Cosmology with Photometric Surveys of Type Ia Supernovae

Abstract

We discuss the extent to which photometric measurements alone can be used to identify Type Ia supernovae (SNIa) and to determine redshift and other parameters of interest for cosmological studies. We fit the light curve data of the type expected from a survey such as the one planned with Large Synoptic Survey Telescope (LSST) and also to remove the contamination from the core-collapse supernovae to SNIa samples. We generate 1000 SNIa mock flux data for each of the LSST filters based on existing design parameters, then use a Markov Chain Monte-Carlo (MCMC) analysis to fit for the redshift, apparent magnitude, stretch factor and the phase of the SNIa. We find that the model fitting works adequately well when the true SNe redshift is below 0.5, while at $z<0.2$ the accuracy of the photometric data is almost comparable with spectroscopic measurements of the same sample. We discuss the contamination of Type Ib/c (SNIb/c) and Type II supernova (SNII) on the SNIa data set. We find it is easy to distinguish the SNII through the large ${\chi }^2$ mismatch when fitting to photometric data with Ia light curves. This is not the case for SNIb/c. We implement a statistical method based on the Bayesian estimation in order to statistically reduce the contamination from SNIb/c for cosmological parameter measurements from the whole SNe sample. The proposed statistical method also evaluate the fraction of the SNIa in the total SNe data set, which provides a valuable guide to establish the degree of contamination.

9 pages, 10 figures, published in ApJ

References in corpus (20)

• Five-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Cosmological Interpretation
• Improved Cosmological Constraints from New, Old and Combined Supernova Datasets
• Bayes in the sky: Bayesian inference and model selection in cosmology
• Observational Constraints on the Nature of the Dark Energy: First Cosmological Results from the ESSENCE Supernova Survey
• Improved Dark Energy Constraints from ~100 New CfA Supernova Type Ia Light Curves
• Improved Distances to Type Ia Supernovae with Multicolor Light Curve Shapes: MLCS2k2
• The death of massive stars - I. Observational constraints on the progenitors of type II-P supernovae
• First-year Sloan Digital Sky Survey-II (SDSS-II) Supernova Results: Hubble Diagram and Cosmological Parameters
• The different progenitors of type Ib, Ic SNe, and of GRB
• Constraining supernova models using the hot gas in clusters of galaxies
• Supernovae in the Subaru Deep Field: An Initial Sample, and Type Ia Rate, out to Redshift 1.6
• Bayesian Estimation Applied to Multiple Species: Towards cosmology with a million supernovae
• Analyze This! A Cosmological Constraint Package for CMBEASY
• Type Ia and II supernovae contributions to the metal enrichment in intra-cluster medium observed with Suzaku
• Bayesian Single-Epoch Photometric Classification of Supernovae
• Beyond Two Dark Energy Parameters
• Cosmic Supernova Rates and the Hubble Sequence
• Two Component Model of Dark Energy
• Type Ia supernova science 2010-2020
• Type Ia supernovae as speed sensors at intermediate redshifts

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• Cosmology with Photometrically-Classified Type Ia Supernovae from the SDSS-II Supernova Survey
• Spectroscopic Needs for Imaging Dark Energy Experiments: Photometric Redshift Training and Calibration
• Euclid: Forecast constraints on the cosmic distance duality relation with complementary external probes
• Kernel PCA for type Ia supernovae photometric classification
• Spectrophotometric templates for core collapse supernovae and their application in simulations of time-domain surveys
• A simple and robust method for automated photometric classification of supernovae using neural networks
• Constraints on the distance duality relation with standard sirens
• Photometric Estimates of Redshifts and Distance Moduli for Type Ia Supernovae
• Machine Learning Classification of SDSS Transient Survey Images
• Photometric Supernova Cosmology with BEAMS and SDSS-II
• Semi-supervised Learning for Photometric Supernova Classification
• Defining Photometric Peculiar Type Ia Supernovae
• Euclid: Forecast constraints on consistency tests of the $\Lambda$CDM model
• The zenithal 4-m International Liquid Mirror Telescope: a unique facility for supernova studies
• Characterizing the contaminating distance distribution for Bayesian supernova cosmology
• BEAMS: separating the wheat from the chaff in supernova analysis
• Euclid: Testing the Copernican principle with next-generation surveys
• Fuzzy Supernova Templates II: Parameter Estimation
• Optical Cross Correlation Filters: An Economical Approach for Identifying SNe Ia and Estimating their Redshifts