Please use this identifier to cite or link to this item: http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/7915
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dc.contributor.advisorWadadekar, Yogesh
dc.contributor.authorRAVI, ARUN
dc.date.accessioned2023-05-18T10:47:48Z
dc.date.available2023-05-18T10:47:48Z
dc.date.issued2023-05-18
dc.identifier.citation66en_US
dc.identifier.urihttp://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/7915
dc.description.abstractRed geysers are an important class of low star forming galaxies that show telltale signs of AGN maintenance mode driven feedback mechanism that keeps them quenched. There are many questions left to be answered about the nature of these low luminosity AGNs in maintaining quenched state - such as which stage of maintenance is dominant in the local galactic population In order to answer these questions, one requires a statistically significant sample of red geysers to conduct a study of the distribution of their properties. From a sample of ∼ 4700 in an earlier data release of the SDSS-MaNGA survey, 139 red geysers were identified by manually inspecting each example. The latest data release of ∼ 10000 galaxies has not been scoured for red geysers yet. Our goal is to build an automated machine learning model to solve this problem. We present our results with different models and discuss a novel algorithm based on the few shot learning paradigm that can perform the task with ∼ 99% accuracy.en_US
dc.language.isoenen_US
dc.subjectResearch Subject Categories::NATURAL SCIENCESen_US
dc.subjectGalaxy Evolutionen_US
dc.subjectMachine Learningen_US
dc.subjectFew Shot Learningen_US
dc.subjectRed Geyser Galaxiesen_US
dc.subjectDeep Learningen_US
dc.subjectNeural Networksen_US
dc.subjectPrototypical Networksen_US
dc.subjectImage Classificationen_US
dc.titleSearching for Red Geyser Galaxies with Machine Learningen_US
dc.typeThesisen_US
dc.description.embargoOne Yearen_US
dc.type.degreeBS-MSen_US
dc.contributor.departmentDept. of Physicsen_US
dc.contributor.registration20181074en_US
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