Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-24T01:11:36.540Z Has data issue: false hasContentIssue false
  • English
  • Français

Deep data: discovery and visualization Application to hyperspectral ALMA imagery

Published online by Cambridge University Press:  30 May 2017

Erzsébet Merényi Open the ORCID record for Erzsébet Merényi [Opens in a new window] ,
Joshua Taylor  and
Andrea Isella
Erzsébet Merényi
Affiliation:
Dept. of Statistics, Rice University, 6100 Main Street, Houston, Texas 77005, U.S.A. email: [email protected] Dept. of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston, Texas 77005, U.S.A. email: [email protected]
Joshua Taylor
Affiliation:
Dept. of Statistics, Rice University, 6100 Main Street, Houston, Texas 77005, U.S.A. email: [email protected]
Andrea Isella
Affiliation:
Dept. of Physics and Astronomy, Rice University, 6100 Main Street, Houston, Texas 77005, U.S.A. email: [email protected]
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.

Leading-edge telescopes such as the Atacama Large Millimeter and sub-millimeter Array (ALMA), and near-future ones, are capable of imaging the same sky area at hundreds-to-thousands of frequencies with both high spectral and spatial resolution. This provides unprecedented opportunities for discovery about the spatial, kinematical and compositional structure of sources such as molecular clouds or protoplanetary disks, and more. However, in addition to enormous volume, the data also exhibit unprecedented complexity, mandating new approaches for extracting and summarizing relevant information. Traditional techniques such as examining images at selected frequencies become intractable while tools that integrate data across frequencies or pixels (like moment maps) can no longer fully exploit and visualize the rich information. We present a neural map-based machine learning approach that can handle all spectral channels simultaneously, utilizing the full depth of these data for discovery and visualization of spectrally homogeneous spatial regions (spectral clusters) that characterize distinct kinematic behaviors. We demonstrate the effectiveness on an ALMA image cube of the protoplanetary disk HD142527. The tools we collectively name “NeuroScope” are efficient for “Big Data” due to intelligent data summarization that results in significant sparsity and noise reduction. We also demonstrate a new approach to automate our clustering for fast distillation of large data cubes.

Keywords

neural machine learningclusteringALMAprotoplanetary disks
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 12 , Symposium S325: Astroinformatics , October 2016 , pp. 281 - 290
Copyright
Copyright © International Astronomical Union 2017 

References

Boehler, Y., Isella, A., Weaver, E., et al. 2016, submittedGoogle Scholar
Csardi, G. & Nepusz, T. 2006, InterJournal, Complex Systems, 1695Google Scholar
DeSieno, D. 1988, in Proc. IEEE Int’l Conference on Neural Networks (ICNN), July 1988, Vol. I, New York, I–117–124Google Scholar
Fortunato, S. 2010, Physics Reports, 486, 75 Google Scholar
Houlahan, P. & Scalo, J. 1992, The Astrophysical Journal, 393, 171 CrossRefGoogle Scholar
Isella, A., Pérez, L. M., Carpenter, J. M., et al. 2013, The Astrophysical Journal, 775, 30 Google Scholar
Kohonen, T. 1988, Self-Organization and Associative Memory (New York: Springer-Verlag)Google Scholar
Lachmair, J., Merényi, E., Porrmann, M., & Rückert, U. 2013, Neurocomputing, 112, 189 Google Scholar
Merényi, E., Jain, A., & Villmann, T. 2007, IEEE Trans. on Neural Networks, 18, 786 Google Scholar
Merényi, E., Tasdemir, K., & Zhang, L. 2009, in Lecture Notes in Computer Science, Vol. 5400, Similarity-Based Clustering, ed. Biehl, M., Hammer, B., Verleysen, M., & Villmann, T. (Berlin Heidelberg: Springer Verlag), 138–168 Google Scholar
Merényi, E., Taylor, J., & Isella, A. 2016, in Proc. of IEEE Symposium Series on Computational Intelligence (SSCI 2016), Athens, Greece, forthcomingGoogle Scholar
Pons, P. & Latapy, M. 2005, ArXiv Physics e-prints, physics/0512106Google Scholar
Rosolowsky, E. & Leroy, A. 2006, Publications of the Astronomical Society of the Pacific, 118, 590 Google Scholar
Rosolowsky, E., Pineda, J., Kaufmann, J., & Goodman, A. 2008, The Astrophysical Journal, 678, 1338 Google Scholar
Rosvall, M., & Bergstrom, C. 2008, Proceedings of the National Academy of Science, 105, 1118 Google Scholar
Sousbie, T. 2013, arXivGoogle Scholar
Tasdemir, K. & Merényi, E. 2009, IEEE Trans. on Neural Networks, 20, 549 Google Scholar
Tasdemir, K. & Merényi, E. 2011, IEEE Trans. Systems, Man and Cybernetics, Part B, 41, 1039, dOI: 10.1109/TSMCB.2010.2104319 Google Scholar
Williams, J., de Geus, E., & Blitz, L. 1994, The Astrophysical Journal, 428, 693 Google Scholar