Point spread function modelling for astronomical telescopes: a review focused on weak gravitational lensing studies
Not on the map. Why?
Abstract (arXiv)
The accurate modelling of the Point Spread Function (PSF) is of paramount importance in astronomical observations, as it allows for the correction of distortions and blurring caused by the telescope and atmosphere. PSF modelling is crucial for accurately measuring celestial objects' properties. The last decades brought us a steady increase in the power and complexity of astronomical telescopes and instruments. Upcoming galaxy surveys like Euclid and LSST will observe an unprecedented amount and quality of data. Modelling the PSF for these new facilities and surveys requires novel modelling techniques that can cope with the ever-tightening error requirements. The purpose of this review is three-fold. First, we introduce the optical background required for a more physically-motivated PSF modelling and propose an observational model that can be reused for future developments. Second, we provide an overview of the different physical contributors of the PSF, including the optic- and detector-level contributors and the atmosphere. We expect that the overview will help better understand the modelled effects. Third, we discuss the different methods for PSF modelling from the parametric and non-parametric families for ground- and space-based telescopes, with their advantages and limitations. Validation methods for PSF models are then addressed, with several metrics related to weak lensing studies discussed in detail. Finally, we explore current challenges and future directions in PSF modelling for astronomical telescopes.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 10 of its 259 references that are in the map. First posted on arXiv 12 Jun 2023.
Builds on
10 of 259 references.
References · 10
- 2015Automatic differentiation in machine learning: a surveycited by 855
- 2018Computational Optimal Transportcited by 780
- 2017Wasserstein Dictionary Learning: Optimal Transport-based unsupervised non-linear dictionary learningcited by 35
- 2022Rethinking data-driven point spread function modeling with a differentiable optical modelcited by 4
- 2020Point Spread Function Modelling for Wide Field Small Aperture Telescopes with a Denoising Autoencodercited by 3
- 2021Rethinking the modeling of the instrumental response of telescopes with a differentiable optical modelcited by 3
- 2018Fast Point Spread Function Modeling with Deep Learningcited by 2
- 2020PSF--NET: A Non-parametric Point Spread Function Model for Ground Based Optical Telescopescited by 2
- 2014Super-resolution method using sparse regularization for point-spread function recoverycited by 1
- 2016Constraint matrix factorization for space variant PSFs field restorationcited by 1
Built on by
Citing papers per year: 2026: 1.
1 paper.