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README.md
cheetah.py
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cplfit.py
jdlc1.txt
jdlc2.txt
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lclmleastsq.py
lclmtests.py
lcmftest.py
lcmultifit.py
lcmultifit2.py
lcmultifit3.py
lcmultifitNEW.py
lcsfGtest.py
lcsftest.py
lcsinglefit.py
lcsinglefit2.py
lcsinglefit3.py
lcsinglefitG.py
lcsinglefitNEW.py
lcspot.py
lcspot2.py
lcspot3.py
lcspotNEW.py
lcspottests.py
pytest.py
readfits.py
splinefit.py
testdata.tgz

README.md

Cheetah

This repo contains code + etc. for modeling starspots in photometric data (lightcurves).

Contributors: Lucianne Walkowicz (Princeton Dept of Astrophysical Sciences) Michael Thomas (Princeton 'XX --> what is your year, MT?) Adam Finkestein (Princeton Computer Science Dept)

NEXT STEPS (AF notes from 2014-05-28):

  • try > 2 spots with synth data
  • range of periods
  • cluster to run multiple experiments in parallel (ionic?)
  • other optimization strategies instead of just L-M / nonlinear simplex...? (just try stuff built into python libraries)

OLD Plan of action:

Code Logistics:

  • AF + MT find their GitHub passwords and send them to LW
  • LW gives AF + MT push access to the repo
  • MT pushes existing code to the repo

Lightcurve Fitting:

  • Begin by fitting synthetic/artificial lightcurves generated by the code itself

  • Possible to take derivatives of the analytic spot functions in Eker, e.g. using Mathematica or Maple?

  • Use python/scipy built-in optimizers (e.g. nonlinear simplex, gradient methods, etc)

  • MCMC implementation - what are the confidence intervals on the fitted parameters?

  • number of spots: start with 1, expand to 2 or 3.

  • Revisit issue of how to do multispot fitting (i.e. avoiding overlapping spots)

  • Eventually apply model to real data, first targeting stars whose inclination is constrained to lie within a particular range.

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