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LSSTScienceCollaborations / ObservingStrategy

A community white paper about LSST observing strategy, with quantifications via the the Metric Analysis Framework.
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Deblending metric #532

Open jmeyers314 opened 8 years ago

jmeyers314 commented 8 years ago

The distribution of the best seeing will affect our ability to deblend objects. To start to probe this, @pmelchior and I looked at the spatial distribution of the best seeing. For example, here's a figure showing the 10th percentile best seeing in the i-band over the wide-fast-deep footprint.

minion_1016_10th ile_fwhmgeom_propid_54_and_r_heal_skymap

It appears that our ability to identify blends may vary considerably over the survey footprint.

drphilmarshall commented 8 years ago

Very interesting. Great stuff! @pmelchior https://github.com/pmelchior would you be interesting in adding a few words as well as this figure to the WL science case in the white paper? I think it belongs there, since in the end both the rotational dithering and the best seeing stacks will affect the WL figure of merit, whose proxy is likely to be some sort of shear systematic statistic I guess. If you are up for this, please do start a pull request and let me know when you think you might be ready for it to be merged. This could be one for version 2 of the paper, for example.

pmelchior commented 8 years ago

@drphilmarshall: I can add an explanation to why the seeing variation matters. It's not just the effective depth, which changes the statistics budget, but the shear calibrations depend strongly on seeing. The more homogenous the observed seeing is, the easier it will be to apply accurate calibrations (from deeper reference fields or simulations) to the observations.

What's the timescale for version 2?