Hostname: page-component-65f69f4695-7j7ct Total loading time: 0 Render date: 2025-06-30T20:43:20.926Z Has data issue: false hasContentIssue false
  • English
  • Français

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
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

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

References

Arodź, T. 2012, Computing and Informatics, 24 no. 2 (2012): 183–199.Google Scholar
Chen, Y.-C., Genovese, C. R., & Wasserman, L. 2013, arXiv preprint arXiv:1312.2098 (2013).Google Scholar
Elad, M. & Aharon, M. 2006, Image Processing, IEEE Transactions on 15, no. 12 (2006): 3736–3745.Google Scholar
Freeman, P. E., Izbicki, R., Lee, A. B., Newman, J. A.et al. 2013, MNRAS (2013): stt1016.Google Scholar
Gretton, A., Sejdinovic, D., Strathmann, H., Balakrishnan, S., Pontil, M., Fukumizu, K., & Sriperumbudur, B. K. 2012, Advances in neural information processing systems, pp. 1205–1213. 2012.Google Scholar
Jafari-Khouzani, K. & Soltanian-Zadeh, H. 2005, Pattern Analysis and Machine Intelligence, IEEE Transactions on 27, no. 6 (2005): 1004–1008.CrossRefGoogle Scholar
Mairal, J., Bach, F., Ponce, J., & Sapiro, G. 2010, The Journal of Machine Learning Research 11 (2010): 19–60.Google Scholar
Windhorst, R. A., Cohen, S. H., Hathi, N. P., McCarthy, P. J.et al. 2011, ApJS 193, no. 2 (2011): 27.Google Scholar