Details
-
Type:
Story
-
Status: Done
-
Resolution: Done
-
Fix Version/s: None
-
Component/s: None
-
Labels:
-
Story Points:12
-
Epic Link:
-
Sprint:AP S19-5, AP F19-5 (October), AP F19-6 (November)
-
Team:Alert Production
Description
Current OpSim simulations suggest there are relatively few images per field in u & g in the first years of the LSST survey, which may make it challenging to construct usable DCR models. This ticket is to develop an metric to characterize the relative value of a specific new image at a specific airmass and parallactic angle (given the past pointing history) in improving the DCR model and hence reducing the total number of DCR-induced false positives.
The new notebook defines two different approaches for calculating a DCR metric. The metric can be used to evaluate whether a given set of observations will be sufficient to constrain the DCR model, and whether a new science observation will be well matched by a DCR model.
I think the second, KDE-based, approach is what we should use, though the simple histogram approach has some advantages. As part of your review, please provide a recommendation of which we should move forward with, and any major features or modifications you would like to see.
You can find the notebook here:
https://github.com/lsst-dm/ap_pipe-notebooks/blob/tickets/DM-18416/DM-18416-DCR-metric-development.ipynb