lightkurve.LightCurve.estimate_cdpp

LightCurve.estimate_cdpp(transit_duration=13, savgol_window=101, savgol_polyorder=2, sigma=5.0) float[source]

Estimate the CDPP noise metric using the Savitzky-Golay (SG) method.

A common estimate of the noise in a lightcurve is the scatter that remains after all long term trends have been removed. This is the idea behind the Combined Differential Photometric Precision (CDPP) metric. The official Kepler Pipeline computes this metric using a wavelet-based algorithm to calculate the signal-to-noise of the specific waveform of transits of various durations. In this implementation, we use the simpler “sgCDPP proxy algorithm” discussed by Gilliland et al (2011ApJS..197….6G) and Van Cleve et al (2016PASP..128g5002V).

The steps of this algorithm are:
  1. Remove low frequency signals using a Savitzky-Golay filter with window length savgol_window and polynomial order savgol_polyorder.

  2. Remove outliers by rejecting data points which are separated from the mean by sigma times the standard deviation.

  3. Compute the standard deviation of a running mean with a configurable window length equal to transit_duration.

We use a running mean (as opposed to block averaging) to strongly attenuate the signal above 1/transit_duration whilst retaining the original frequency sampling. Block averaging would set the Nyquist limit to 1/transit_duration.

Parameters
transit_durationint, optional

The transit duration in units of number of cadences. This is the length of the window used to compute the running mean. The default is 13, which corresponds to a 6.5 hour transit in data sampled at 30-min cadence.

savgol_windowint, optional

Width of Savitsky-Golay filter in cadences (odd number). Default value 101 (2.0 days in Kepler Long Cadence mode).

savgol_polyorderint, optional

Polynomial order of the Savitsky-Golay filter. The recommended value is 2.

sigmafloat, optional

The number of standard deviations to use for clipping outliers. The default is 5.

Returns
cdppfloat

Savitzky-Golay CDPP noise metric in units parts-per-million (ppm).

Notes

This implementation is adapted from the Matlab version used by Jeff van Cleve but lacks the normalization factor used there: svn+ssh://murzim/repo/so/trunk/Develop/jvc/common/compute_SG_noise.m