Research done during summer of 2017, through CIERA's LSST group. The end goal of the project is to develop a machine learning algorithm to classify supernovae based on their photometry. One issue is the frequent gaps in data. This poster explores and compares several different techniques for interpolation. The machine learning portion of the project is still in progress, and will be completed throughout the 2017/2018 school year. Poster last updated: 8/20/17
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