Please use this identifier to cite or link to this item: http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/7915
Title: Searching for Red Geyser Galaxies with Machine Learning
Authors: Wadadekar, Yogesh
RAVI, ARUN
Dept. of Physics
20181074
Keywords: Research Subject Categories::NATURAL SCIENCES
Galaxy Evolution
Machine Learning
Few Shot Learning
Red Geyser Galaxies
Deep Learning
Neural Networks
Prototypical Networks
Image Classification
Issue Date: 18-May-2023
Citation: 66
Abstract: Red 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.
URI: http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/7915
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