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CIGaRS is a proof of the concept of unified inference of SN Ia and host properties in an interconnected Bayesian hierarchical model. This chapter presents its initial implementation, combining the SICRET with the Prospector full spectral model of galaxies via two “interaction” channels: self-consistent dust modelling via Simple-BayeSN to transfer a physically motivated galaxy extinction onto the SN Ia colour; and a rate of SN Ia occurrence tied to the host’s stellar population via a phenomenological (parametrised) delay-time distribution (DTD) indicative of the progenitor scenario(s). Using STAR NRE and assuming observables from a photometric-only survey like the LSST (i.e. noisy estimates of SN Ia light-curve summary parameters and multiband host photometry; note, no explicit redshift measurement), we simultaneously standardise the SNæ, infer the cosmological parameters, and constrain the SN Ia delay-time distribution (DTD), thus investigating both the constituents and evolution of our Universe and the astrophysics of stellar populations.

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One with the environment: combined inference of galaxy redshift and SNæ

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  • 发表日期：

    2026年

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  • Konstantin Karchev

摘要

CIGaRS is a proof of the concept of unified inference of SN Ia and host properties in an interconnected Bayesian hierarchical model. This chapter presents its initial implementation, combining the SICRET with the Prospector full spectral model of galaxies via two “interaction” channels: self-consistent dust modelling via Simple-BayeSN to transfer a physically motivated galaxy extinction onto the SN Ia colour; and a rate of SN Ia occurrence tied to the host’s stellar population via a phenomenological (parametrised) delay-time distribution (DTD) indicative of the progenitor scenario(s). Using STAR NRE and assuming observables from a photometric-only survey like the LSST (i.e. noisy estimates of SN Ia light-curve summary parameters and multiband host photometry; note, no explicit redshift measurement), we simultaneously standardise the SNæ, infer the cosmological parameters, and constrain the SN Ia delay-time distribution (DTD), thus investigating both the constituents and evolution of our Universe and the astrophysics of stellar populations.