Measuring the Properties of Dark Energy with Photometrically Classified Pan-STARRS Supernovae. I. Systematic Uncertainty from Core-collapse Supernova Contamination

Abstract

The Pan-STARRS (PS1) Medium Deep Survey discovered over 5,000 likely supernovae (SNe) but obtained spectral classifications for just 10% of its SN candidates. We measured spectroscopic host galaxy redshifts for 3,147 of these likely SNe and estimate that ∼1,000 are Type Ia SNe (SNe Ia) with light-curve quality sufficient for a cosmological analysis. We use these data with simulations to determine the impact of core-collapse SN (CC SN) contamination on measurements of the dark energy equation of state parameter, w. Using the method of Bayesian Estimation Applied to Multiple Species (BEAMS), distances to SNe Ia and the contaminating CC SN distribution are simultaneously determined. We test light-curve based SN classification priors for BEAMS as well as a new classification method that relies upon host galaxy spectra and the association of SN type with host type. By testing several SN classification methods and CC SN parameterizations on large SN simulations, we estimate that CC SN contamination gives a systematic error on w (σ CC w ) of 0.014, 29% of the statistical uncertainty. Our best method gives σ CC w = 0.004, just 8% of the statistical uncertainty, but could be affected by incomplete knowledge of the CC SN distribution. This method determines the SALT2 color and shape coefficients, α and β, with ∼3% bias. However, we find that some variants require α and β to be fixed to known values for BEAMS to yield accurate measurements of w. Finally, the inferred abundance of bright CC SNe in our sample is greater than expected based on measured CC SN rates and luminosity functions.

ZTF Early Observations of Type Ia Supernovae. II. First Light, the Initial Rise, and Time to Reach Maximum Brightness

“…These smaller, more focused, missions can provide exquisite observations of a select handful of SNe that can be used to drive the priors in our inference. While there have been thousands of SNe Ia studied to date (e.g., Jones et al 2017), more examples are indeed still needed: there are only four normal, z<0.03 SNe Ia in our sample, and these four are nearly as valuable as the entire remainder of the sample for establishing the diversity of SNe Ia. As low-z, high-cadence surveys improve our understanding of these priors, we can combine that knowledge with the hitherto unimaginable statistical samples from LSST (∼millions of SNe), to better understand the early evolution and rise time distribution of Type Ia SNe.…”

First cosmology results using Type Ia supernova from the Dark Energy Survey: simulations to correct supernova distance biases

“…Examples include CC simulations to model contamination in photometrically identified SN Ia samples (Kessler et al 2010a;Rodney et al 2012;Kessler & Scolnic 2017;Jones et al 2017), and simulating Kilonovae (Barnes & Kasen 2013) to model the search efficiency (Soares-Santos et al 2016;Doctor et al 2017), and to predict discovery rates (Scolnic et al 2018a).…”

Spectrophotometric templates for core-collapse supernovae and their application in simulations of time-domain surveys

“…In current SN Ia photometric cosmological analyses, the effect of contamination is assessed using simulation techniques (Kunz et al 2007;Hlozek et al 2012;Kessler & Scolnic 2017), which are sensitive to how well the contamination can be modelled, rather than to the classification techniques used. The recent analysis of the photometric SN Ia sample from the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS 1; Jones et al 2017Jones et al , 2018 has shown that simulations of core collapse SNe using currently available template libraries and luminosity functions significantly underestimate the apparent core collapse SN contamination in the data, and a significant tuning of the underlying properties of the core collapse SN population is required.…”