Metadata-Version: 1.1
Name: neurosynchro
Version: 0.1.2
Summary: Use neural networks to approximate polarized synchrotron radiative transfer coefficients
Home-page: https://github.com/pkgw/neurosynchro/
Author: Peter Williams
Author-email: peter@newton.cx
License: MIT
Description: *Neurosynchro* is a small Python package for creating and using neural
        networks to quickly approximate the coefficients needed for fully-polarized
        synchrotron radiative transfer. It builds on the `Keras <https://keras.io/>`_
        deep learning library. Documentation may be found `on ReadTheDocs
        <https://neurosynchro.readthedocs.io/en/stable/>`_.
        
        Say that you have a code — such as `Rimphony
        <https://github.com/pkgw/rimphony/>`_ or `Symphony
        <https://github.com/AFD-Illinois/symphony>`_ — that calculates synchrotron
        radiative transfer coefficients as a function of some input model parameters
        (electron temperature, particle energy index, etc.). These calculations are
        often accurate but slow. With *neurosynchro*, you can train a neural network
        that will quickly approximate these calculations with good accuracy. The
        achievable level of accuracy will depend on the particulars of your target
        distribution function, range of input parameters, and so on.
        
        This code is specific to synchrotron radiation because it makes certain
        assumptions about how the coefficients scale with input parameters such as the
        observing frequency.
Keywords: neural-networks radiative-transfer science
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering :: Astronomy
