Dynamic Black-hole Emission Tomography with Physics-informed Neural Fields

• URL: http://arxiv.org/abs/2602.08029v1
• Date: Sun, 08 Feb 2026 16:03:25 GMT
• Title: Dynamic Black-hole Emission Tomography with Physics-informed Neural Fields
• Authors: Berthy T. Feng, Andrew A. Chael, David Bromley, Aviad Levis, William T. Freeman, Katherine L. Bouman,
• Abstract summary: We propose PI-DEF, a physics-informed approach that uses differentiable neural rendering to fit a 4D (time + 3D) emissivity field given EHT measurements.<n>Our approach jointly reconstructs the 3D velocity field with the 4D emissivity field and enforces the velocity as a soft constraint on the dynamics of the emissivity.
• Score: 25.10923166026416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the success of static black-hole imaging, the next frontier is the dynamic and 3D imaging of black holes. Recovering the dynamic 3D gas near a black hole would reveal previously-unseen parts of the universe and inform new physics models. However, only sparse radio measurements from a single viewpoint are possible, making the dynamic 3D reconstruction problem significantly ill-posed. Previously, BH-NeRF addressed the ill-posed problem by assuming Keplerian dynamics of the gas, but this assumption breaks down near the black hole, where the strong gravitational pull of the black hole and increased electromagnetic activity complicate fluid dynamics. To overcome the restrictive assumptions of BH-NeRF, we propose PI-DEF, a physics-informed approach that uses differentiable neural rendering to fit a 4D (time + 3D) emissivity field given EHT measurements. Our approach jointly reconstructs the 3D velocity field with the 4D emissivity field and enforces the velocity as a soft constraint on the dynamics of the emissivity. In experiments on simulated data, we find significantly improved reconstruction accuracy over both BH-NeRF and a physics-agnostic approach. We demonstrate how our method may be used to estimate other physics parameters of the black hole, such as its spin.

