About Me

My name is Ilija Medan and I am a second year graduate student in the Department of Physics & Astronomy at Georgia State University. Generally, I am interested in studying the structure and evolution of the Milky Way using results from large astronomical surveys. At Georgia State, I am conducting my research under the advisement of Dr. Sebastien Lepine.

Pre-Graduate Research

After receiving my B.S. in Mechanical Engineering, with a minor in Physics, from Santa Clara University, I worked at the SOFIA Science Center as a research assistant for first Dr. Ravi Sankrit and then Dr. B-G Andersson. With Dr. Sankrit I used HST images of Kepler's Supernova Remnant in order to catalog and study the evolution of the bright, radiative knots, and the stellar properties (color and proper motion) of the stars in the field (230th AAS poster and abstract).

With Dr. Andersson I studied the variations in grain alignment in the wall of the Local Bubble using archival polarimetry, photometry and spectroscopy (231st AAS poster and abstract). The plot above shows a proxy for grain alignment efficiency vs. galactic longitude for the observed grain alignment efficiency in the wall of the Local Bubble (data points). The solid line shown in the plot is the strength of the radiation field from the nearby OB associations at the distance of the Local Bubble wall. As it can be seen, there is a strong correlation between the observed grain alignment efficiency and the nearby radiation field, which supports radiatively driven grain alignment. Additionally with Dr. Andersson, I preformed radiaitve transfer modeling of the circumstellar envelope of the carbon-rich AGB star IRC +10216.

Graduate Research

During my first year, I have developed a Bayesian cross-matching method to match high proper motion stars from Gaia DR2 and SUPERBLINK to photometric catalogs spanning the UV to the infrared (such as GALEX, SDSS, Pan-STARRS, RAVE, 2MASS and AllWISE). To associate objects across these catalogs that differ in accuracy and magnitude limit, I compare the multidimensional (astrometry+photometry) distribution of all sources in the vicinity of each high proper motion star ("true" distribution) to a reference distribution of random field stars obtained by extracting all sources in a region on the sky displaced 2’ from the high proper motion star ("displaced" distribution). The 2’ offset preserves the local field stellar density and magnitude distribution, which allows us to characterize the local frequency of chance alignments, and calculate Bayesian statistics for each match. The two panel below shows such distributions, where the left panel shows the true distribution and the right panel the displaced distribution. It can be seen that the distribution of random field stars (the displaced distirbution) is a component of the true distribution. Because of this, the subtraction of the displaced from the true distribution will reveal the distribution of actual matches that can be used to calculate Bayesian probabilites. My resulting high Bayesian probability photometry catalog has been shown to return a higher percent of matches then the internal Gaia DR2 cross-match, especially for Pan-STARRS where I have a match rate of ~98% within the footprint of the survey as compared to ~21% for the internal Gaia match.

Curriculum Vitae

Contact

Office:
1 Park Place
#714
Atlanta, GA 30303

Email:
medan@astro.gsu.edu

Comet Classification

Using the catalog of cometary orbital parameters provided by Rocher 2007, I classifed comets using the Tisserand parameter (Carusi & Valsecchi 1987). The most abundant type of comets in this catalog were the so-called "Jupiter Family" comets, and are shown in red in the below graphic. For scale, the orbiit of Earth (inner dashed line) and Jupiter (outer dashed line) are shown. The large "clumping" of comets whose long-axis of orbit is centered around x=0 are associated with the 71 fragments of Comet 73P/Schwassmann-Wachmann 3, which began to break apart in September of 1995 (Crovisier et al. 1996).

Asteroid Families in PanSTARRS

I extracted the positions, date of observation and colors for all asteroids observed by the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS). I then associated the Pan-STARRS asteroids with known, numbered asteroids using a catalog of asteroid orbital elements. To determine asteoid families in various regions based on distance from the sun, I first identified background asteroids (i.e. asteroids that did not belong to any family) using a Local Outlier Factor, where the backgrund asteroids as shown as red data points in the graphic below. The remaining asteroids were then clustered into families using Agglomerative Clustering. In the graphic to the left, various families are color-coded and the families' centers in orbital element space are shown with a plus sign.

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