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Register a new userDark Energy Survey Supernovae: Simulations and Survey Strategy
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Joseph P. Bernstein
Publication date
2009
fields
Physics
and research's language is
English
Authors
J. P. Bernstein
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We present simulations for the Dark Energy Survey (DES) using a new code suite (SNANA) that generates realistic supernova light curves accounting for atmospheric seeing conditions and intrinsic supernova luminosity variations using MLCS2k2 or SALT2 models. Errors include stat-noise from photo-statistics and sky noise. We applied SNANA to simulate DES supernova observations and employed an MLCS-based fitter to obtain the distance modulus for each simulated light curve. We harnessed the light curves in order to study selection biases for high-redshift supernovae and to constrain the optimal DES observing strategy using the Dark Energy Task Force figure of merit.
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We present an analysis of supernova light curves simulated for the upcoming Dark Energy Survey (DES) supernova search. The simulations employ a code suite that generates and fits realistic light curves in order to obtain distance modulus/redshift pairs that are passed to a cosmology fitter. We investigated several different survey strategies including field selection, supernova selection biases, and photometric redshift measurements. Using the results of this study, we chose a 30 square degree search area in the griz filter set. We forecast 1) that this survey will provide a homogeneous sample of up to 4000 Type Ia supernovae in the redshift range 0.05<z<1.2, and 2) that the increased red efficiency of the DES camera will significantly improve high-redshift color measurements. The redshift of each supernova with an identified host galaxy will be obtained from spectroscopic observations of the host. A supernova spectrum will be obtained for a subset of the sample, which will be utilized for control studies. In addition, we have investigated the use of combined photometric redshifts taking into account data from both the host and supernova. We have investigated and estimated the likely contamination from core-collapse supernovae based on photometric identification, and have found that a Type Ia supernova sample purity of up to 98% is obtainable given specific assumptions. Furthermore, we present systematic uncertainties due to sample purity, photometric calibration, dust extinction priors, filter-centroid shifts, and inter-calibration. We conclude by estimating the uncertainty on the cosmological parameters that will be measured from the DES supernova data.
This paper details the modeling pipeline and validates the baseline analysis choices of the DES Year 3 joint analysis of galaxy clustering and weak lensing (a so-called 3$times$2pt analysis). These analysis choices include the specific combination of cosmological probes, priors on cosmological and systematics parameters, model parameterizations for systematic effects and related approximations, and angular scales where the model assumptions are validated. We run a large number of simulated likelihood analyses using synthetic data vectors to test the robustness of our baseline analysis. We demonstrate that the DES Year 3 modeling pipeline, including the calibrated scale cuts, is sufficiently accurate relative to the constraining power of the DES Year 3 analyses. Our systematics mitigation strategy accounts for astrophysical systematics, such as galaxy bias, intrinsic alignments, source and lens magnification, baryonic effects, and source clustering, as well as for uncertainties in modeling the matter power spectrum, reduced shear, and estimator effects. We further demonstrate excellent agreement between two independently-developed modeling pipelines, and thus rule out any residual uncertainties due to the numerical implementation.
We present a sample of 21 hydrogen-free superluminous supernovae (SLSNe-I), and one hydrogen-rich SLSN (SLSN-II) detected during the five-year Dark Energy Survey (DES). These SNe, located in the redshift range 0.220<z<1.998, represent the largest homogeneously-selected sample of SLSN events at high redshift. We present the observed g,r, i, z light curves for these SNe, which we interpolate using Gaussian Processes. The resulting light curves are analysed to determine the luminosity function of SLSN-I, and their evolutionary timescales. The DES SLSN-I sample significantly broadens the distribution of SLSN-I light curve properties when combined with existing samples from the literature. We fit a magnetar model to our SLSNe, and find that this model alone is unable to replicate the behaviour of many of the bolometric light curves. We search the DES SLSN-I light curves for the presence of initial peaks prior to the main light-curve peak. Using a shock breakout model, our Monte Carlo search finds that 3 of our 14 events with pre-max data display such initial peaks. However, 10 events show no evidence for such peaks, in some cases down to an absolute magnitude of <-16, suggesting that such features are not ubiquitous to all SLSN-I events. We also identify a red pre-peak feature within the light curve of one SLSN, which is comparable to that observed within SN2018bsz.
We present spectroscopy from the first three seasons of the Dark Energy Survey Supernova Program (DES-SN). We describe the supernova spectroscopic program in full: strategy, observations, data reduction, and classification. We have spectroscopically confirmed 307 supernovae, including 251 type Ia supernovae (SNe Ia) over a redshift range of $0.017 < z < 0.85$. We determine the effective spectroscopic selection function for our sample, and use it to investigate the redshift-dependent bias on the distance moduli of SNe Ia we have classified. We also provide a full overview of the strategy, observations, and data products of DES-SN, which has discovered 12,015 likely supernovae during these first three seasons. The data presented here are used for the first cosmology analysis by DES-SN (DES-SN3YR), the results of which are given in DES Collaboration (2018a).
We consider the effects of weak gravitational lensing on observations of 196 spectroscopically confirmed Type Ia Supernovae (SNe Ia) from years 1 to 3 of the Dark Energy Survey (DES). We simultaneously measure both the angular correlation function and the non-Gaussian skewness caused by weak lensing. This approach has the advantage of being insensitive to the intrinsic dispersion of SNe Ia magnitudes. We model the amplitude of both effects as a function of $sigma_8$, and find $sigma_8 = 1.2^{+0.9}_{-0.8}$. We also apply our method to a subsample of 488 SNe from the Joint Light-curve Analysis (JLA) (chosen to match the redshift range we use for this work), and find $sigma_8 = 0.8^{+1.1}_{-0.7}$. The comparable uncertainty in $sigma_8$ between DES-SN and the larger number of SNe from JLA highlights the benefits of homogeneity of the DES-SN sample, and improvements in the calibration and data analysis.
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