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Found 6 papers, 5 papers with code
Diffusion-HMC: Parameter Inference with Diffusion Model driven Hamiltonian Monte Carlo
This work uses a single diffusion generative model to address these interlinked objectives -- as a surrogate model or emulator for cold dark matter density fields conditional on input cosmological parameters, and as a parameter inference model that solves the inverse problem of constraining the cosmological parameters of an input field.
Cosmological Field Emulation and Parameter Inference with Diffusion Models
Cosmological simulations play a crucial role in elucidating the effect of physical parameters on the statistics of fields and on constraining parameters given information on density fields.
Simulation-based Inference for Exoplanet Atmospheric Retrieval: Insights from winning the Ariel Data Challenge 2023 using Normalizing Flows
These advancements pave the way for more effective and timely analysis of exoplanet atmospheric properties, advancing our understanding of these distant worlds.
Can denoising diffusion probabilistic models generate realistic astrophysical fields?
In this work we investigate the ability of these models to generate fields in two astrophysical contexts: dark matter mass density fields from cosmological simulations and images of interstellar dust.
Equivariant Wavelets: Fast Rotation and Translation Invariant Wavelet Scattering Transforms
Wavelet scattering networks, which are convolutional neural networks (CNNs) with fixed filters and weights, are promising tools for image analysis.
On Galactic density modeling in the presence of dust extinction
This formalism demonstrates that the spatially-complex effect of extinction on the selection function of a pencil-beam or contiguous sky survey is equivalent to a low-pass filtering of the extinction-affected selection function with the smooth density field.
Astrophysics of Galaxies
