Hintze Centre for Astrophysical Surveys

Abstract:

<jats:p>This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inference in astrophysics and cosmology. The pipeline includes software for implementing various neural architectures, training schema, priors, and density estimators in a manner easily adaptable to any research workflow. It includes comprehensive validation metrics to assess posterior estimate coverage, enhancing the reliability of inferred results. Additionally, the pipeline is easily parallelizable, designed for efficient exploration of modeling hyperparameters. To demonstrate its capabilities, we present real applications across a range of astrophysics and cosmology problems, such as: estimating galaxy cluster masses from X-ray photometry; inferring cosmology from matter power spectra and halo point clouds; characterising progenitors in gravitational wave signals; capturing physical dust parameters from galaxy colors and luminosities; and establishing properties of semi-analytic models of galaxy formation. We also include exhaustive benchmarking and comparisons of all implemented methods as well as discussions about the challenges and pitfalls of ML inference in astronomical sciences. All code and examples are made publicly available at https://github.com/maho3/ltu-ili.</jats:p>

Abstract:

We present SN 2023zaw—a subluminous (M r = −16.7 mag) and rapidly evolving supernova (t 1/2,r = 4.9 days), with the lowest nickel mass (≈0.002 M ⊙) measured among all stripped-envelope supernovae discovered to date. The photospheric spectra are dominated by broad He i and Ca near-infrared emission lines with velocities of ∼10,000−12,000 km s−1. The late-time spectra show prominent narrow He i emission lines at ∼1000 km s−1, indicative of interaction with He-rich circumstellar material. SN 2023zaw is located in the spiral arm of a star-forming galaxy. We perform radiation-hydrodynamical and analytical modeling of the lightcurve by fitting with a combination of shock-cooling emission and nickel decay. The progenitor has a best-fit envelope mass of ≈0.2 M ☉ and an envelope radius of ≈50 R ⊙. The extremely low nickel mass and low ejecta mass (≈0.5 M ⊙) suggest an ultrastripped SN, which originates from a mass-losing low-mass He-star (zero-age main-sequence mass < 10 M ⊙) in a close binary system. This is a channel to form double neutron star systems, whose merger is detectable with LIGO. SN 2023zaw underscores the existence of a previously undiscovered population of extremely low nickel mass (<0.005 M ☉) stripped-envelope supernovae, which can be explored with deep and high-cadence transient surveys.