# LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology

arXiv: https://arxiv.org/abs/2402.05137 · HTML: https://mlsanity.com/paper/2402.05137/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Builds on (most cited first, 1 of 1)

- [Fishnets: Information-Optimal, Scalable Aggregation for Sets and Graphs](https://mlsanity.com/paper/2310.03812/) (2023)

## Built on by (most cited first, 8 of 8)

- [SpecCLIP: Aligning and Translating Spectroscopic Measurements for Stars](https://mlsanity.com/paper/2507.01939/) (2025)
- [COmoving Computer Acceleration (COCA): N-body simulations in an emulated frame of reference](https://mlsanity.com/paper/2409.02154/) (2024)
- [Statistical Patterns in the Equations of Physics and the Emergence of a Meta-Law of Nature](https://mlsanity.com/paper/2408.11065/) (2024)
- [Guidance for Prior Change via Density Ratio Estimation](https://mlsanity.com/paper/2608.21729/) (2026)
- [Hybrid summary statistics: neural weak lensing inference beyond the power spectrum](https://mlsanity.com/paper/2407.18909/) (2024)
- [Cosmology with Persistent Homology: Parameter Inference via Machine Learning](https://mlsanity.com/paper/2412.15405/) (2024)
- [The Degeneracy Distillery](https://mlsanity.com/paper/2606.23838/) (2026)
- [Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust](https://mlsanity.com/paper/2608.12470/) (2026)

## Abstract (arXiv)

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 schemata, 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 and is 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; characterizing 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.