Imaging is the process of transforming noisy, incomplete data into a space that humans can interpret. NIFTy is a Bayesian framework for imaging and has already successfully been applied to many fields in astrophysics. Previous design decisions held the performance and the development of methods in NIFTy back. We present a rewrite of NIFTy, coined NIFTy.re, which reworks the modeling principle, extends the inference strategies, and outsources much of the heavy lifting to JAX. The rewrite dramatically accelerates models written in NIFTy, lays the foundation for new types of inference machineries, improves maintainability, and enables interoperability between NIFTy and the JAX machine learning ecosystem.

翻译：成像是一个将含噪、不完整的数据转换到人类可解释空间的过程。NIFTy是一个用于成像的贝叶斯框架，并已成功应用于天体物理学的多个领域。先前的设计决策限制了NIFTy的性能提升与方法发展。本文提出NIFTy的重构版本，命名为NIFTy.re，其重构了建模原理，扩展了推断策略，并将大量计算密集型任务转移至JAX平台。此次重写显著加速了基于NIFTy构建的模型，为新型推断机制奠定了基础，提升了代码可维护性，并实现了NIFTy与JAX机器学习生态系统间的互操作性。