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Estimating the distribution of Galaxy Morphologies on a continuous space

Published online by Cambridge University Press:  01 July 2015

Giuseppe Vinci ,
Peter Freeman ,
Jeffrey Newman ,
Larry Wasserman  and
Christopher Genovese
Giuseppe Vinci
Affiliation:
Dept. of Statistics, Baker Hall, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA email: gvinci@andrew.cmu.edu
Peter Freeman
Affiliation:
Dept. of Statistics, Baker Hall, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA email: gvinci@andrew.cmu.edu
Jeffrey Newman
Affiliation:
Dept. of Physics & Astronomy, University of Pittsburgh, 310 Allen Hall 3941 O'Hara St., Pittsburgh, PA 15260, USA
Larry Wasserman
Affiliation:
Dept. of Statistics, Baker Hall, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA email: gvinci@andrew.cmu.edu
Christopher Genovese
Affiliation:
Dept. of Statistics, Baker Hall, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA email: gvinci@andrew.cmu.edu
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Abstract

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The incredible variety of galaxy shapes cannot be summarized by human defined discrete classes of shapes without causing a possibly large loss of information. Dictionary learning and sparse coding allow us to reduce the high dimensional space of shapes into a manageable low dimensional continuous vector space. Statistical inference can be done in the reduced space via probability distribution estimation and manifold estimation.

Keywords

dictionary learningmanifold estimationRadon transformredshiftsparse codinggalaxies: statistics
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 10 , Symposium S306: Statistical Challenges in 21st Century Cosmology , May 2014 , pp. 68 - 71
Copyright
Copyright © International Astronomical Union 2015 

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