Advanced options
Below are examples of different commands, including their before and after plots to demonstrate the desired effects.
Changing the fractional width of the power excess
via –ew & –exwidth
Fractional amount to scale the width of the oscillations envelope by – which is normally calculated w.r.t. solar values.
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Mitigating known Kepler artefacts
via -k, –kc & –kepcorr
Remove the well-known Kepler short-cadence artefact that occurs at/near the long-cadence nyquist frequency (\(\sim 270 \mu \mathrm{Hz}\)) by simulating white noise
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Hard-wiring the lower/upper limits of the power excess
via –lp & –lowerp
Manually set the lower frequency bound (or limit) of the power excess, which is helpful in the following scenarios:
the width of the power excess is wildly different from that estimated by the solar scaling relation
artefact or strange (typically not astrophysical) feature is close to the power excess and cannot be removed otherwise
power excess is near the nyquist frequency
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I’m not sure how I feel about this one
via –npeaks & –peaks
Change the number of peaks chosen in the autocorrelation function (ACF) - this is especially helpful for low S/N cases, where the spectrum is noisy and the ACF has many peaks close the expected spacing (FIX THIS)
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Provide estimate for numax and save some time
via –numax
Turns out that a majority of the scaling relations used in this software can be written in terms of numax and therefore with the single estimate, we can guess the rest of the parameters (and fairly well, at that!)
If the value of \(\rm \nu_{max}\) is known, this can be provided to bypass the first module and save some time. There are also other ways to go about doing this, please see our notebook tutorial that goes through these different ways.
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Setting different frequency limits for the
via –ux & –upperx
Set the upper frequency limit in the power spectrum when estimating \(\rm \nu_{max}\) before the main fitting routine. This is helpful if there are high frequency artefacts that the software latches on to.
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Smooth the echelle diagram by using matplotlib’s built-in interpolator
via -i, –ie & –interpech
Smooth the echelle diagram output by turning on the (bilinear) interpolation, which is helpful for identifying ridges in low S/N cases
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