Galaxy And Mass Assembly: A Comparison between Galaxy-Galaxy Lens Searches in KiDS/GAMA
Grade Level at Time of Presentation
Senior
Major
BS - Physics
Institution
University of Louisville
KY House District #
31
KY Senate District #
19
Faculty Advisor/ Mentor
Benne Holwerda, PhD.
Department
Department of Physics and Astronomy
Abstract
Strong gravitational lenses are cases where a distant background galaxy is located directly behind a massive foreground galaxy, whose gravity causes the light from the background galaxy to bend around the foreground galaxy. In addition to being visually stunning, these rare events are useful laboratories for furthering our understanding of gravity and cosmology and to determine properties, such as the mass and dark matter content, of the lensing galaxies themselves. The trouble is finding enough of these strong gravitational lenses for further study. The immensity of the catalogs being collected by state-of-the-art telescopes requires equally innovative methods for interpreting that data. We have examined three such techniques for identifying strong lenses: mixed spectroscopy, machine-learning, and citizen-science. Spectroscopy involves studying the objects' signatures across the electromagnetic spectrum and is a tried-and-true, reliable method. Machine-learning promises to find more and different cases of lensing through teaching the computer to recognize features of lensing through visual templates. Citizen-science is a broad term for the inclusion of science-enthusiasts in the process of analyzing images on a scale too large to be undertaken by a small team of experts. For the first time, all three detection techniques have been used in the same regions of the sky, where the Kilo Degree Survey (KiDS) overlaps regions of the Galaxy and Mass Assembly (GAMA) survey. With all three catalogs of strong lenses in hand, we analyzed the strengths and weaknesses of each method and looked for potential crossover between the catalogs. We uncovered inherent biases and advantages to each method in finding lensing systems with different properties, which will serve as a directory for selecting the best methods to be used in new research toward these phenomena. With astronomy moving into the era of large-scale imaging surveys, our project provides a basis for selecting the best techniques for detecting these rare astronomical events.
Galaxy And Mass Assembly: A Comparison between Galaxy-Galaxy Lens Searches in KiDS/GAMA
Strong gravitational lenses are cases where a distant background galaxy is located directly behind a massive foreground galaxy, whose gravity causes the light from the background galaxy to bend around the foreground galaxy. In addition to being visually stunning, these rare events are useful laboratories for furthering our understanding of gravity and cosmology and to determine properties, such as the mass and dark matter content, of the lensing galaxies themselves. The trouble is finding enough of these strong gravitational lenses for further study. The immensity of the catalogs being collected by state-of-the-art telescopes requires equally innovative methods for interpreting that data. We have examined three such techniques for identifying strong lenses: mixed spectroscopy, machine-learning, and citizen-science. Spectroscopy involves studying the objects' signatures across the electromagnetic spectrum and is a tried-and-true, reliable method. Machine-learning promises to find more and different cases of lensing through teaching the computer to recognize features of lensing through visual templates. Citizen-science is a broad term for the inclusion of science-enthusiasts in the process of analyzing images on a scale too large to be undertaken by a small team of experts. For the first time, all three detection techniques have been used in the same regions of the sky, where the Kilo Degree Survey (KiDS) overlaps regions of the Galaxy and Mass Assembly (GAMA) survey. With all three catalogs of strong lenses in hand, we analyzed the strengths and weaknesses of each method and looked for potential crossover between the catalogs. We uncovered inherent biases and advantages to each method in finding lensing systems with different properties, which will serve as a directory for selecting the best methods to be used in new research toward these phenomena. With astronomy moving into the era of large-scale imaging surveys, our project provides a basis for selecting the best techniques for detecting these rare astronomical events.