AstroPhot
A fast, flexible, full featured, and differentiable astronomical image 2D forward modelling tool for precise parallel parametric multi-wavelength/epoch photometry
Install / Use
/learn @Autostronomy/AstroPhotREADME
AstroPhot is a fast, flexible, and principled astronomical image modelling tool for precise parallel multi-wavelength/epoch photometry. It is a python based package that uses PyTorch or JAX to quickly and efficiently perform analysis tasks. Written by Connor Stone for tasks such as LSB imaging, handling crowded fields, multi-band photometry, and analyzing massive data from future telescopes. AstroPhot is flexible and fast for any parametric astronomical image modelling task. While it uses PyTorch and/or JAX (originally developed for Machine Learning) it is NOT a machine learning based tool. In fact AstroPhot very rigidly sticks to Gaussian/Poisson likelihood modelling (with extensions for priors if desired).
AstroPhot is now a caskade ecosystem project, meaning its parameters have an incredible amount of flexibility. Check out the documentation for more details!
Installation
AstroPhot can be installed with pip:
pip install astrophot
If PyTorch gives you any trouble on your system, just follow the instructions on the pytorch website to install a version for your system.
Also note that AstroPhot is only available for python3.
See the documentation for more details.
Documentation
You can find the documentation at the ReadTheDocs site connected with the AstroPhot project which covers many of the main use cases for AstroPhot. There is tons of useful information in there, hopefully you can mix and match tutorials to get to just about any parametric image modelling task quickly! Feel free to contact the author, Connor Stone, for any questions not answered by the documentation or tutorials.
Credit / Citation
If you use AstroPhot in your research, please follow the citation instructions here.
Thanks to our contributors!
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