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Near-Field Cosmology with Gas-Poor Dwarf Galaxies

The space density of gas-poor dwarf galaxies provides an important constraint on LCDM galaxyformation models. We propose to test the efficacy of convolutional neural networks (CNN) in identifying such diffuse dwarfs within distances of ~2-30 Mpc and estimating their distances, via their distinct morphologies, for survey data comparable to LSST and WFIRST.

License

This project is Copyright (c) Henry C. Ferguson, Craig Jones, Erik Tollerud, Robel Geda and licensed under the terms of the BSD 3-Clause license. This package is based upon the Astropy package template <https://github.com/astropy/package-template>_ which is licensed under the BSD 3-clause licence. See the licenses folder for more information.

Contributing

We love contributions! dsii_dwarfs is open source, built on open source, and we'd love to have you hang out in our community.

Imposter syndrome disclaimer: We want your help. No, really.

There may be a little voice inside your head that is telling you that you're not ready to be an open source contributor; that your skills aren't nearly good enough to contribute. What could you possibly offer a project like this one?

We assure you - the little voice in your head is wrong. If you can write code at all, you can contribute code to open source. Contributing to open source projects is a fantastic way to advance one's coding skills. Writing perfect code isn't the measure of a good developer (that would disqualify all of us!); it's trying to create something, making mistakes, and learning from those mistakes. That's how we all improve, and we are happy to help others learn.

Being an open source contributor doesn't just mean writing code, either. You can help out by writing documentation, tests, or even giving feedback about the project (and yes - that includes giving feedback about the contribution process). Some of these contributions may be the most valuable to the project as a whole, because you're coming to the project with fresh eyes, so you can see the errors and assumptions that seasoned contributors have glossed over.

Note: This disclaimer was originally written by Adrienne Lowe <https://github.com/adriennefriend> for a PyCon talk <https://www.youtube.com/watch?v=6Uj746j9Heo>, and was adapted by dsiidwarfs based on its use in the README file for the MetPy project <https://github.com/Unidata/MetPy>.