Sparse representation of photometric redshift probability density functions: Preparing for petascale astronomy

Research output: Contribution to journalArticlepeer-review


One of the consequences of entering the era of precision cosmology is the widespread adoption of photometric redshift probability density functions (PDFs). Both current and future photometric surveys are expected to obtain images of billions of distinct galaxies. As a result, storing and analysing all of these PDFs will be non-trivial and even more severe if a survey plans to compute and store multiple different PDFs. In this paper we propose the use of a sparse basis representation to fully represent individual photo-z PDFs. By using an orthogonal matching pursuit algorithm and a combination of Gaussian and Voigt basis functions, we demonstrate how our approach is superior to a multi-Gaussian fitting, as we require approximately half of the parameters for the same fitting accuracy with the additional advantage that an entire PDF can be stored by using a 4-byte integer per basis function, and we can achieve better accuracy by increasing the number of bases. By using data from the Canada-France-Hawaii Telescope Lensing Survey, we demonstrate that only 10-20 points per galaxy are sufficient to reconstruct both the individual PDFs and the ensemble redshift distribution, N(z), to an accuracy of 99.9 per cent when compared to the one built using the original PDFs computed with a resolution of δz = 0.01, reducing the required storage of 200 original values by a factor of 10-20. Finally, we demonstrate how this basis representation can be directly extended to a cosmological analysis, thereby increasing computational performance without losing resolution nor accuracy.

Original languageEnglish (US)
Pages (from-to)3550-3561
Number of pages12
JournalMonthly Notices of the Royal Astronomical Society
Issue number4
StatePublished - Jun 2014


  • Astronomical data bases: miscellaneous
  • Galaxies: distances and redshifts
  • Galaxies: statistics
  • Methods: data analysis
  • Methods: statistical

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science


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