Fast-cadence High-contrast Imaging with Information Field Theory

Abstract

Although many exoplanets have been indirectly detected in recent years, direct imaging of them with ground-based telescopes remains challenging. In the presence of atmospheric fluctuations, it is ambitious to resolve the high brightness contrasts at the small angular separation between the star and its potential partners. Post-processing of telescope images has become an essential tool to improve the resolvable contrast ratios. This paper contributes a post-processing algorithm for fast-cadence imaging, which deconvolves sequences of telescope images. The algorithm infers a Bayesian estimate of the astronomical object, as well as the atmospheric optical path length, including its spatial and temporal structures. For this, we utilize physics-inspired models for the object, the atmosphere, and the telescope. The algorithm is computationally expensive but allows us to resolve high contrast ratios despite short observation times and no field rotation. We test the performance of the algorithm with pointlike companions synthetically injected into a real data set acquired with the SHARK-VIS pathfinder instrument at the LBT telescope. Sources with brightness ratios down to 6 × 10−4 to the star are detected at 185 mas separation with a short observation time of 0.6 s.

Re-Envisioning Numerical Information Field Theory (NIFTy.re): A Library for Gaussian Processes and Variational Inference

“…We expect NIFTy.re to be highly useful for many imaging applications and envision many applications within and outside of astrophysics Arras et al, 2022;Eberle et al, 2022Eberle et al, , 2023Frank et al, 2017;S. Hutschenreuter et al, 2022;Sebastian Hutschenreuter et al, 2023;Leike et al, 2020;Mertsch & Phan, 2023;J. Roth et al, 2023;Jakob Roth et al, 2023;Scheel-Platz et al, 2023;Tsouros et al, 2024;Welling et al, 2021;Westerkamp et al, 2023).…”