Calculate depth and duration of exoplanet's transit using Python, astropy or lightkurve I am creating a program for the analysis of exoplanets in Python using Astropy and Lightkurve libraries. I have light curve of specified star and I would like to calculate depth and duration of planet transit. There is example for Kepler 10 bellow with these steps:


Load target pixel image for Kepler 10,
Convert it to flattened time series,
Calculate period using periodogram,
Create folded light curve.


import lightkurve as lk

target_pixel = lk.search_targetpixelfile("Kepler-10", quarter=1).download()
lightcurve = target_pixel.to_lightcurve(aperture_mask=target_pixel.pipeline_mask).flatten()
periodogram = lightcurve.to_periodogram(method="bls", period=np.arange(0.5, 10, 0.001))
period = periodogram.period_at_max_power

folded.scatter()
print(period)


The output is (I marked the depth and duration in red):



Now, how can I calculate duration of transit (approximately from -0.15 to to 0 on x-axis)? Interval from -0.5 to 0.5 should be whole period (0.837 d), so transit duration should be +/- 0.13 d.

And what about transit depth? I was thinking about:

min = np.min(folded.flux)
median = np.median(folded.flux)
median / min - 1 # 0.00038


However, the minimum point is not the one in the middle of the transit due to noise, so transit depth may not be accurate.
Calculation of both, depth and duration, is usually done not on the raw data but derived from a fit to the data.

In your last three lines of code you also calculate the average / medium over all data while you should calculate the uneclipsed mean or median flux only for the non-transit time (with using median it possibly has only a tiny influence, yet it might).

As a first and crude step, I'd de-noise the data by applying a floating average filter over the data; you will have to test for its width and see what gives you best results: you don't want to average out features, but you want to average-out noise.

The better approach is to not smooth but actually fit a physical model to the data which takes into account the typical light curve behaviour of a transit. For an implementation I can point you to pytransit (reference paper). (Are you sure you are not re-inventing the wheel?). See also this paper by Maxted and Gill for a comparison of a few algorithms.