Improved Faint Asteroid Discovery Using Deep Learning and Finding Asteroids in an era of Constellations of Cubesat.
||01 July 2020
||8:00 AM - 10:00 AM
||For more information, please contact the Graduate Research School.
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This research will focus on using deep learning Convolutional Neural Networks (CNN) to improve candidate detection rates of fainter and/or faster moving transients in ground based wide-field astronomical survey imagery (Pan-STARRS, Catalina Sky Survey, Vera C. Rubin Observatory once online). The main goals will be (1) to reduce the false positive detections so that more and fainter candidates can be examined by observers, (2) to explore deep learning use to differentiate satellite mega-constellations and asteroids and (3) to improve the detection rates of fainter objects which are otherwise either missed or disqualified by classical data processing Pipelines.