Frederick Dauphin
I am a 2020 alum of Carnegie Mellon University working as a Science Support Analyst at the Space Telescope Science Institute. My interests are primarily the intersection of data science and astronomy, specifically how we can leverage machine learning algorithms to discover new physics.
Affiliation –Space Telescope Science Institute
Position –Science Support Analyst
Sessions
IR blobs are small, circular, dark artifacts in the Hubble Space Telescope's WFC3 IR images caused by particulates that are occasionally deposited onto a flat mirror that is nearly optically conjugate to the IR detector. Machine learning can potentially reduce the effort currently devoted to visually inspecting blobs. We describe how machine learning (ML) techniques have been implemented to develop software that will automatically find new IR blobs and notify the WFC3 Quicklook team. This report describes the data preparation, development of the ML model, and criteria for success. The results of our latest test cases demonstrate that the model finds blobs reliably, with the model correctly classifying blob and non-blob images 94% and 88% of the time, respectively. We also report tips and lessons learned from our experience in machine learning as a result of this project.