Bayesian Estimation Applied to Multiple Species: Towards cosmology with a million supernovae

Abstract

Observed data is often contaminated by undiscovered interlopers, leading to biased parameter estimation. Here we present BEAMS (Bayesian Estimation Applied to Multiple Species) which significantly improves on the standard maximum likelihood approach in the case where the probability for each data point being `pure' is known. We discuss the application of BEAMS to future Type Ia supernovae (SNIa) surveys, such as LSST, which are projected to deliver over a million supernovae lightcurves without spectra. The multi-band lightcurves for each candidate will provide a probability of being Ia (pure) but the full sample will be significantly contaminated with other types of supernovae and transients. Given a sample of N supernovae with mean probability, P, of being Ia, BEAMS delivers parameter constraints equal to NP spectroscopically-confirmed SNIa. In addition BEAMS can be simultaneously used to tease apart different families of data and to recover properties of the underlying distributions of those families (e.g. the Type Ibc and II distributions). Hence BEAMS provides a unified classification and parameter estimation methodology which may be useful in a diverse range of problems such as photometric redshift estimation or, indeed, any parameter estimation problem where contamination is an issue.

12 pages, 5 figures. Minor revisions to match published version

References in corpus (23)

• Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant
• Measurements of Omega and Lambda from 42 High-Redshift Supernovae
• Type Ia Supernova Discoveries at z>1 From the Hubble Space Telescope: Evidence for Past Deceleration and Constraints on Dark Energy Evolution
• The Supernova Legacy Survey: Measurement of Omega_M, Omega_Lambda and w from the First Year Data Set
• Cosmological Results from High-z Supernovae
• BVRI Light Curves for 22 Type Ia Supernovae
• The Absolute Luminosities of the Calan/Tololo Type Ia Supernovae
• UBVRI Light Curves of 44 Type Ia Supernovae
• The Carnegie Supernova Project: The Low-Redshift Survey
• The Hubble Diagram of the Calan/Tololo Type Ia Supernovae and the value of Ho
• Correlated Fluctuations in Luminosity Distance and the (Surprising) Importance of Peculiar Motion in Supernova Surveys
• A Comparative Study of the Absolute-Magnitude Distributions of Supernovae
• Dipole of the luminosity distance: a direct measure of H(z)
• Photometric selection of high-redshift type Ia supernovae
• Not Color Blind: Using Multiband Photometry to Classify Supernovae
• Spectroscopy of High-Redshift Supernovae from the ESSENCE Project: The First Two Years
• Identification of Type Ia Supernovae at Redshift 1.3 and Beyond with the Advanced Camera for Surveys on HST
• Large-Scale Bulk Motions Complicate the Hubble Diagram
• Redshift Accuracy Requirements for Future Supernova and Number Count Surveys
• Redshift-Independent Distances to Type Ia Supernovae
• Photometric Identification of Type Ia Supernovae at Moderate Redshift
• Testing Dark Energy with the Advanced Liquid-Mirror Probe of Asteroids, Cosmology and Astrophysics
• The Deepest Supernova Search is Realized in the Hubble Ultra Deep Field Survey

Cited by in corpus (58)

