Bayesian constraints on dark matter halo properties using gravitationally-lensed supernovae
N. V. Karpenka, M. C. March, F. Feroz, M. P. Hobson

TL;DR
This paper develops a hierarchical Bayesian method using nested sampling to analyze gravitational lensing effects of dark matter haloes on supernovae, validating it with simulations and applying it to real data to constrain halo properties.
Contribution
Introduces a novel Bayesian analysis framework with nested sampling for dark matter halo characterization via supernova lensing, enabling robust model comparison and parameter estimation.
Findings
Simulations show the method can accurately recover halo parameters with larger supernova samples.
Real data analysis yields marginal detection of lensing signal, with constraints influenced by the velocity dispersion-luminosity scaling law.
No significant lensing detection for the NFW halo model.
Abstract
A hierarchical Bayesian method is applied to the analysis of Type-Ia supernovae (SNIa) observations to constrain the properties of the dark matter haloes of galaxies along the SNIa lines-of-sight via their gravitational lensing effect. The full joint posterior distribution of the dark matter halo parameters is explored using the nested sampling algorithm {\sc MultiNest}, which also efficiently calculates the Bayesian evidence, thereby facilitating robust model comparison. We first demonstrate the capabilities of the method by applying it to realistic simulated SNIa data, based on the real 3-year data release from the Supernova Legacy Survey (SNLS3). Assuming typical values for the halo parameters in our simulations, we find that a catalogue analogous to the existing SNLS3 data set is incapable of detecting the lensing signal, but a catalogue containing approximately three times as many…
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