Fourier Power Function Shapelets (FPFS) is a perturbation-based estimator for
shear responses of galaxy shape, flux and detection --- It uses the
leading-order perturbations of shear (a vector perturbation) and image noise (a
tensor perturbation) to derive the shear responses and noise responses of
measurements and detections. It is a passive shear estimator --- It does not
repeatedly distort each observed galaxy to derive the shear responses; instead,
the shear responses are derived using the analytical shear responses of a set
of basis functions (Shapelets basis and peak basis). This method can process
about 1000 galaxies in 1 cpu second, and it has been tested with simple
simulations and demonstrated to control multiplicative shear estimation bias
below 1% even in the existence of blending.
Documentation for FPFS modules can be found here
For stable (old) version, which have not been updated:
pip install fpfsOr clone the repository:
git clone https://github.com/mr-superonion/FPFS.git
cd FPFS
pip install -e . --userBefore using the code, please setup the jax environment
source fpfs_configThe following papers are ready to be cited if you find any of these papers interesting or use the pipeline. Comments are welcome.
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version 3: Li & Mandelbaum (2022) correct for detection bias from pixel level by interpreting smoothed pixel values as a projection of signal onto a set of basis functions.
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version 2: Li , Li & Massey (2022) derive the covariance matrix of FPFS measurements and corrects for noise bias to second-order. In addition, it derives the correction for selection bias.
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version 1: Li et. al (2018) build up the FPFS formalism based on Fourier_Quad and polar shapelets.
Before sending pull request, please make sure that the modified code passed the pytest and flake8 tests. Run the following commands under the root directory for the tests:
flake8
pytest -vv