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.