This content will become publicly available on June 3, 2023

The Dark Energy Survey Supernova Program: Cosmological biases from supernova photometric classification
Abstract Cosmological analyses of samples of photometrically-identified type Ia supernovae (SNe Ia) depend on understanding the effects of ‘contamination’ from core-collapse and peculiar SN Ia events. We employ a rigorous analysis using the photometric classifier SuperNNova on state-of-the-art simulations of SN samples to determine cosmological biases due to such ‘non-Ia’ contamination in the Dark Energy Survey (DES) 5-year SN sample. Depending on the non-Ia SN models used in the SuperNNova training and testing samples, contamination ranges from 0.8–3.5 per cent, with a classification efficiency of 97.7–99.5 per cent. Using the Bayesian Estimation Applied to Multiple Species (BEAMS) framework and its extension BBC (‘BEAMS with Bias Correction’), we produce a redshift-binned Hubble diagram marginalised over contamination and corrected for selection effects, and use it to constrain the dark energy equation-of-state, w. Assuming a flat universe with Gaussian ΩM prior of 0.311 ± 0.010, we show that biases on w are <0.008 when using SuperNNova, with systematic uncertainties associated with contamination around 10 per cent of the statistical uncertainty on w for the DES-SN sample. An alternative approach of discarding contaminants using outlier rejection techniques (e.g., Chauvenet’s criterion) in place of SuperNNova leads to biases on w that are larger but still more »
Authors:
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
Award ID(s):
Publication Date:
NSF-PAR ID:
10351278
Journal Name:
Monthly Notices of the Royal Astronomical Society
ISSN:
0035-8711
National Science Foundation
##### More Like this
1. ABSTRACT As part of the cosmology analysis using Type Ia Supernovae (SN Ia) in the Dark Energy Survey (DES), we present photometrically identified SN Ia samples using multiband light curves and host galaxy redshifts. For this analysis, we use the photometric classification framework SuperNNovatrained on realistic DES-like simulations. For reliable classification, we process the DES SN programme (DES-SN) data and introduce improvements to the classifier architecture, obtaining classification accuracies of more than 98 per cent on simulations. This is the first SN classification to make use of ensemble methods, resulting in more robust samples. Using photometry, host galaxy redshifts, and a classification probability requirement, we identify 1863 SNe Ia from which we select 1484 cosmology-grade SNe Ia spanning the redshift range of 0.07 < z < 1.14. We find good agreement between the light-curve properties of the photometrically selected sample and simulations. Additionally, we create similar SN Ia samples using two types of Bayesian Neural Network classifiers that provide uncertainties on the classification probabilities. We test the feasibility of using these uncertainties as indicators for out-of-distribution candidates and model confidence. Finally, we discuss the implications of photometric samples and classification methods for future surveys such as Vera C. Rubin Observatory Legacy Surveymore »
2. ABSTRACT The 5-yr Dark Energy Survey Supernova Programme (DES-SN) is one of the largest and deepest transient surveys to date in terms of volume and number of supernovae. Identifying and characterizing the host galaxies of transients plays a key role in their classification, the study of their formation mechanisms, and the cosmological analyses. To derive accurate host galaxy properties, we create depth-optimized coadds using single-epoch DES-SN images that are selected based on sky and atmospheric conditions. For each of the five DES-SN seasons, a separate coadd is made from the other four seasons such that each SN has a corresponding deep coadd with no contaminating SN emission. The coadds reach limiting magnitudes of order ∼27 in g band, and have a much smaller magnitude uncertainty than the previous DES-SN host templates, particularly for faint objects. We present the resulting multiband photometry of host galaxies for samples of spectroscopically confirmed type Ia (SNe Ia), core-collapse (CCSNe), and superluminous (SLSNe) as well as rapidly evolving transients (RETs) discovered by DES-SN. We derive host galaxy stellar masses and probabilistically compare stellar-mass distributions to samples from other surveys. We find that the DES spectroscopically confirmed sample of SNe Ia selects preferentially fewer high-mass hostsmore »
3. Abstract

Current and future cosmological analyses with Type Ia supernovae (SNe Ia) face three critical challenges: (i) measuring the redshifts from the SNe or their host galaxies; (ii) classifying the SNe without spectra; and (iii) accounting for correlations between the properties of SNe Ia and their host galaxies. We present here a novel approach that addresses each of these challenges. In the context of the Dark Energy Survey (DES), we analyze an SN Ia sample with host galaxies in the redMaGiC galaxy catalog, a selection of luminous red galaxies. redMaGiC photo-zestimates are expected to be accurate toσΔz/(1+z)∼ 0.02. The DES-5YR photometrically classified SN Ia sample contains approximately 1600 SNe, and 125 of these SNe are in redMaGiC galaxies. We demonstrate that redMaGiC galaxies almost exclusively host SNe Ia, reducing concerns relating to classification uncertainties. With this subsample, we find similar Hubble scatter (to within ∼0.01 mag) using photometric redshifts in place of spectroscopic redshifts. With detailed simulations, we show that the bias due to using redMaGiC photo-zs on the measurement of the dark energy equation of statewis up to Δw∼ 0.01–0.02. With real data, we measure a difference inwwhen using the redMaGiC photo-zs versus the spec-zs of Δw= 0.005. Finally,more »

4. ABSTRACT

Distance-redshift diagrams probe expansion history of the Universe. We show that the stellar mass-binding energy (massE) relation of galaxies proposed in our previous study offers a new distance ruler at cosmic scales. By using elliptical galaxies in the main galaxy sample of the Sloan Digital Sky Survey Data Release 7, we construct a distance-redshift diagram over the redshift range from 0.05 to 0.2 with the massE ruler. The best-fit dark energy density is 0.675 ± 0.079  for flat Λ-cold dark matter (ΛCDM) model, consistent with those by other probes. At the median redshift of 0.11, the median distance is estimated to have a fractional error of 0.34 per cent, much lower than those by supernova (SN) Ia and baryonic acoustic oscillation (BAO) and even exceeding their future capability at this redshift. The above low-$\mathit{ z}$ measurement is useful for probing dark energy that dominates at the late Universe. For a flat dark energy equation of state model (flat wCDM), the massE alone constrains w to an error that is only a factor of 2.2, 1.7, and 1.3 times larger than those by BAO, SN Ia, and cosmic microwave background (CMB), respectively.

5. Abstract

Type Ia supernovae (SNe Ia) are more precise standardizable candles when measured in the near-infrared (NIR) than in the optical. With this motivation, from 2012 to 2017 we embarked on the RAISIN program with the Hubble Space Telescope (HST) to obtain rest-frame NIR light curves for a cosmologically distant sample of 37 SNe Ia (0.2 ≲z≲ 0.6) discovered by Pan-STARRS and the Dark Energy Survey. By comparing higher-zHST data with 42 SNe Ia atz< 0.1 observed in the NIR by the Carnegie Supernova Project, we construct a Hubble diagram from NIR observations (with only time of maximum light and some selection cuts from optical photometry) to pursue a unique avenue to constrain the dark energy equation-of-state parameter,w. We analyze the dependence of the full set of Hubble residuals on the SN Ia host galaxy mass and find Hubble residual steps of size ∼0.06-0.1 mag with 1.5σ−2.5σsignificance depending on the method and step location used. Combining our NIR sample with cosmic microwave background constraints, we find 1 +w= −0.17 ± 0.12 (statistical + systematic errors). The largest systematic errors are the redshift-dependent SN selection biases and the properties of the NIR mass step. We also use these data to measureH0=more »