Astro-ML-in-Julia

Using machine learning methods in Julia to analyze astronomical time series data.

##Process

1. Galex data stuff
2. Split Data for Parallelization
3. Preprocessing
   1. more descriptive stuff here
   2. more stuff
4. Combine Features
5. Normalize and Impute Features
6. Cluster

##Keywords Extracted from Kepler FITS Files

• KEPLERID unique Kepler target identifier
• GRCOLOR [mag] (g-r) color, SDSS bands
• JKCOLOR [mag] (J-K) color, 2MASS bands
• GKCOLOR [mag] (g-K) color, SDSS g - 2MASS K
• TEFF [K] Effective temperature
• LOGG [cm/s2] log10 surface gravity
• FEH [log10([Fe/H])] metallicity
• RADIUS [solar radii] stellar radius

##Things TO DO:

• Clean up the directory
• make this documentation more comprehensive
• create multiplots
  • lightcurve
  • periodgrams
  • phasefolded light curve
• finish multiplot driver
  • title the plot with KID
  • print the plots to a directory
• get membership of clustering results
• look at projections of the feature space
  • plot the targets against two features
• Clean up drivers and use SETTINGS.txt to make code work for any machine
• Investigate artifacts on feature plots