloan Digital Sky Survey measurements of More et al. and Baxter et al. Moreover, our weak lensing measurement demonstrates for the first time the existence of a splashback-like steepening of the matter profile of galaxy clusters. We measure r(sp) = 1.34 +/- 0.21 h(-1) Mpc from the weak lensing data, in good agreement with our galaxy density measurements. For different cluster and galaxy samples, we find that, consistent with.CDM simulations, rsp scales with R-200m and does not evolve with redshift over the redshift range of 0.3-0.6. We also find that potential systematic effects associated with the REDMAPPER algorithm may impact the location of rsp. We discuss the progress needed to understand the systematic uncertainties and fully exploit forthcoming data from DES and future surveys, emphasizing the importance of more realistic mock catalogs and independent cluster samples.

Evidence for Dynamically Driven Formation of the GW170817 Neutron Star Binary in NGC 4993

We present a study of NGC 4993, the host galaxy of the GW170817 gravitational-wave event, the GRB 170817A short gamma-ray burst (sGRB), and the AT 2017gfo kilonova. We use Dark Energy Camera imaging, AAT spectra, and publicly available data, relating our findings to binary neutron star (BNS) formation scenarios and merger delay timescales. NGC 4993 is a nearby early-type galaxy, with an i-band Sersic index n = 4.0 and low asymmetry (A = 0.04 ± 0.01). These properties are unusual for sGRB hosts. However, NGC 4993 presents shell-like structures and dust lanes indicative of a recent galaxy merger, with the optical transient located close to a shell. We constrain the star formation history (SFH) of the galaxy assuming that the galaxy merger produced a star formation burst, but find little to no ongoing star formation in either spatially resolved broadband SED or spectral fitting. We use the best-fit SFH to estimate the BNS merger rate in this type of galaxy, as R^(gal)_(NSM) = 5.7^(+0.57)_(-3.3) x 10^(-6) yr^(-1). If star formation is the only considered BNS formation scenario, the expected number of BNS mergers from early-type galaxies detectable with LIGO during its first two observing seasons is 0.038^(+0.004)_(-0.022), as opposed to ~0.5 from all galaxy types. Hypothesizing that the binary formed due to dynamical interactions during the galaxy merger, the subsequent time elapsed can constrain the delay time of the BNS coalescence. By using velocity dispersion estimates and the position of the shells, we find that the galaxy merger occurred t_(mer) ≾ 200 Myr prior to the BNS coalescence.

DES science portal: Computing photometric redshifts

A significant challenge facing photometric surveys for cosmological purposes is the need to produce reliable redshift estimates. The estimation of photometric redshifts (photo-zs) has been consolidated as the standard strategy to bypass the high production costs and incompleteness of spectroscopic redshift samples. Training-based photo-z methods require the preparation of a high-quality list of spectroscopic redshifts, which needs to be constantly updated. The photo-z training, validation, and estimation must be performed in a consistent and reproducible way in order to accomplish the scientific requirements. To meet this purpose, we developed an integrated web-based data interface that not only provides the framework to carry out the above steps in a systematic way, enabling the ease testing and comparison of different algorithms, but also addresses the processing requirements by parallelizing the calculation in a transparent way for the user. This framework called the Science Portal (hereafter Portal) was developed in the context the Dark Energy Survey (DES) to facilitate scientific analysis. In this paper, we show how the Portal can provide a reliable environment to access vast datasets, provide validation algorithms and metrics, even in the case of multiple photo-zs methods. It is possible to maintain the provenance between the steps of a chain of workflows while ensuring reproducibility of the results. We illustrate how the Portal can be used to provide photo-z estimates using the DES first year (Y1A1) data. While the DES collaboration is still developing techniques to obtain more precise photo-zs, having a structured framework like the one presented here is critical for the systematic vetting of DES algorithmic improvements and the consistent production of photo-zs in future DES releases.