• A 3% Solution: Determination of the Hubble Constant with the Hubble Space Telescope and Wide Field Camera 3
• Determining the Type, Redshift, and Age of a Supernova Spectrum
• Dark energy two decades after: Observables, probes, consistency tests
• Correcting Type Ia Supernova Distances for Selection Biases and Contamination in Photometrically Identified Samples
• Limits on stellar-mass compact objects as dark matter from gravitational lensing of type Ia supernovae
• The Sloan Digital Sky Survey-II Supernova Survey: Search Algorithm and Follow-up Observations
• Results from the Supernova Photometric Classification Challenge
• 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
• Avocado: Photometric Classification of Astronomical Transients with Gaussian Process Augmentation
• UNITY: Confronting Supernova Cosmology's Statistical and Systematic Uncertainties in a Unified Bayesian Framework
• The Foundation Supernova Survey: Measuring Cosmological Parameters with Supernovae from a Single Telescope
• Kernel PCA for type Ia supernovae photometric classification
• BAHAMAS: new SNIa analysis reveals inconsistencies with standard cosmology
• 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
• Measuring the Properties of Dark Energy with Photometrically Classified Pan-STARRS Supernovae. I. Systematic Uncertainty from Core-Collapse Supernova Contamination
• Machine Learning Classification of SDSS Transient Survey Images
• Semi-supervised Learning for Photometric Supernova Classification
• Probing the stellar initial mass function with high-$z$ supernovae
• STACCATO: A Novel Solution to Supernova Photometric Classification with Biased Training Sets
• The Dark Energy Survey 5-year photometrically identified Type Ia Supernovae
• Cosmic Core-Collapse Supernovae from Upcoming Sky Surveys
• Evidence for Cosmic Acceleration is Robust to Observed Correlations Between Type Ia Supernova Luminosity and Stellar Age
• The Dark Energy Survey: Cosmology Results With ~1500 New High-redshift Type Ia Supernovae Using The Full 5-year Dataset
• SICRET: Supernova Ia Cosmology with truncated marginal neural Ratio EsTimation
• Effects of type Ia supernovae absolute magnitude priors on the Hubble constant value
• Steve: A hierarchical Bayesian model for Supernova Cosmology
• Statistical Classification Techniques for Photometric Supernova Typing
• Using Host Galaxy Photometric Redshifts to Improve Cosmological Constraints with Type Ia Supernova in the LSST Era
• Cosmology with Photometric Surveys of Type Ia Supernovae
• The Dark Energy Survey Supernova Program: Cosmological biases from supernova photometric classification
• Extensive search for bias in SNIa data
• zBEAMS: A unified solution for supernova cosmology with redshift uncertainties
• The Dark Energy Survey Supernova Program: Cosmological Analysis and Systematic Uncertainties
• The zenithal 4-m International Liquid Mirror Telescope: a unique facility for supernova studies
• Bayesian astrostatistics: a backward look to the future
• Extending BEAMS to incorporate correlated systematic uncertainties
• Parameter Estimation with BEAMS in the presence of biases and correlations
• Assessment of the Systematic Uncertainties in the Cosmological Analysis of the SDSS Supernovae Photometric Sample
• Binning is Sinning: Redemption for Hubble Diagram using Photometrically Classified Type Ia Supernovae
• Characterizing the contaminating distance distribution for Bayesian supernova cosmology
• On the cosmological performance of photometrically classified supernovae with machine learning
• Bayesian Anomaly Detection and Classification
• SIDE-real: Supernova Ia Dust Extinction with truncated marginal neural ratio estimation applied to real data
• Impact of Rubin Observatory cadence choices on supernovae photometric classification
• Understanding Type Ia Supernova Distance Biases by Simulating Spectral Variations
• BEAMS: separating the wheat from the chaff in supernova analysis
• Optical Cross Correlation Filters: An Economical Approach for Identifying SNe Ia and Estimating their Redshifts
• Hawai'i Supernova Flows: A Peculiar Velocity Survey Using Over a Thousand Supernovae in the Near-Infrared
• BayeSN and SALT: A Comparison of Dust Inference Across SN Ia Light-curve Models with DES5YR
• TABASCAL: Removing multi-satellite interference from radio interferometry observations
• ZTF SN Ia DR2: Improved SN Ia colors through expanded dimensionality with SALT3+
• Considerations for optimizing photometric classification of supernovae from the Rubin Observatory
• Searching for bias and correlations in a Bayesian way
• Redshift Evolution of the HII Galaxy $L$-$\sigma$ Relation: Gaussian Process Analysis and Cosmological Implications
• FlowSN: Neural Simulation-Based Inference under Realistic Selection Effects applied to Supernova Cosmology
• Type Ia supernova Hubble diagrams with host galaxy photometric redshifts