University of Minnesota
School of Physics & Astronomy
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Melanie Beck

PAN 320 (office)
beck @

About My Work

My work focuses on using machine learning algorithms in conjunction with visual classifications to accurately distinguish between various galaxy types (elliptical, spiral, merger, etc.) at a range of redshifts. Classifying galaxies by their light profiles is crucial for a deeper understanding of the physical attributes of a galaxy as well as their evolution with cosmic time. With so many millions of galaxies, this task cannot be accomplished solely by eye but must be aided by machine. This will be especially true with upcoming datasets such as the LSST and Euclid.

Selected Publications

Beck, M. R., et al., Spectro-polarimetry confirms central powering in a Lya nebula at z=3.09, ApJ, (submitted 2015)


B.S. Physics and Mathematics, University of Nevada, Reno