arXiv subjects

Ezequiel Centofanti

Publications and source records attributed to Ezequiel Centofanti.

3 recordsLinked to original sources

arXiv2026

Point spread function wavefront recovery from in-focus stellar observations

Recovering the wavefront error (WFE) field of an optical system from intensity in-focus observations is a challenging inverse problem with broad implications for telescope point spread function (PSF) modelling. Accurate WFE recovery enables both precise PSF modelling and direct insight into the state of the telescope optics, facilitating the detection of potential malfunctions. Recently, non-parametric PSF models have shown promising performance in modelling complex optical systems in space-based telescopes. WaveDiff is a semi-parametric PSF model that represents the PSF in wavefront space by combining parametric and learnable features with a differentiable forward optical model. This parameterisation enables phase retrieval from in-focus observations by exploiting the spatial variation of the PSF across the field of view (FOV). The original version of WaveDiff achieves outstanding PSF recovery results in pixel space; however, the recovered WFE is far from the ground truth, with a relative error of around $30 \%$. In this paper, we present a new optimisation scenario that bridges WaveDiff's parametric and non-parametric components through wavefront feature projection, yielding a substantial improvement in WFE recovery and making WaveDiff the first demonstrated method to combine wide-field WFE recovery, in-focus-only polychromatic observations, and non-parametric wavefront features in a single framework. We show that incorporating wavefront projections and increasing the number of optimisation cycles enables WaveDiff to recover the WFE with an error of approximately $3 \%$ using only noisy, undersampled, in-focus observations. This represents a tenfold improvement over the original model while further reducing the pixel-space error. The code to reproduce the results of this article is publicly available at https://github.com/tobias-liaudat/wf-psf/tree/v1.4.0

astro-ph.IM↗

arXiv2026

argosim: a Python package for radio interferometric simulations

In this paper, we present argosim, a Python package for simulating radio interferometric observations. The argosim package is modular, lightweight and compatible with all major operating systems. Its computational backend is written in JAX, which allows for greatly accelerated performance as well as the advantage of being fully differentiable. We detail the main argosim modules and describe how to use them to generate an observation, from the antenna positions to the cleaned image. The package is a fully open-source project, and its code is publicly available on GitHub.

astro-ph.IM↗

arXiv2025

Breaking the degeneracy in stellar spectral classification from single wide-band images

The spectral energy distribution (SED) of observed stars in wide-field images is crucial for chromatic point spread function (PSF) modelling methods, which use unresolved stars as integrated spectral samples of the PSF across the field of view. This is particularly important for weak gravitational lensing studies, where precise PSF modelling is essential to get accurate shear measurements. Previous research has demonstrated that the SED of stars can be inferred from low-resolution observations using machine-learning classification algorithms. However, a degeneracy exists between the PSF size, which can vary significantly across the field of view, and the spectral type of stars, leading to strong limitations of such methods. We propose a new SED classification method that incorporates stellar spectral information by using a preliminary PSF model, thereby breaking this degeneracy and enhancing the classification accuracy. Our method involves calculating a set of similarity features between an observed star and a preliminary PSF model at different wavelengths and applying a support vector machine to these similarity features to classify the observed star into a specific stellar class. The proposed approach achieves a 91\% top-two accuracy, surpassing machine-learning methods that do not consider the spectral variation of the PSF. Additionally, we examined the impact of PSF modelling errors on the spectral classification accuracy.

astro-ph.IM↗