CUDA-based ray tracing shows black hole shadows and emission rates vary with global monopole, charge, and rotation parameters but are insensitive to the Euler-Heisenberg nonlinearity, yielding observational bounds on those three quantities.
Akiyama and al.,First M87 Event Horizon Telescope Results
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Machine learning constrains non-commutative black hole parameters and reports consistency with Sgr A* Keck observations.
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On Computational CUDA Studies of Black Hole Shadows
CUDA-based ray tracing shows black hole shadows and emission rates vary with global monopole, charge, and rotation parameters but are insensitive to the Euler-Heisenberg nonlinearity, yielding observational bounds on those three quantities.
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Constraining Black Hole Parameters in Non-Commutative Geometry using Machine Learning
Machine learning constrains non-commutative black hole parameters and reports consistency with Sgr A* Keck observations.
- On Thermodynamics of Charged Black Holes, Swampland, and Dark Matter