alessiospuriomancini/cosmopower
Machine Learning - accelerated Bayesian inference
This tool helps astrophysicists and cosmologists rapidly analyze cosmological data. It takes in cosmological parameters like matter density and Hubble constant, and quickly outputs predicted matter and Cosmic Microwave Background power spectra. This allows researchers to efficiently test different cosmological models against observational data to understand the universe.
No commits in the last 6 months. Available on PyPI.
Use this if you are a researcher in cosmology needing to accelerate Bayesian inference for analyzing survey data and want to replace slow Boltzmann codes with machine learning emulators.
Not ideal if your scientific inverse problem is not related to cosmology or if you require a simple, non-ML based inference pipeline without differentiable components.
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79
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32
Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
Dec 21, 2024
Commits (30d)
0
Dependencies
5
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