Photometric redshift via Gaussian processes with physical kernels
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README.md

Delight

Photometric redshift via Gaussian processes with physical kernels.

Read the documentation here: http://delight.readthedocs.io

Warning: this code is still in active development and is not quite ready to be blindly applied to arbitrary photometric galaxy surveys. But this day will come.

alt tag alt tag Documentation Status Latest PDF Coverage Status

Tests: pytest for unit tests, PEP8 for code style, coveralls for test coverage.

Content

./paper/: journal paper describing the method
./delight/: main code (Python/Cython)
./tests/: test suite for the main code
./notebooks/: demo notebooks using delight
./data/: some useful inputs for tests/demos
./docs/: documentation
./other/: useful mathematica notebooks, etc

Requirements

Python 3.5, cython, numpy, scipy, pytest, pylint, coveralls, matplotlib, astropy, mpi4py

Authors

Boris Leistedt (NYU)
David W. Hogg (NYU) (Flatiron)

Please cite [Leistedt and Hogg (2016)] (https://arxiv.org/abs/1612.00847) if you use this code your research. The BibTeX entry is:

@article{delight,
    author  = "Boris Leistedt and David W. Hogg",
    title   = "Data-driven, Interpretable Photometric Redshifts Trained on Heterogeneous and Unrepresentative Data",
    journal = "The Astrophysical Journal",
    volume  = "838",
    number  = "1",
    pages   = "5",
    url     = "http://stacks.iop.org/0004-637X/838/i=1/a=5",
    year    = "2017",
    eprint         = "1612.00847",
    archivePrefix  = "arXiv",
    primaryClass   = "astro-ph.CO",
    SLACcitation   = "%%CITATION = ARXIV:1612.00847;%%"
}

License

Copyright 2016-2017 the authors. The code in this repository is released under the open-source MIT License. See the file LICENSE for more details.