Advisor(s)
Adam Miller, Northwestern University
Aaron Geller, Northwestern University
Nicholas Easton, Case Western Reserve University
Location
Room Math Study Area
Start Date
26-4-2019 1:40 PM
End Date
26-4-2019 2:05 PM
Abstract
The Large Synoptic Survey Telescope (LSST) is an 8.4 m telescope in Chile. It will observe ~37 billion objects in the night sky and, of these objects, more than 20 million will exhibit significant brightness variations, known as variable stars. Efficient algorithms are needed to classify the different categories of variable stars to better understand different aspects of Astrophysics. To improve the automated classification of variable stars, we have started a citizen science Zooniverse project, Stellar Sleuths. The data given is in the form of light curves (stellar brightness versus time) from the Asteroid Terrestrial-impact Last Alert System (ATLAS), and they can be used to determine if a star shows periodic variations in brightness. However, there is additional information (such as the temperature of a star) that can be used to aid the classification process. In this project, we have experimented with the addition of supplemental information to add to the light curves for classification. With data from Gaia, a space-based telescope with an unprecedented ability to measure precise distances to stars, we place hundreds of millions of additional (normal) stars on the color-magnitude diagram (CMD) as a reference to compare with a given variable star. Prior to April 2018, for many of these stars, this information was poorly unknown. Gaia distance measurements allow us to determine the intrinsic brightness of the stars that it observes. We find that CMDs significantly improve our ability to classify different variable stars.
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Classifying Variable Stars with Gaia Color-Magnitude Diagrams
Room Math Study Area
The Large Synoptic Survey Telescope (LSST) is an 8.4 m telescope in Chile. It will observe ~37 billion objects in the night sky and, of these objects, more than 20 million will exhibit significant brightness variations, known as variable stars. Efficient algorithms are needed to classify the different categories of variable stars to better understand different aspects of Astrophysics. To improve the automated classification of variable stars, we have started a citizen science Zooniverse project, Stellar Sleuths. The data given is in the form of light curves (stellar brightness versus time) from the Asteroid Terrestrial-impact Last Alert System (ATLAS), and they can be used to determine if a star shows periodic variations in brightness. However, there is additional information (such as the temperature of a star) that can be used to aid the classification process. In this project, we have experimented with the addition of supplemental information to add to the light curves for classification. With data from Gaia, a space-based telescope with an unprecedented ability to measure precise distances to stars, we place hundreds of millions of additional (normal) stars on the color-magnitude diagram (CMD) as a reference to compare with a given variable star. Prior to April 2018, for many of these stars, this information was poorly unknown. Gaia distance measurements allow us to determine the intrinsic brightness of the stars that it observes. We find that CMDs significantly improve our ability to classify different variable stars.