Generic data reduction for nulling interferometry package: the grip of a single data reduction package on all the nulling interferometers

TL;DR

GRIP is a versatile, GPU-accelerated Python package that standardizes data reduction for nulling interferometry instruments using a statistical self-calibration approach, enhancing sensitivity and accuracy.

Contribution

This paper introduces GRIP, a unified data reduction toolbox for nulling interferometry that applies a novel statistical self-calibration method across different instruments.

Findings

GRIP effectively reduces real nulling interferometry data.

The statistical self-calibration method improves sensitivity.

GRIP is compatible with multiple instruments.

Abstract

Nulling interferometry is a powerful observing technique to reach exoplanets and circumstellar dust at separations too small for direct imaging with single-dish telescopes and too large for indirect methods. With near-future instrumentation, it bears the potential to detect young, hot planets near the snow lines of their host stars. A future space mission could detect and characterize a large number of rocky, habitable-zone planets around nearby stars at thermal-infrared wavelengths. The null self-calibration is a method aiming at modelling 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. This statistical approach opens the possibility of designing a GPU-based Python package to reduce the data from any of these instruments, by simply providing the data and a simulator of the instrument. GRIP is a toolbox to reduce nulling and interferometric data based on the statistical self-calibration method. In this article, we present the main features of GRIP as well as applications on real data.