GRIP: a generic data reduction package for nulling interferometry

Nulling interferometry is a powerful observing technique to study exoplanets and circumstellar dust at separations too small for direct imaging with single-dish telescopes. With recent photonics developments and the near-future ground-based instrumental projects, it bears the potential to detect young giant planets near the snow lines of their host stars. The observable quantity of a nulling interferometer is called the null depth, its precise measurement and calibration remain challenging against instrument and atmospheric noise. Null self-calibration is a method aiming to model the statistical distribution of the nulled signal. It has proven to be more sensitive and accurate than average-based data reduction methods in nulling interferometry. The variety of existing and upcoming of nullers raises the issue of consistency of the calibration process, structure of the data and the ability to reduce archived data on the long term. It has also led to many different implementations of the Null self-calibration method. In this article, we introduce GRIP: the first open-source toolbox to reduce nulling data with enhanced statistical self-calibration methods from any nulling interferometric instrument within a single and consistent framework. Astrophysical results show good consistency with two published GLINT and LBTI datasets and confirm nulling precision down to a few 10$^{-4}$.