Rethinking the modeling of the instrumental response of telescopes with a differentiable optical model

Liaudat, Tobias; Starck, Jean-Luc; Kilbinger, M.; Frugier, Pierre-Antoine

doi:10.48550/arxiv.2111.12541

Cited by 4 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We present three variations of the WaveDiff model that we refer to as its flavors. This paper extends the work in [31,32].…”

Section: Our Contributionsupporting

confidence: 84%

Rethinking data-driven point spread function modeling with a differentiable optical model

Liaudat¹,

Starck²,

Kilbinger³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

In astronomy, upcoming space telescopes with wide-field optical instruments have a spatially varying point spread function (PSF). Certain scientific goals require a highfidelity estimation of the PSF at target positions where no direct measurement of the PSF is provided. Even though observations of the PSF are available at some positions of the field of view (FOV), they are undersampled, noisy, and integrated in wavelength in the instrument's passband. PSF modeling represents a challenging ill-posed problem, as it requires building a model from these observations that can infer a super-resolved PSF at any wavelength and any position in the FOV. Current data-driven PSF models can tackle spatial variations and super-resolution. However, they are not capable of capturing PSF chromatic variations.Our model, coined WaveDiff, proposes a paradigm shift in the data-driven modeling of the point spread function field of telescopes. By adding a differentiable optical forward model into the modeling framework, we change the data-driven modeling space from the pixels to the wavefront. This change allows the transfer of a great deal of complexity from the instrumental response into the forward model. The proposed model relies on efficient automatic differentiation technology as well as modern stochastic first-order optimization techniques recently developed by the thriving machine-learning community. Our framework paves the way to building powerful models that are physically motivated and do not require special calibration data.This paper demonstrates the WaveDiff model on a simplified setting of a space telescope. The proposed framework represents a performance breakthrough with respect to the existing state-of-the-art data-driven approach. The pixel reconstruction errors decrease 6-fold at observation resolution and 44-fold for a 3x super-resolution. The ellipticity errors are reduced by a factor of at least 20 and the size error by a factor of more than 250. By only using noisy broad-band in-focus observations, we successfully capture the PSF chromatic variations due to diffraction. WaveDiff source code and examples associated with this paper are available at this link .

show abstract

“…We present three variations of the WaveDiff model that we refer to as its flavors. This paper extends the work in [31,32].…”

Section: Our Contributionsupporting

confidence: 84%

Rethinking data-driven point spread function modeling with a differentiable optical model

Liaudat¹,

Starck²,

Kilbinger³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Even though the WaveDiff model is built in the wavefront space, it is constrained only by noisy in-focus observations without requiring particular calibration observations. This paper extends the work in [33,34].…”

Section: Our Contributionsupporting

confidence: 76%

Rethinking data-driven point spread function modeling with a differentiable optical model

Liaudat

Starck²,

Kilbinger³

et al. 2023

Inverse Problems

View full text Add to dashboard Cite

In astronomy, upcoming space telescopes with wide-field optical instruments have a spatially varying point spread function (PSF). Specific scientific goals require a high-fidelity estimation of the PSF at target positions where no direct measurement of the PSF is provided. Even though observations of the PSF are available at some positions of the field of view (FOV), they are undersampled, noisy, and integrated into wavelength in the instrument’s passband. PSF modeling represents a challenging ill-posed problem, as it requires building a model from these observations that can infer a super-resolved PSF at any wavelength and position in the FOV. Current data-driven PSF models can tackle spatial variations and super-resolution. However, they are not capable of capturing PSF chromatic variations. Our model, coined WaveDiff, proposes a paradigm shift in the data-driven modeling of the point spread function field of telescopes. We change the data-driven modeling space from the pixels to the wavefront by adding a differentiable optical forward model into the modeling framework. This change allows the transfer of a great deal of complexity from the instrumental response into the forward model. The proposed model relies on efficient automatic differentiation technology and modern stochastic first-order optimization techniques recently developed by the thriving machine-learning community. Our framework paves the way to building powerful, physically motivated models that do not require special calibration data. This paper demonstrates the WaveDiff model in a simplified setting of a space telescope. The proposed framework represents a performance breakthrough with respect to the existing state-of-the-art data-driven approach. The pixel reconstruction errors decrease 6-fold at observation resolution and 44-fold for a 3x super-resolution. The ellipticity errors are reduced at least 20 times, and the size error is reduced more than 250 times. By only using noisy broad-band in-focus observations, we successfully capture the PSF chromatic variations due to diffraction.

show abstract

“…It was shown by Sureau et al (2020) that Tikhonet can handle such data by deconvolving each galaxy in the field independently and using the PSF corresponding to the center of the galaxy. The PSF field is generally not known, but can be reconstructed from observed stars present in the image (Liaudat et al, 2021;Liaudat et al, 2022). • Many galaxies share very similar morphologies.…”

Section: Advantages and Drawbacks Of Tikhonet For Astronomical Image ...mentioning

confidence: 99%

Deep learning-based galaxy image deconvolution

Akhaury

Starck

Jablonka

et al. 2022

Front. Astron. Space Sci.

Self Cite

View full text Add to dashboard Cite

With the onset of large-scale astronomical surveys capturing millions of images, there is an increasing need to develop fast and accurate deconvolution algorithms that generalize well to different images. A powerful and accessible deconvolution method would allow for the reconstruction of a cleaner estimation of the sky. The deconvolved images would be helpful to perform photometric measurements to help make progress in the fields of galaxy formation and evolution. We propose a new deconvolution method based on the Learnlet transform. Eventually, we investigate and compare the performance of different Unet architectures and Learnlet for image deconvolution in the astrophysical domain by following a two-step approach: a Tikhonov deconvolution with a closed-form solution, followed by post-processing with a neural network. To generate our training dataset, we extract HST cutouts from the CANDELS survey in the F606W filter (V-band) and corrupt these images to simulate their blurred-noisy versions. Our numerical results based on these simulations show a detailed comparison between the considered methods for different noise levels.

show abstract

Rethinking the modeling of the instrumental response of telescopes with a differentiable optical model

Cited by 4 publications

References 14 publications

Rethinking data-driven point spread function modeling with a differentiable optical model

Rethinking data-driven point spread function modeling with a differentiable optical model

Rethinking data-driven point spread function modeling with a differentiable optical model

Deep learning-based galaxy image deconvolution

Contact Info

Product

Resources

About