Eve Kovacs

Type Ia supernovae selection and forecast of cosmology constraints for the Dark Energy Survey

Abstract We present the results of a study of selection criteria to identify Type Ia supernovae photometrically in a simulated mixed sample of Type Ia supernovae and core collapse supernovae. The simulated sample is a mockup of the expected results of the Dark Energy Survey. Fits to the MLCS2k2 and SALT2 Type Ia supernova models are compared and used to help separate the Type Ia supernovae from the core collapse sample. The Dark Energy Task Force Figure of Merit (modified to include core collapse supernovae systematics) is used to discriminate among the various selection criteria. This study of varying selection cuts for Type Ia supernova candidates is the first to evaluate core collapse contamination using the Figure of Merit. Different factors that contribute to the Figure of Merit are detailed. With our analysis methods, both SALT2 and MLCS2k2 Figures of Merit improve with tighter selection cuts and higher purities, peaking at 98% purity.

DES15E2mlf: A Spectroscopically Confirmed Superluminous Supernova that Exploded 3.5 Gyr After the Big Bang

We present the Dark Energy Survey (DES) discovery of DES15E2mlf, the most distant superluminous supernova (SLSN) spectroscopically confirmed to date. The light curves and Gemini spectroscopy of DES15E2mlf indicate that it is a Type I superluminous supernova (SLSN-I) at z = 1.861 (a lookback time of ∼10 Gyr) and peaking at MAB = −22.3 ± 0.1 mag. Given the high redshift, our data probe the rest-frame ultraviolet (1400–3500 A) properties of the SN, finding velocity of the C III feature changes by ∼5600 km s−1 over 14 d around maximum light. We find the host galaxy of DES15E2mlf has a stellar mass of 3.5+3.6 −2.4 × 109 M, which is more massive than the typical SLSN-I host galaxy.

Large-Scale Simulations of Sky Surveys

Large-volume sky surveys have accessed the Universes vast temporal and spatial expanse via a remarkable set of measurements, and many more are sure to follow. To make new predictions for these cosmological observations and to properly interpret them, large-scale numerical simulation and modeling has become an essential tool. Here, the authors discuss Hardware/Hybrid Accelerated Cosmology Code (HACC), an extreme-scale N-body cosmology code and its associated analysis framework, focusing on the complexity of the analysis workflow, which is as important as running the underlying simulation.

Visualization of multivariate dark matter halos in cosmology simulations

In this poster, we present a new approach to visualize multivariate dark matter halos representing the spheroid part of galaxies, the disk part of galaxies, black holes and the halo itself. The data being visualized here is an end result of tracking the evolution of cosmic structures called dark matter halos in cosmological simulation and evaluating the formation and evolution of galaxies within. Cosmologists have traditionally visualized individual galaxies in the form of two dimensional density maps, graphs and parallel coordinates. We introduce a new way of mapping multiple parameters of dark matter halos to a halo-icon. This allows the scientist to view all of the parameters associated with the dark matter halos in one visualization.

DESCQA: An Automated Validation Framework for Synthetic Sky Catalogs

Author(s): Mao, YY; Kovacs, E; Heitmann, K; Uram, TD; Benson, AJ; Campbell, D; Cora, SA; Derose, J; Matteo, TD; Habib, S; Hearin, AP; Kalmbach, JB; Krughoff, KS; Lanusse, F; Lukic, Z; Mandelbaum, R; Newman, JA; Padilla, N; Paillas, E; Pope, A; Ricker, PM; Ruiz, AN; Tenneti, A; Vega-Martinez, CA; Wechsler, RH; Zhou, R; Zu, Y | Abstract:

Photometric classification and redshift estimation of LSST Supernovae

Supernova (SN) classification and redshift estimation using photometric data only have become very important for the Large Synoptic Survey Telescope (LSST), given the large number of SNe that LSST will observe and the impossibility of spectroscopically following up all the SNe. We investigate the performance of an SN classifier that uses SN colours to classify LSST SNe with the Random Forest classification algorithm. Our classifier results in an area-under-the-curve of 0.98 which represents excellent classification. We are able to obtain a photometric SN sample containing 99 per cent SNe Ia by choosing a probability threshold. We estimate the photometric redshifts (photo-z) of SNe in our sample by fitting the SN light curves using the SALT2 model with nested sampling. We obtain a mean bias (⟨z_(phot) − z_(spec)⟩) of 0.012 with σ((z_(phot) − z_(spec))/(1+z_(spec)) = 0.0294 without using a host-galaxy photo-z prior, and a mean bias (⟨z_(phot) − z_(spec)⟩) of 0.0017 with σ((z_(phot) − z_(spec))/(1 + z_(spec)) = 0.0116 using a host-galaxy photo-z prior. Assuming a flat ΛCDM model with Ωm = 0.3, we obtain Ωm of 0.305 ± 0.008 (statistical errors only), using the simulated LSST sample of photometric SNe Ia (with intrinsic scatter σ_(int) = 0.11) derived using our methodology without using host-galaxy photo-z prior. Our method will help boost the power of SNe from the LSST as cosmological probes.