PynPoint Code for Exoplanet Imaging

TL;DR

PynPoint is a Python package designed for analyzing exoplanet imaging data by modeling and subtracting the star's point spread function, enabling efficient detection of faint nearby planets with demonstrated performance on real data.

Contribution

This paper introduces PynPoint, a new Python-based tool for exoplanet imaging analysis that uses principal component analysis for PSF modeling and is optimized for speed and modularity.

Findings

Successfully reanalyzed beta Pictoris b data in 1.5 minutes on a Mac Pro.

Achieved low memory usage, enabling analysis on standard workstations and laptops.

Demonstrated the package's efficiency and modular design for future extensions.

Abstract

We announce the public release of PynPoint, a Python package that we have developed for analysing exoplanet data taken with the angular differential imaging observing technique. In particular, PynPoint is designed to model the point spread function of the central star and to subtract its flux contribution to reveal nearby faint companion planets. The current version of the package does this correction by using a principal component analysis method to build a basis set for modelling the point spread function of the observations. We demonstrate the performance of the package by reanalysing publicly available data on the exoplanet beta Pictoris b, which consists of close to 24,000 individual image frames. We show that PynPoint is able to analyse this typical data in roughly 1.5 minutes on a Mac Pro, when the number of images is reduced by co-adding in sets of 5. The main computational work parallelises well as a result of a reliance on SciPy and NumPy functions. For this calculation the peak memory load is 6Gb, which can be run comfortably on most workstations. A simpler calculation, by co-adding over 50, takes 3 seconds with a peak memory usage of 600 Mb. This can be performed easily on a laptop. In developing the package we have modularised the code so that we will be able to extend functionality in future releases, through the inclusion of more modules, without it affecting the users application programming interface. We distribute the PynPoint package through the central PyPi sever, and the documentation is available online (http://pynpoint.ethz.ch).