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.

翻译：本文介绍了“学习宇宙隐式似然推理”（LtU-ILI）流程，这是一个用于在天体物理学和宇宙学中进行快速、用户友好且前沿的机器学习推理的代码库。该流程包含的软件能够以易于适应任何研究工作流的方式，实现各种神经架构、训练方案、先验分布和密度估计器。它包含全面的验证指标，用于评估后验估计的覆盖性，从而提升推断结果的可靠性。此外，该流程易于并行化，并专为高效探索建模超参数而设计。为展示其能力，我们呈现了其在一系列天体物理学和宇宙学问题中的实际应用，例如：通过X射线光度测量估算星系团质量；从物质功率谱和晕点云推断宇宙学参数；表征引力波信号中的前身天体；从星系颜色和光度中提取物理尘埃参数；以及确定星系形成的半解析模型的性质。我们还对所有已实现的方法进行了详尽的基准测试和比较，并讨论了在天文学科学中进行机器学习推理所面临的挑战与陷阱。所有代码和示例均已公开，可在 https://github.com/maho3/ltu-ili 获取。