PZ LOR: A Summary of the Submissions

The Rubin Observatory Data Management (DM) team sincerely thanks everyone who engaged with the PZ LOR process.

In total, there were 20 submissions: 19 LOR and 1 “non-LOR” describing DESC’s photo-z activities.

Of the 19 LOR, 12 advocated for particular algorithms, 6 presented scientific use cases, and 1 was a notification of future software development (deep probabilistic networks).

Of the 12 LOR advocating for particular algorithms:

  • 4 were machine-learning (ML) based codes (GPz; DEmP; PZFlow; DNF)
  • 3 were template-fitting (TF) based codes (LePhare; Phosphoros, BPZ)
  • 2 were hybrid ML+TF codes (Delight, ML-accelerated hierarchical SPS models)
  • 3 were for codes which performed “post-processing” to enhance PZ estimates
    • e.g., combine PZ estimates, recalibrate PDFs, refine outlier flags

Read more about these 12 LOR and DM’s shortlist.

Of the 6 LOR describing scientific use cases:

  • 1 focused on galaxies
  • 2 focused on dark energy
  • 3 focused on active galactic nuclei

Read more about the scientific use cases.

The “Roadmap to Photometric Redshifts for the LSST Object Catalog“, DMTN-049, has been updated to include the community input provided via the PZ LOR and a look forward to Rubin Commissioning and the Photo-z Validation Cooperative.

Read more about commissioning and the Photo-z Validation Cooperative.

As always, questions about any of the above are welcome and encouraged to be posted as new topics in the “Science - Photometric Redshifts” category of the Rubin Community Forum.

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