VizieR Online Data Catalog: Shear measurement with machine learning code (Tewes+, 2019)
Abstract
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/ (1 data file).- Publication:
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VizieR Online Data Catalog
- Pub Date:
- November 2018
- Bibcode:
- 2018yCat..36210036T
- Keywords:
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- Models;
- Gravitational lensing