Comments (3)
I haven't found any examples where a squared exponential kernel is a better representation of real data than something like the SHOTerm which is built into celerite. You can approximate a squared exponential using a mixture of terms, but I expect that would be unstable and probably wouldn't gain you anything.
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Thanks for the quick reply. I'm working with very noisy lightcurves so I would like to model the instrumental systematics. So you're saying I could use a granulation kernel to do this?
from celerite.
For sure! Depending on the details of your data there might be better choices, but I can't see any reason why an exponential squared kernel would be it!
I'd be happy to provide more specifc suggestions if you want to drop me an email.
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Related Issues (20)
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