Weak-lensing shear measurement with machine learning
Teaching artificial neural networks about feature noise
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M. Tewes, T. Kuntzer, R. Nakajima, F. Courbin, H. Hildebrandt, and T. Schrabback

	
The python code accompanying our paper is split into two packages.

    - "Tenbilac" is a simple artificial neural network library implementing
    the peculiar distinction between training cases and realizations, in python
    and numpy.

    - "MomentsML" is a toolbox for experimenting with shear and shape estimators,
    build around GalSim and Astropy. It includes a simple wrapper to process
    GREAT3 data, and an interface to tenbilac.

These packages provide a demonstration implementation of the algorithms described in the paper. 
They are oriented towards experimentation rather than being optimized for integration into a shear analysis pipeline.
Instructions on how to install and use the packages are provided in the included README.md files.
In particular, to reproduce the results and figures from the paper, see the section "Getting started" in the README.md inside of the momentsml directory.

Potential updates and extensions to these codes will be described at
https://astro.uni-bonn.de/~mtewes/ml-shear-meas/
