LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics
  and Cosmology
Matthew Ho Deaglan J. Bartlett Nicolas Chartier Carolina Cuesta-Lazaro Simon Ding Axel Lapel Pablo Lemos Christopher C. Lovell T. Lucas Makinen Chirag Modi Viraj Pandya Shivam Pandey Lucia A. Perez Benjamin Wandelt Greg L. Bryan
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.