I am a software engineer II developing and maintaining software that I use to conduct explorative analysis on astronomical calibration data at the Space Telescope Science Institute. Moreover, I work on developing and enhancing tools that contribute to the accessibility of astronomical data within the astronomy community.Affiliation –
Space Telescope Science InstituteTwitter handle –
@jenny4medinaGitHub ID –
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.