Deadline: Sep 30 2021.
Introduction: The Rubin Observatory Data Management (DM) team is tasked with constructing LSST Science Pipelines that produce science-ready data products, and this includes photometric redshift (PZ) estimates for the LSST data release (DR) Object catalog (ls.st/dpdd). As described in the LSST PZ Roadmap (DMTN-049; see also this topic), DM will select one or more existing, community-vetted algorithms to generate Object PZ which, at least initially, meet a set of minimum scientific attributes and serve the widest variety of science applications.
LOR Purpose: The Rubin science community has a considerable wealth of expertise in generating PZ catalogs and will be the primary users of the Object PZ data products. These letters provide a formal opportunity for the science community to define their minimum scientific needs regarding the LSST Object PZ data product, and/or to advocate for one or more PZ estimators that will meet these scientific needs.
Submission: In the spirit of open cooperation, all letters will be public. Letters should be submitted by creating a new topic in the Photometric Redshifts category in the Rubin Community Forum: start here, click “+New Topic” at upper right, enter a title and text (no tag), and if applicable upload a PDF using the up-arrow icon in the menu bar. Use a title that starts with “LOR” and provides a bit of detail (e.g., “LOR for the CMNN PZ Estimator", “LOR: PZ and Local Volume Science"). Short letters could be presented in the text body of the new topic; longer letters could be provided by uploading an accompanying PDF.
Support for Letter Writers: All are welcome to attend one or more sessions of DM’s monthly LSST PZ Virtual Forum series, which will be focused on discussions about the LORs and the Photo-z Roadmap: PZ Virtual Forum schedule and connection info. Questions are also very welcome to be posted as replies in this thread or as new topics in the Photometric Redshifts category of this forum.
LOR Writers: Any group or individual who would use the LSST Object PZ for their future scientific analyses, and/or are developers of potentially suitable PZ estimators, are encouraged to submit an letter. The DM team is especially interested in hearing the needs of scientists who plan to use the LSST Object catalog but who would/could not generate custom PZ estimates. LORs are not restricted to Rubin data rights holders.
LOR Scope: Letters could qualitatively (or quantitatively) recommend one or more specific PZ estimators (or type of estimator), but they do not have to: letters could instead recommend minimum attributes of the LSST PZ data product that would enable basic LSST science, or focus on describing the science that the LSST PZ data product should enable. Discussions on the research and development of new or improved algorithms can be considered as beyond the scope of these letters, as the shortlist will only include currently existing PZ estimators. New in-depth quantitative analyses of PZ estimator performance can also be considered as beyond the scope of the LORs because such analyses are the focus of the next stage of the PZ roadmap (see below). That said, no letters will be rejected for extending beyond these scope boundaries, which are provided just to guide and inform the community’s efforts.
Guidelines for PZ LORs:
- Keep scope in mind: to identify the minimum scientific attributes, a wide set of science applications, and established PZ estimators for the LSST Object PZ data products.
- Follow the template. It’s okay to skip some sections, but do not add new ones.
- Make it short (1-3 pages ideally) – a lot of detail is not needed at this time.
- Be qualitative – quantitative analyses will be the focus of the PZ Validation Cooperative.
- Refer to DMTN-049 for more detail about the roadmap, evaluation criteria, and LOR.
Beyond the LORs: The Rubin DM team will use these letters to inform the evaluation criteria used to select PZ estimators, and to assemble a shortlist of viable PZ estimators to be evaluated quantitatively using Rubin Commissioning data during the “PZ Validation Cooperative” phase of the PZ Roadmap. DM might add viable PZ estimators to the shortlist if DM thinks that they meet the evaluation criteria and the community’s scientific needs, even if they were not mentioned by any of the LORs. Writing a letter is not required (and will not be construed as a commitment) to participate in the PZ Validation Cooperative.
Template PZ Letter of Recommendation
Title: E.g., “LOR on behalf of X science”, or “LOR for the X PZ Estimator”.
Contributors: Names and affiliations of the letter writers.
Co-signers: If applicable, the names and affiliations of co-signers.
0. Summary Statement
Provide a short statement that introduces the writers’ and the letter’s main recommendation(s). Include the citations for any software discussed.
1. Scientific Utility
Rubin DM seeks to understand LSST PZ-related science cases in order to ensure that the LSST Object PZ data products will be scientifically useful for a wide variety of communities, especially those which would/could not generate custom PZ.
Describe how you would use the PZ data products for your LSST science, or the LSST science enabled by the PZ estimator(s) you are recommending. Examples from past experiences would be useful here.
2. Outputs
Rubin DM seeks to ensure that the selected LSST Object PZ estimator generates science-ready outputs that serve a wide variety of the community’s minimum scientific needs.
If possible, describe the minimum set of PZ outputs that are required for your science, or the outputs generated by the PZ estimator(s) you are recommending. E.g., full posteriors, point estimates, statistics (mode, mean, standard deviation, skewness, kurtosis), best-fit templates, and/or flags (e.g., quality, failure modes).
3. Performance
Rubin DM seeks a general understanding of the minimum quality of PZ estimates needed to meet the basic PZ-related science goals of the community.
If possible, describe the minimum PZ quality that would enable your LSST science (e.g., the minimum point-estimate error at intermediate redshifts, or whatever is relevant to your science goals), or the predicted minimum quality of the PZ estimator(s) you are recommending. It is understood that this information might not be available at this time.
4. Technical Aspects
Rubin DM has a set of technical considerations for PZ estimators, regarding their scalability, inputs, outputs, language, external data sets, training, storage, and compute resources.
If possible – and this is probably only possible for letters being prepared by PZ algorithm developers – please briefly address the technical considerations described in Section 3 of the PZ Roadmap (DMTN-049). Details are not necessary, but an evaluation of the technical considerations at the level of “will meet”, “will probably meet”, “probably will not meet”, “will not meet” would be most helpful at this time.