Re-Envisioning Numerical Information Field Theory (NIFTy.re): A Library for Gaussian Processes and Variational Inference

Abstract

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.

10 pages, 2 figures, published in Journal of Open Source Software (JOSS)

References in corpus (8)

• Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro
• A parsec-scale Galactic 3D dust map out to 1.25 kpc from the Sun
• Geometric variational inference
• Unified Radio Interferometric Calibration and Imaging with Joint Uncertainty Quantification
• Bayesian radio interferometric imaging with direction-dependent calibration
• Field dynamics inference via spectral density estimation
• The Galactic 3D large-scale dust distribution via Gaussian process regression on spherical coordinates
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• Mid-infrared extinction toward the Galactic center
• The superclouds of the local Milky Way
• Interpolation techniques for reconstructing Galactic Faraday rotation
• Non-parametric Bayesian reconstruction of Galactic magnetic fields using Information Field Theory: The inclusion of line-of-sight information in ultra-high energy cosmic ray backtracking
• JAXbind: Bind any function to JAX
• Latent-space Field Tension for Astrophysical Component Detection An application to X-ray imaging
• Bayesian Multi-wavelength Imaging of the LMC SN1987A with SRG/eROSITA
• A Bayesian Method for Air-Shower Reconstruction using Information Field Theory