Colleen Cleary

Rebels Manager

Rebels Manager is a roller derby league management and social app built with Django and PostGreSQL.

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Weird World

Weird World is a collaborative project built with a PostGreSQL and Ruby on Rails back-end and a React front-end. Think of this as a cooler Trip Advisor.

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Women of STEM: Astronomy

Women of Stem: Astronomy is a private forum made with Node, Express, Mongoose, EJS, HTML, and CSS.

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the Tipsy Tiki

Tipsy Tiki is a team-built MEAN stack, full CRUD drink directory created with AngularJS and MongoDB.

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Ghost Neutralizer

Ghost Neutralizer is a point-and-shoot game made with HTML, CSS, JavaScript, and JQuery.

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Through the Darkness of Future’s Past: Tracing Simulated Black Holes Through Time Using Cosmological Simulations

The M-Sigma relation is a significantly tight correlation between the mass of a supermassive black hole and the stellar velocity dispersion of the galactic bulge of its host. The narrow spread of the M-Sigma relation provides a useful and reliable method for estimating the mass of a black hole at the center of a galaxy. However, as the mass of the galaxy decreases, so does the amount of comparable objects in the M-Sigma relation, and the scatter of the relation increases. The lack of data in the lower range is due to the difficulty of detecting supermassive black holes in low mass galaxies. Using several high resolution cosmological simulations, we identified several low mass galaxies which host black holes at z=0. We report on the mass, radial position, and accretion rate of each black hole, along with the mass and star formation history of each host galaxy at various points throughout their lifetimes. We present the results of our search for signatures of visibility that could potentially be detected using current technology. Studying these simulated objects serves as a useful facsimile for understanding and locating low mass galaxies with supermassive black holes in the real, unsimulated universe. This work was recently published in the Monthly Notices of the Royal Astronomical Society

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Principal Component Analysis of Brown Dwarfs

Principal component analysis is a technique for reducing variables and emphasizing patterns in a data set. In this study, the data set consisted of the attributes of 174 brown dwarfs. The PCA was performed on several photometric measurements in near-infrared wavelengths and colors in order to determine if these variables showed a correlation with the physical parameters. This research resulted in two separate models that predict luminosity and temperature. The application of principal component analysis on the near-infrared photometric measurements and colors of brown dwarfs, along with models, provides alternate methods for predicting the luminosity and temperature of brown dwarfs using only photometric measurements.

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