Binning is Sinning: Redemption for Hubble Diagram using Photometrically Classified Type Ia Supernovae
arXiv:2306.05819 · doi:10.3847/2041-8213/ace34d
Abstract
Bayesian Estimation Applied to Multiple Species (BEAMS) is implemented in the BEAMS with Bias Corrections (BBC) framework to produce a redshift-binned Hubble diagram (HD) for Type Ia supernovae (SNe Ia). BBC corrects for selection effects and non-SNIa contamination, and systematic uncertainties are described by a covariance matrix with dimension matching the number of BBC redshift bins. For spectroscopically confirmed SN Ia samples, a recent "Binning is Sinning" article (BHS21, arxiv:2012.05900) showed that an unbinned HD and covariance matrix reduces the systematic uncertainty by a factor of ~1.5 compared to the binned approach. Here we extend their analysis to obtain an unbinned HD for a photometrically identified sample processed with BBC. To test this new method, we simulate and analyze 50 samples corresponding to the Dark Energy Survey (DES) witha low-redshift anchor; the simulation includes SNe Ia, and contaminants from core-collapse SNe and peculiar SNe Ia. The analysis includes systematic uncertainties for calibration, and measures the dark energy equation of state parameter (w). Compared to a redshift-binned HD, the unbinned HD with nearly 2000 events results in a smaller systematic uncertainty, in qualitative agreement with BHS21, and averaging results among the 50 samples we find no evidence for a w-bias. To reduce computation time for fitting an unbinned HD with large samples, we propose an HD-rebinning method that defines the HD in bins of redshift, color, and stretch; the rebinned HD results in similar uncertainty as the unbinned case, and shows no evidence for a w-bias.
References in corpus (30)
- Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant
- Measurements of Omega and Lambda from 42 High-Redshift Supernovae
- The Complete Light-curve Sample of Spectroscopically Confirmed Type Ia Supernovae from Pan-STARRS1 and Cosmological Constraints from The Combined Pantheon Sample
- Improved cosmological constraints from a joint analysis of the SDSS-II and SNLS supernova samples
- The Pantheon+ Analysis: Cosmological Constraints
- Supernova Constraints and Systematic Uncertainties from the First 3 Years of the Supernova Legacy Survey
- The Supernova Legacy Survey 3-year sample: Type Ia Supernovae photometric distances and cosmological constraints
- CfA3: 185 Type Ia Supernova Light Curves from the CfA
- SNANA: A Public Software Package for Supernova Analysis
- First Cosmology Results using Type Ia Supernovae from the Dark Energy Survey: Constraints on Cosmological Parameters
- Correcting Type Ia Supernova Distances for Selection Biases and Contamination in Photometrically Identified Samples
- CfA4: Light Curves for 94 Type Ia Supernovae
- Photometric Supernova Classification With Machine Learning
- SuperNNova: an open-source framework for Bayesian, Neural Network based supernova classification
- The Pantheon+ Analysis: SuperCal-Fragilistic Cross Calibration, Retrained SALT2 Light Curve Model, and Calibration Systematic Uncertainty
- The Foundation Supernova Survey: Motivation, Design, Implementation, and First Data Release
- Measuring Dark Energy Properties with Photometrically Classified Pan-STARRS Supernovae. II. Cosmological Parameters
- Photometric SN Ia Candidates from the Three-Year SDSS-II SN Survey Data
- Models and Simulations for the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC)
- A More General Model for the Intrinsic Scatter in Type Ia Supernova Distance Moduli
- The Foundation Supernova Survey: Measuring Cosmological Parameters with Supernovae from a Single Telescope
- First Cosmology Results using Type Ia Supernova from the Dark Energy Survey: Simulations to Correct Supernova Distance Biases
- Bayesian Estimation Applied to Multiple Species: Towards cosmology with a million supernovae
- Spectrophotometric templates for core collapse supernovae and their application in simulations of time-domain surveys
- Photometric Supernova Cosmology with BEAMS and SDSS-II
- The Dark Energy Survey Supernova Program: Modelling selection efficiency and observed core collapse supernova contamination
- Binning is Sinning (Supernova Version): The Impact of Self-Calibration in Cosmological Analyses with Type Ia Supernovae
- Using Host Galaxy Photometric Redshifts to Improve Cosmological Constraints with Type Ia Supernova in the LSST Era
- The Dark Energy Survey Supernova Program: Cosmological biases from supernova photometric classification
- Reducing Zero-point Systematics in Dark Energy Supernova Experiments