dipsearch

lcps.slidingWindow.dipsearch(EPICno, photometry, winSize=10, stepSize=1, Nneighb=2, minDur=2, maxDur=5, detectionThresh=0.995)

Use a sliding window technique to search for dips in photometric time series.

dipsearch iteratively runs through a light curve with a window of N=`winSize` data points. The fluxes within the window are compared to a local median, which is computed from the neighboring Nneighb windows. The data in the current window is ignored for the median computation.

The window is scanned for minDur <= N <= maxDur consecutive data points that fall short of a threshold flux of detectionThresh`*`localMedian. If such an event is detected, its time and minimum flux is returned.

Parameters:

EPICno : str

EPIC number of the target

photometry : Astropy Table

A table with the whole photometric data containing columns ‘TIME’, ‘FLUX’

winSize : int

Size of a window

stepSize : int

steps per slide (Default = 1, i.e. slide one data point per iteration).

Nneighb : int

Number of neighboring windows per side to be considered for the local median (At the boundaries of the time series, the considered data extends to the beginning or end of the array, respectively)

minDur : int

minimum dip duration in # of data points

maxDur : int

maximum dip duration in # of data points

detectionThresh : float

fraction of flux, below which a deviation is registered

Returns:

dips : Astropy table

A table containing parameters of detected dips. Columns: EPICno : str

EPIC number of the target

t_egress : float

time at end of detected dip

minFlux : float

Minimum flux relative to localMedian