`` Semi-supervised standardized detection (3SD) of extrasolar planets ''

The directory contains an implementation of the Algorithms 1 (detection) and 2 (p-value computation), as described in Sulis et al., 2021 (accepted in A&A).

It also contains two examples of how to run the detection algorithms:

• ``Example1_NTS_available.py'', which implements the case where a null training sample (NTS) of the stochastic noise is available (see Sec. 5.3).

and

• ``Example2_no_NTS_available.py'', which implements the case where no NTS of the stochastic noise is available. In this case, the NTS is estimated from the RV series under test (see Sec. 5.5).

The different steps are detailed in the python codes.

For practical implementation, we note that the procedure is versatile in the sense that the specific couple (test, periodogram) is left to the user, and the procedure may adapt to different noise sources, null training samples (if available), and time sampling grids.

To run the codes, you need Python3 and the following libraries:

numpy, matplotlib, astropy, lmfit, PyAstronomy, math, tqdm, statsmodels

Note : The current version is a first release, that will be improved with time. If you have any suggestion, please write me a message on Github or at sophia.sulis@lam.fr.