PZ LOR: A Summary of the Proposed PZ Estimators (DM Shortlist)

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

The Rubin Observatory Data Management (DM) team sincerely thank everyone who wrote a PZ LOR to advocate on behalf of a particular PZ Estimator.

The LORs for five of the PZ estimators demonstrated that the software was established and would (or would likely) be capable of meeting the scientific, performance, and technical aspects as described in the call for LORs: GPz, DEmP, DNF, LePhare, and BPZ.

The LORs for three of the PZ estimators which are still in development also demonstrated that the software would (or would likely) be capable of meeting these aspects: PZFlow, Delight, and Phosphoros.

These eight PZ estimators comprise DM’s formal initial shortlist for potential codes to generate PZ for the LSST Objects catalog: GPz, DEmP, DNF, LePhare, BPZ, PZFlow, Delight, and Phosphoros.

Given that DM’s ability to support the implementation and validation of PZ estimators during commissioning will be limited, the five more established estimators (GPz, DEmP, DNF, LePhare, BPZ) would be prioritized. Further prioritization would be left to the discretion and expertise of the individuals handling the implementation.

The LOR for the in-development estimator based on ML-accelerated hierarchical SPS models was, due to the newness of the method, unclear about whether it would likely meet the technical aspects defined in Section 3 of DMTN-049, but seems very promising. The three LOR for the post-processing codes established their potential positive impacts.

Furthermore, the DM team is aware that additional PZ Estimators, such as those analyzed in Schmidt et al. (2020), would likely be appropriate for the task of generating PZ for LSST Objects: e.g., ANNz2, EAZY, FlexZBoost, METAPhoR, SkyNet, and TPZ.

The “Roadmap to Photometric Redshifts for the LSST Object Catalog”, DMTN-049, has been updated to include a summary of the PZ estimators advocated for by the LOR, and the shortlist.

Questions about any of the above are encouraged to be posted as replies in this topic thread or as new topics in the “Science - Photometric Redshifts” category of the Rubin Community Forum.


DESC = Dark Energy Science Collaboration
DM = Data Management
DP = Data Preview
LOR = Letter of Recommendation (for PZ Estimators)
LSST = Legacy Survey of Space and Time
ML = machine learning
PZ = photometric redshift(s)
SPS = stellar population synthesis
TF = template fitting