SICRET: Supernova Ia Cosmology with truncated marginal neural Ratio EsTimation

Abstract

Type Ia supernovae (SNae Ia), standardisable candles that allow tracing the expansion history of the Universe, are instrumental in constraining cosmological parameters, particularly dark energy. State-of-the-art likelihood-based analyses scale poorly to future large datasets, are limited to simplified probabilistic descriptions, and must explicitly sample a high-dimensional latent posterior to infer the few parameters of interest, which makes them inefficient. Marginal likelihood-free inference, on the other hand, is based on forward simulations of data, and thus can fully account for complicated redshift uncertainties, contamination from non-SN Ia sources, selection effects, and a realistic instrumental model. All latent parameters, including instrumental and survey-related ones, per-object and population-level properties, are implicitly marginalised, while the cosmological parameters of interest are inferred directly. As a proof of concept, we apply truncated marginal neural ratio estimation (TMNRE), a form of marginal likelihood-free inference, to BAHAMAS, a Bayesian hierarchical model for SALT parameters. We verify that TMNRE produces unbiased and precise posteriors for cosmological parameters from up to 100 000 SNae Ia. With minimal additional effort, we train a network to infer simultaneously the O(100 000) latent parameters of the supernovae (e.g. absolute brightnesses). In addition, we describe and apply a procedure that utilises local amortisation of the inference to convert the approximate Bayesian posteriors into frequentist confidence regions with exact coverage. Finally, we discuss the planned improvements to the model that are enabled by using a likelihood-free inference framework, like selection effects and non-Ia contamination.

17 pages, 12 figures; This article has been accepted for publication in MNRAS. (c): 2022 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved

References in corpus (19)

• In the Realm of the Hubble tension $-$ a Review of Solutions
• The Pantheon+ Analysis: Cosmological Constraints
• The Pantheon+ Analysis: The Full Dataset and Light-Curve Release
• Supernova Constraints and Systematic Uncertainties from the First 3 Years of the Supernova Legacy Survey
• CfA3: 185 Type Ia Supernova Light Curves from the CfA
• Correcting Type Ia Supernova Distances for Selection Biases and Contamination in Photometrically Identified Samples
• The Carnegie Supernova Project I: Third Photometry Data Release of Low-Redshift Type Ia Supernovae and Other White Dwarf Explosions
• SALT3: An Improved Type Ia Supernova Model for Measuring Cosmic Distances
• Type Ia Supernova Light Curve Inference: Hierarchical Bayesian Analysis in the Near Infrared
• Forward Global Photometric Calibration of the Dark Energy Survey
• Improved Treatment of Host-Galaxy Correlations in Cosmological Analyses With Type Ia Supernovae
• Bayesian Estimation Applied to Multiple Species: Towards cosmology with a million supernovae
• A Hierarchical Bayesian SED Model for Type Ia Supernovae in the Optical to Near-Infrared
• Cosmological Results from the RAISIN Survey: Using Type Ia Supernovae in the Near Infrared as a Novel Path to Measure the Dark Energy Equation of State
• A Revised SALT2 Surface for Fitting Type Ia Supernova Light Curves
• The need for accurate redshifts in supernova cosmology
• Peculiar velocities in the local Universe: comparison of different models and the implications for $H_0$ and dark matter
• Further evidence for significant luminosity evolution in supernova cosmology
• Specific Effect of Peculiar Velocities on Dark-Energy Constraints from Type Ia Supernovae

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