A photometric supernova classifier using a data driven approach.

Trained and tested on simulated dataset http://sdssdp62.fnal.gov/sdsssn/SIMGEN_PUBLIC/SIMGEN_PUBLIC_DES.tar.gz

Developed in Python and R languages as PhD project, and inspired to paper from Richards, J.W. et. al (2012).

Thesis will be available soon after defense in fall 2015.

1. Correction for astrophysical effects
2. Interpolation using Gaussian processes
3. Parameter extraction performed by diffusion maps
4. Classification model built with random forest algorithm

• Interpolation is performed using Python package GPy, available at https://github.com/SheffieldML/GPy
• Parameter extraction is performed by R package diffusionMap
• The classification model is built using R package randomForests