Details

    • Type: Sub-task
    • Status: Done (View Workflow)
    • Priority: Normal
    • Resolution: Done
    • Affects Version/s: None
    • Fix Version/s: None
    • Component/s: None
    • Labels:
      None
    • Sprint:
      2DDRP-2019 D

      Description

      I have to find more efficient method to estimate wavefront aberrations.

      First I used Levenberg–Marquardt algorithm, as used by Josh Meyers in HSC work. In early stages of the project I convinced myself that the algorithm was prone to find only local minimum and was giving poor results. I then switched to using emcee algorithm, but this had similar problem. At the moment I am using Parallel-Tempering Ensemble MCMC algorithm which more efficiently explores the parameter space. Problem is that this is very slow and takes large amount of computational time (e.g., ~10 hours on 28 cores for a single donut).

      There are several avenues to explore:

      1. Speeding up computation of individual donuts. This probably means breaking out from GALSIM -> potentially painful

      2. Improving current code or the code that I know. Do I really have to use cool methods such as parallel tempering? Can I get faster convergence, by e.g., evaluating code in stages or setting better initial values? Is it really true that LM settles to wrong local minima and I can not use it? Should I use nested sampling to converge faster?

      3. Use methods from literature. I found two papers that give some details on how they calculated Zernike coefficients relatively cheaply, using iterative methods.
      Tokovinin & Heathcote 2006
      Roodman 2010 and connected DECcam papers
      +Bo Xin et al. algorithm, as mentioned in their papers and DM-Donuts slack channel

        Attachments

          Activity

            People

            • Assignee:
              ncaplar ncaplar
              Reporter:
              ncaplar ncaplar
              Reviewers:
              hassan
            • Votes:
              0 Vote for this issue
              Watchers:
              2 Start watching this issue

              Dates

              • Created:
                Updated:
                Resolved: