arXiv subjects

A. Lau

Publications and source records attributed to A. Lau.

3 recordsLinked to original sources

arXiv2026

Simulation tools for realistic high-order wavefront correction with the Roman Coronagraph

The Roman Space Telescope Coronagraph Instrument (Roman CGI) will demonstrate high-contrast imaging from space using coronagraphic masks, deformable mirrors, and high-order wavefront sensing and control (HOWFSC). After the baseline technology demonstration, the Roman Coronagraph Community Participation Program (CPP) will pursue science and engineering studies that require realistic predictions of instrument behaviour, observing efficiency, and wavefront control performance. This paper describes \texttt{corgihowfsc}, a configurable simulation framework for repeatable Roman CGI HOWFSC studies. The framework preserves the Roman ground-in-the-loop (GITL) workflow represented by the NASA \texttt{cgi-howfsc} package, while allowing the images to be generated by the higher-fidelity \texttt{corgisim} model. In this configuration, \texttt{cgi-howfsc} remains the reference implementation for estimation, control, and compact-model Jacobian generation; \texttt{corgisim} can supply more flight-like images for studies of instrument performance and robustness. \texttt{corgihowfsc} also coordinates exposure planning, camera settings, expected iteration timing, and contrast normalisation of the HOWFSC loop through \texttt{cgi-eetc}, calibration-related workflows through \texttt{cgi-coralign}, structured diagnostics, and local or distributed execution. By exposing observing modes, image models, probe choices, estimators, controllers, deformable-mirror settings, and runtime options through reusable configuration files, \texttt{corgihowfsc} enables controlled comparisons between reference compact-model simulations and higher-fidelity HOWFSC studies.

astro-ph.IM↗

arXiv2024

ESCAPE project: fundamental detection limits of JWST/NIRCam coronographic observations

In this study, we explored the fundamental contrast limit of NIRCam coronagraphy observations, representing the achievable performance with post-processing techniques. This limit is influenced by photon noise and readout noise, with complex noise propagation through post-processing methods like principal component analysis. We employed two approaches: developing a formula based on simplified scenarios and using Markov Chain Monte Carlo (MCMC) methods, assuming Gaussian noise properties and uncorrelated pixel noise. Tested on datasets HIP\,65426, AF\,Lep, and HD\,114174, the MCMC method provided accurate but computationally intensive estimates. The analytical approach offered quick, reliable estimates closely matching MCMC results in simpler scenarios. Our findings showed the fundamental contrast curve is significantly deeper than the current achievable contrast limit obtained with post-processing techniques at shorter separations, being 10 times deeper at $0.5''$ and 4 times deeper at $1''$. At greater separations, increased exposure time improves sensitivity, with the transition between photon and readout noise dominance occurring between $2''$ and $3''$. We conclude the analytical approach is a reliable estimate of the fundamental contrast limit, offering a faster alternative to MCMC. These results emphasize the potential for greater sensitivity at shorter separations, highlighting the need for improved or new post-processing techniques to enhance JWST NIRCam sensitivity or contrast curve.

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arXiv2023

Improved prior for adaptive optics point spread function estimation from science images: Application for deconvolution

Access to knowledge of the point spread function (PSF) of adaptive optics(AO)-assisted observations is still a major limitation when processing AO data. This limitation is particularly important when image analysis requires the use of deconvolution methods. As the PSF is a complex and time-varying function, reference PSFs acquired on calibration stars before or after the scientific observation can be too different from the actual PSF of the observation to be used for deconvolution, and lead to artefacts in the final image. We improved the existing PSF-estimation method based on the so-called marginal approach by enhancing the object prior in order to make it more robust and suitable for observations of resolved extended objects. Our process is based on a two-step blind deconvolution approach from the literature. The first step consists of PSF estimation from the science image. For this, we made use of an analytical PSF model, whose parameters are estimated based on a marginal algorithm. This PSF was then used for deconvolution. In this study, we first investigated the requirements in terms of PSF parameter knowledge to obtain an accurate and yet resilient deconvolution process using simulations. We show that current marginal algorithms do not provide the required level of accuracy, especially in the presence of small objects. Therefore, we modified the marginal algorithm by providing a new model for object description, leading to an improved estimation of the required PSF parameters. Our method fulfills the deconvolution requirement with realistic system configurations and different classes of Solar System objects in simulations. Finally, we validate our method by performing blind deconvolution with SPHERE/ZIMPOL observations of the Kleopatra asteroid.

astro-ph.IM↗