Event Title

An Automated Search for Almost Dark Galaxies in the Sloan Digital Sky Survey

Session Number

U04

Advisor(s)

Don York, University of Chicago

Location

A-135

Start Date

28-4-2016 9:50 AM

End Date

28-4-2016 10:15 AM

Abstract

One of the most important problems in astrophysics today is the mystery of dark matter. A class of dwarf galaxies known as Almost Dark Galaxies (ADGs), which contain a high percentage of dark matter and little to no starlight, are the ideal laboratories to study this mystery. I developed two distinct methods for automated detection of ADGs in the Sloan Digital Sky Survey (SDSS) – the first approach selects image sections with uneven light distribution not centered on bright objects. This indicates presence of a faint diffuse object, usually a galaxy, whose light causes the SDSS bounding box to shift away from the center object. A second method that I am currently developing identifies clusters of closely separated blue objects. This pattern is clearly observable in Leo P, a prototypical ADG, and my algorithm is searching to locate similar clusters in order to identify ADG candidates. Preliminary results from both approaches are promising, with the bounding box approach successfully identifying Leo P. I am presently confirming results from the blue cluster approach. Constructing a large, statistically valid sample of ADGs could not only help understand how these galaxies formed but also play an important role in determining the distribution of dark matter in the universe.


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Apr 28th, 9:50 AM Apr 28th, 10:15 AM

An Automated Search for Almost Dark Galaxies in the Sloan Digital Sky Survey

A-135

One of the most important problems in astrophysics today is the mystery of dark matter. A class of dwarf galaxies known as Almost Dark Galaxies (ADGs), which contain a high percentage of dark matter and little to no starlight, are the ideal laboratories to study this mystery. I developed two distinct methods for automated detection of ADGs in the Sloan Digital Sky Survey (SDSS) – the first approach selects image sections with uneven light distribution not centered on bright objects. This indicates presence of a faint diffuse object, usually a galaxy, whose light causes the SDSS bounding box to shift away from the center object. A second method that I am currently developing identifies clusters of closely separated blue objects. This pattern is clearly observable in Leo P, a prototypical ADG, and my algorithm is searching to locate similar clusters in order to identify ADG candidates. Preliminary results from both approaches are promising, with the bounding box approach successfully identifying Leo P. I am presently confirming results from the blue cluster approach. Constructing a large, statistically valid sample of ADGs could not only help understand how these galaxies formed but also play an important role in determining the distribution of dark matter in the universe.