Machine Learning on the Milky Way Project DR1
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README.md

Brut

This repository contains the code and manuscript text used in the paper

The Milky Way Project: Leveraging Citizen Science and Machine Learning to Detect Interstellar Bubbles. Beaumont, Goodman, Kendrew, Williams, Simpson 2014, ApJS, in press (arXiv link)

The v1 tag represents the state of the code at the time of publication.

Data associated with this project is also archived at The Dataverse (doi:10.7910/DVN/26463)

High level summary

Brut uses a database of known bubbles (from the Milky Way Project) and Spitzer images from our galaxy to build an automatic bubble classifier. The classifier is based on the Random Forest algorithm, and uses the WiseRF implementation of this algorithm.

The main question that Brut attempts to answer is "does this image contain a bubble?" The images presented to Brut are 2-color square postage stamps extracted from 8 and 24 micron Spitzer images of the Galactic plane.

The picloud platform was used to perform some of the computation in parallel, in the cloud.

If you want to dig into the details of how the model is built, start with the Makefile in the scripts/ directory.

Organization

bubbly/

Contains the python library used to fit Random Forest classification models to Spitzer images

figures/

Contains code to generate figures in the paper

notebooks/

Contains several IPython notebooks in various states of organization -- some are polished documents describing aspects of the analysis, others are temporary workbooks.

paper/

Contains the manuscript text itself

scripts/

Python scripts to fit models and generate other derived data products

Reproduction

This repository is MIT Licensed.

To reproduce the figures and models generated for the paper, type:

python setup.py develop
cd bubbly/data && make
cd ../../paper && make

Though I promise you you'll have to play with dependencies to get this all set up :)

Dependencies

Brut is built on top of several python libraries, and uses data from the GLIMPSE and MIPSGAL surveys from the Spitzer Space Telescope. You'll need the following libraries

  • aplpy
  • astropy
  • h5py
  • IPython
  • matplotlib
  • numpy
  • scipy
  • skimage
  • sklearn
  • picloud
  • WiseRF

In addition, you need to download the GLIMPSE and MIPSGAL mosaic data. The Makefile inside bubbly/data does this.