A catalog of broad morphology of Pan-STARRS galaxies based on deep learning

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dc.contributor.author Goddard, Hunter
dc.date.accessioned 2021-04-13T22:32:17Z
dc.date.available 2021-04-13T22:32:17Z
dc.identifier.uri https://hdl.handle.net/2097/41353
dc.description.abstract Autonomous digital sky surveys such as Pan-STARRS have the ability to image a very large number of galactic and extra-galactic objects, and the large and complex nature of the image data reinforces the use of automation. Here we describe the design and implementation of a data analysis process for automatic broad morphology annotation of galaxies, and applied it to the data of Pan-STARRS DR1. The process is based on filters followed by a two-step convolutional neural network (CNN) classification. Training samples are generated by using an augmented and balanced set of manually classified galaxies. Results are evaluated for accuracy by comparison to the annotation of Pan-STARRS included in a previous broad morphology catalog of SDSS galaxies. Our analysis shows that a CNN combined with several filters is an effective approach for annotating the galaxies and removing unclean images. The catalog contains morphology labels for 1,662,190 galaxies with ~95\% accuracy. The accuracy can be further improved by selecting labels above certain confidence thresholds. The catalog is publicly available. en_US
dc.description.sponsorship National Science Foundation en_US
dc.language.iso en_US en_US
dc.subject Machine learning en_US
dc.subject Astronomy en_US
dc.subject Image classification en_US
dc.title A catalog of broad morphology of Pan-STARRS galaxies based on deep learning en_US
dc.type Thesis en_US
dc.description.degree Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Computer Science en_US
dc.description.advisor Major Professor Not Listed en_US
dc.date.published 2021 en_US
dc.date.graduationmonth May en_US


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