Correcting Type Ia Supernova Distances for Selection Biases and Contamination in Photometrically Identified Samples

By R. Kessler, D. Scolnic. 2016

200 citations recorded by the source corpus.

We present a new technique to create a bin-averaged Hubble diagram (HD) from photometrically identified SN Ia data. The resulting HD is corrected for selection biases and contamination from core-collapse (CC) SNe, and can be used to infer cosmological parameters. This method, called “BEAMS with Bias Corrections” (BBC), includes two fitting stages. The first BBC fitting stage uses a posterior distribution that includes multiple SN likelihoods, a Monte Carlo simulation to bias-correct the fitted SALT-II parameters, and CC probabilities determined from a machine-learning technique. The BBC fit determines (1) a bin-averaged HD (average distance versus redshift), and (2) the nuisance parameters α and β, which multiply the stretch and color (respectively) to standardize the SN brightness. In the second stage, the bin-averaged HD is fit to a cosmological model where priors can be imposed. We perform high-precision tests of the BBC method by simulating large (150,000 event) data samples corresponding to the Dark Energy Survey Supernova Program. Our tests include three models of intrinsic scatter, each with two different CC rates. In the BBC fit, the SALT-II nuisance parameters α and β are recovered to within 1% of their true values. In the cosmology fit, we determine the dark energy equation of state parameter w using a fixed value of as a prior: averaging over all six tests based on 6 × 150,000 = 900,000 SNe, there is a small w-bias of . Finally, the BBC fitting code is publicly available in the SNANA package.

Subfields: Astronomy and Astrophysics, Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/3152faf8e0d9cd6c6b75c68a40c25471faff1cd7

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Citations in the corpus

The Complete Light-curve Sample of Spectroscopically Confirmed SNe Ia from Pan-STARRS1 and Cosmological Constraints from the Combined Pantheon Sample (2017, methodology): “As discussed later in this section, we use the BEAMS with Bias Corrections (BBC) method (Kessler & Scolnic 2017) to recover binned Hubble residuals with redshift, with respect to a reference cosmology.”
Measuring Dark Energy Properties with Photometrically Classified Pan-STARRS Supernovae. II. Cosmological Parameters (2017, methodology): “These include two light curve-based methods, Nearest Neighbor (NN; Sako et al. 2014; Kessler & Scolnic 2017) and Fitprob.”
On the Hubble Constant Tension in the SNe Ia Pantheon Sample (2021, background): “…tests include probing the relations between luminosity and the properties of the host galaxies of the SNe (Kelly et al. 2010; Lampeitl et al. 2010; Sullivan et al. 2010) and analysis of the light-curve fit parameters of SNe and how these parameters relate to luminosity (Kessler & Scolnic 2017).”
Type Ia Supernova Distances at Redshift >1.5 from the Hubble Space Telescope Multi-cycle Treasury Programs: The Early Expansion Rate (2017, methodology): “The Pantheon analysis also incorporates the BBC methodology of Kessler & Scolnic (2017) (see also Scolnic & Kessler 2016), which corrects for distance biases dependent on the light-curve properties of the SNe and the surveys from which they are selected.”
The Pantheon+ Analysis: Cosmological Constraints (2022, background): “…i i i i i i i z i i , 2 , meas, 2 floor 2 , lens, 2 , 2 vpec, 2 ( ) ( ) ( ) s s s s s s = + + + + m where σmeas is the measurement uncertainty of SALT2 lightcurve fit parameters and their associated covariances (see Equation (3) of Kessler & Scolnic 2017) resulting from photometric uncertainties.”