Hostname: page-component-586b7cd67f-dsjbd Total loading time: 0 Render date: 2024-11-26T17:53:12.287Z Has data issue: false hasContentIssue false
  • English
  • Français

Uncertain Photometric Redshifts with Deep Learning Methods

Published online by Cambridge University Press:  30 May 2017

A. D’Isanto
A. D’Isanto*
Affiliation:
Heidelberg Institute for Theoretical Studies (HITS), Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg - GERMANY 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.

The need for accurate photometric redshifts estimation is a topic that has fundamental importance in Astronomy, due to the necessity of efficiently obtaining redshift information without the need of spectroscopic analysis. We propose a method for determining accurate multi-modal photo-z probability density functions (PDFs) using Mixture Density Networks (MDN) and Deep Convolutional Networks (DCN). A comparison with a Random Forest (RF) is performed.

Keywords

techniques: galaxies: distances and redshiftsphotometricmethods: data analysissurveys(galaxies:) quasars: general etc.
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 12 , Symposium S325: Astroinformatics , October 2016 , pp. 209 - 212
Copyright
Copyright © International Astronomical Union 2017 

References

Christopher, M. Bishop. Mixture density networks. Technical report, 1994.Google Scholar
LeCun, Y., Bottou, L., bengio, Y., & Haffner, P., Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324, November 1998 CrossRefGoogle Scholar
Breiman, Leo. Random forests. Mach. Learn., 45 (1):5–32, October 2001.CrossRefGoogle Scholar
Gneiting, T., Raftery, A. E., Westveld, A. H., & Goldman, T. Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation. Monthly Weather Review, 133:1098, 2005 Google Scholar
Polsterer, K. L., D’Isanto, A., & Gieseke, F. Uncertain photometric redshifts. 2016 Google Scholar
Richards, G. T., Hall, P. B., & Schneider, D. P., et al., VizieR Online Data Catalog: The SDSS-DR7 quasar catalog (Schneider+, 2010). VizieR Online Data Catalog, 7260, May 2010 Google Scholar
Fernique, P., Allen, M. G., et al. Hierarchical progressive surveys. Multi-resolution HEALPix data structures for astronomical images, catalogues, and 3-dimensional data cubes. A&A, 578:A114, June 2015.Google Scholar
Lupton, R. H., Gunn, J. E., & Szalay, A. S. A Modified Magnitude System that Produces Well-Behaved Magnitudes, Colors, and Errors Even for Low Signal-to-Noise Ratio Measurements. AJ, 118:1406–1410, September 1999.CrossRefGoogle Scholar