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Dealing with Uncertain Multimodal Photometric Redshift Estimations

Published online by Cambridge University Press:  30 May 2017

Kai L. Polsterer
Kai L. Polsterer*
Affiliation:
Astroinformatics, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118, Heidelberg, Germany email: [email protected]
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Abstract

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Due to limitations in available instrumentation and observation time, a spectroscopic determination of distance is not feasible for all objects in the sky. Therefore statistical methods that estimate redshifts, based on photometric measurement are of tremendous importance to many astrophysical questions. Determining cosmological parameters and understanding evolutionary processes in the universe are just two examples. When perform astrophysical analyses, it is necessary to treat the uncertainties of the estimates correctly. Over-simplification of results and the usage of wrong tools to evaluate the performance of probabilistic redshift estimates were commonly found in the literature. We present proper tools for evaluating uncertain redshift estimates and discuss the necessity of multimodal redshift distributions.

Keywords

instrumentation: miscellaneousmethods: data analysismethods: miscellaneousmethods: statisticaltechniques: photometricgalaxies: distances and redshifts
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 12 , Symposium S325: Astroinformatics , October 2016 , pp. 156 - 165
Copyright
Copyright © International Astronomical Union 2017 

References

Bishop, C. M. 2007, Pattern Recognition and Machine Learning (Information Science and Statistics), 1 edn. Springer Google Scholar
Dawid, A. P., 1984, Journal of the Royal Statistical Society. Series A (General), 278CrossRefGoogle Scholar
Gieseke, F., Polsterer, K. L., & Zinn, P.-C. 2012, Astronomical Data Analysis Software and Systems XXI, 461, 537 Google Scholar
Gieseke, F., Polsterer, K. L., Oancea, C. E. & Igel, C. 2014 European Symposium on Artificial Neural Networks (ESANN), 87Google Scholar
Gneiting, T., Raftery, A. E., Westveld, A. H. III, & Goldman, T. 2005, Monthly Weather Review, 133, 1098 CrossRefGoogle Scholar
Gneiting, T., Balabdaoui, F., & Raftery, A. E. 2007 Journal of the Royal Statistical Society: Series B, 69, 243 CrossRefGoogle Scholar
Gneiting, T. & Raftery, A. E., 2007, Journal of the American Statistical Association, 102, 359 CrossRefGoogle Scholar
Grimit, E. P., Gneiting, T., Berrocal, V. J., & Johnson, N. A., 2006, Quarterly Journal of the Royal Meteorological Society, 132, 2925 CrossRefGoogle Scholar
Heinermann, J., Kramer, O., Polsterer, K. L., & Gieske, F. 2013 Annual Conference on Artificial Intelligence, 86CrossRefGoogle Scholar
Hersbach, H., 2000, Weather and Forecasting, 15, 559 2.0.CO;2>CrossRefGoogle Scholar
Polsterer, K. L., Zinn, P.-C., & Gieseke, F. 2013, MNRAS, 428, 226 CrossRefGoogle Scholar
Polsterer, K. L., Gieseke, F., Igel, C., & Goto, T. 2014, Astronomical Data Analysis Software and Systems XXIII, 485, 425 Google Scholar
Polsterer, K. L., D'Isanto, A., & Gieseke, F. 2016 MNRAS Google Scholar
Schneider, D. P., Richards, G. T., Hall, P. B., et al. 2010, The Astronomical Journal, 139, 2360 CrossRefGoogle Scholar