Related papers

• From Black Hole to Galaxy: Neural Operator: Framework for Accretion and Feedback Dynamics [70.27068115318681]
  We introduce a neural-based ''subgrid black hole'' that learns the small-scale local dynamics and embeds it within direct simulations.<n>Thanks to the great speedup in fine-scale evolution, our approach captures intrinsic variability in accretion-driven feedback, allowing dynamic coupling between the central black hole and galaxy-scale gas.
  arXiv  Detail & Related papers  (2025-12-01T11:47:49Z)
• Physics Informed Neural Networks for design optimisation of diamond particle detectors for charged particle fast-tracking at high luminosity hadron colliders [70.66815108184498]
  Future high-luminosity hadron colliders demand tracking detectors with extreme radiation tolerance, high spatial precision, and sub-nanosecond timing.<n>3D diamond pixel sensors offer these capabilities due to diamond's radiation hardness and high carrier mobility.<n>We model the phenomenon through a 3rd-order, 3+1D PDE derived as a quasi-stationary approximation of Maxwell's equations.
  arXiv  Detail & Related papers  (2025-09-25T13:09:28Z)
• Revealing the 3D Cosmic Web through Gravitationally Constrained Neural Fields [15.645523903662033]
  Weak gravitational lensing is the slight distortion of galaxy shapes caused primarily by the gravitational effects of dark matter in the universe.<n>We seek to invert the weak lensing signal from 2D telescope images to reconstruct a 3D map of the universe's dark matter field.<n>We propose a methodology using a gravitationally-constrained neural field to flexibly model the continuous matter distribution.
  arXiv  Detail & Related papers  (2025-04-21T17:43:21Z)
• The Ising model as a window on quantum gravity with matter [0.0]
  We argue that the Ising model CFT can be used to obtain some clear insights into 3D (quantum) gravity with matter.<n>We provide an explanation in terms of the properties of bulk matter fields interacting with the BTZ black hole.
  arXiv  Detail & Related papers  (2025-02-26T10:22:25Z)
• PBR-NeRF: Inverse Rendering with Physics-Based Neural Fields [49.6405458373509]
  We present an inverse rendering (IR) model capable of jointly estimating scene geometry, materials, and illumination.<n>Our method is easily adaptable to other inverse rendering and 3D reconstruction frameworks that require material estimation.
  arXiv  Detail & Related papers  (2024-12-12T19:00:21Z)
• PhyRecon: Physically Plausible Neural Scene Reconstruction [81.73129450090684]
  We introduce PHYRECON, the first approach to leverage both differentiable rendering and differentiable physics simulation to learn implicit surface representations. Central to this design is an efficient transformation between SDF-based implicit representations and explicit surface points. Our results also exhibit superior physical stability in physical simulators, with at least a 40% improvement across all datasets.
  arXiv  Detail & Related papers  (2024-04-25T15:06:58Z)
• Orbital Polarimetric Tomography of a Flare Near the Sagittarius A* Supermassive Black Hole [17.08371108747886]
  We show the first 3D reconstruction of an emission flare recovered from ALMA light curves observed on April 11, 2017. Our recovery shows compact, bright regions at a distance of roughly six times the event horizon. It suggests a clockwise rotation in a low-inclination orbital plane, consistent with prior studies.
  arXiv  Detail & Related papers  (2023-10-11T17:36:17Z)
• AdS/CFT Correspondence with a 3D Black Hole Simulator [0.0]
  We use a square lattice of fermions with inhomogeneous tunneling couplings to simulate rotationally symmetric 3D black holes on Dirac fields. We identify the parametric regime where the theoretically predicted 2D CFT faithfully describes the black hole entanglement entropy. With the help of the universal simulator we further demonstrate that a large family of 3D black holes exhibit the same ground state entanglement entropy behavior as the BTZ black hole.
  arXiv  Detail & Related papers  (2022-11-28T13:36:32Z)
• Gravitationally Lensed Black Hole Emission Tomography [21.663531093434127]
  We propose BH-NeRF, a novel tomography approach that leverages gravitational lensing to recover the continuous 3D emission field near a black hole. Our method captures the unknown emission field using a continuous volumetric function parameterized by a coordinate-based neural network. This work takes the first steps in showing how future measurements from the Event Horizon Telescope could be used to recover evolving 3D emission around the supermassive black hole in our Galactic center.
  arXiv  Detail & Related papers  (2022-04-07T20:09:51Z)
• {\phi}-SfT: Shape-from-Template with a Physics-Based Deformation Model [69.27632025495512]
  Shape-from-Template (SfT) methods estimate 3D surface deformations from a single monocular RGB camera. This paper proposes a new SfT approach explaining 2D observations through physical simulations.
  arXiv  Detail & Related papers  (2022-03-22T17:59:57Z)
• Simultaneous boundary shape estimation and velocity field de-noising in Magnetic Resonance Velocimetry using Physics-informed Neural Networks [70.7321040534471]
  Magnetic resonance velocimetry (MRV) is a non-invasive technique widely used in medicine and engineering to measure the velocity field of a fluid. Previous studies have required the shape of the boundary (for example, a blood vessel) to be known a priori. We present a physics-informed neural network that instead uses the noisy MRV data alone to infer the most likely boundary shape and de-noised velocity field.
  arXiv  Detail & Related papers  (2021-07-16T12:56:09Z)
• Machine-Learning Non-Conservative Dynamics for New-Physics Detection [69.45430691069974]
  Given a trajectory governed by unknown forces, our Neural New-Physics Detector (NNPhD) aims to detect new physics. We demonstrate that NNPhD successfully discovers new physics by decomposing the force field into conservative and non-conservative components. We also show how NNPhD coupled with an integrator outperforms previous methods for predicting the future of a damped double pendulum.
  arXiv  Detail & Related papers  (2021-05-31T18:00:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.