All aspects of high-contrast imaging require software, from simulating optical systems for instrument design to data reduction. The LEOPARD group understands the importance of high-quality open-source software. Specifically, we developed two software packages that have a high impact on the high-contrast imaging community.
People: Emiel Por, Rob van Holstein, Steven Bos, David Doelman
HCIPy
HCIPy is an open-source object-oriented framework written in Python for performing end-to-end simulations of high-contrast imaging instruments for astronomy.
The library defines wavefronts and optical elements for defining an optical system, and provides both Fraunhofer and Fresnel diffraction propgators. Polarization is supported using Jones calculus, with polarizers and waveplates included out of the box. It implements atmospheric turbulence using thin infinitely-long phase screens, and can model scintillation using Fresnel propagation between individual layers. Many wavefront sensors are implemented including a Shack-Hartmann and Pyramid wavefront sensor. Implemented coronagraphs include the vortex, Lyot and APP coronagraph.
By including simulation of both adaptive optics and coronagraphy into a single framework, HCIPy allows simulations including feedback from post-coronagraphic focal-plane wavefront sensors to the AO system.
The main website is hosted at https://hcipy.org. For documentation, see https://docs.hcipy.org.
IRDAP
IRDAP (IRDIS Data reduction for Accurate Polarimetry) is a highly-automated end-to-end pipeline to reduce SPHERE-IRDIS polarimetric data using polarimetric differential imaging (PDI). Its core feature is the model-based correction method of the instrumental polarization effects as described in van Holstein et al. (2020). IRDAP handles data taken both in field- and pupil-tracking mode and using the broadband filters Y, J, H and Ks. Data taken with the narrowband filters can be reduced as well, although with a somewhat worse accuracy. For pupil-tracking observations IRDAP can additionally apply angular differential imaging.
Reducing data with IRDAP is very straightforward and does not require the user to do any coding or have knowledge of Python (IRDAP is written for Python 3.6 and 3.7). IRDAP is simply run from a terminal with only a few commands and uses a configuration file with a limited number of input parameters. Within several minutes, IRDAP performs a complete data reduction from raw data to final data products.
Documentation can be found here: https://irdap.readthedocs.io/en/latest/