Point spread function modelling for wide-field small-aperture telescopes with a denoising autoencoder

Jia, Peng; Li, Xiyu; Li, Zhengyang; Wang, Weinan; Cai, Dongmei

doi:10.1093/mnras/staa319

Cited by 20 publications

(15 citation statements)

References 31 publications

(35 reference statements)

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“…This allows to shift the usual data-driven modeling space from the pixels to the wavefront and translate much of the modeling complexity into the forward model. The use of DL methods for the modeling of instrumental response fields is limited due to the complexity of the modeling problem and the fact that current efforts are blind to the physics of the problem [5]. The present work lays the groundwork for the introduction of physically-motivated and interpretable DL methods into the modeling of instrumental response fields.…”

Section: Introductionmentioning

confidence: 99%

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

Liaudat¹,

Starck²,

Kilbinger³

et al. 2021

Preprint

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We propose a paradigm shift in the data-driven modeling of the instrumental response 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 allows to transfer a great deal of complexity from the instrumental response into the forward model while being able to adapt to the observations, remaining data-driven. Our framework allows a way forward to building powerful models that are physically motivated, interpretable, and that do not require special calibration data. We show that for a simplified setting of a space telescope, this framework represents a real performance breakthrough compared to existing data-driven approaches with reconstruction errors decreasing 5 fold at observation resolution and more than 10 fold for a 3x super-resolution. We successfully model chromatic variations of the instrument's response only using noisy broad-band in-focus observations. * https://tobias-liaudat.github.io Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021).

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Section: Introductionmentioning

confidence: 99%

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

Liaudat¹,

Starck²,

Kilbinger³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…This has motivated the development of a wide range of methodologies for PSF modeling. These include models based on Moffat functions [6], polynomial models [7][8][9][10], principal component analysis [11][12][13], sparse matrix factorization [14][15][16], specific basis functions [17,18], optimal transport [19], neural networks [20][21][22][23], and a parametric model of the telescope's optics [24][25][26].…”

Section: Contextmentioning

confidence: 99%

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

Liaudat¹,

Starck²,

Kilbinger³

et al. 2022

Preprint

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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 .

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“…For the PNET, when observation condition changes, we propose to extract images of reference stars to obtain PSFs and background noise (Jia et al 2020c). Then we use simulation methods discussed in Section 3 to generate simulated images and train the PNET with these images.…”

Section: Keeping Performance Of the Pnet For Images With Variable Psfsmentioning

confidence: 99%

The Deep Neural Network based Photometry and Astrometry Framework for Wide Field Small Aperture Telescopes

Jia¹,

Sun²,

Yang³

et al. 2021

Preprint

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Wide field small aperture telescopes (WFSATs) are mainly used to obtain scientific information of point-like and streak-like celestial objects. However, qualities of images obtained by WFSATs are seriously affected by the background noise and variable point spread functions. Developing high speed and high efficiency data processing method is of great importance for further scientific research. In recent years, deep neural networks have been proposed for detection and classification of celestial objects and have shown better performance than classical methods. In this paper, we further extend abilities of the deep neural network based astronomical target detection framework to make it suitable for photometry and astrometry. We add new branches into the deep neural network to obtain types, magnitudes and positions of different celestial objects at the same time. Tested with simulated data, we find that our neural network has better performance in photometry than classical methods. Because photometry and astrometry are regression algorithms, which would obtain high accuracy measurements instead of rough classification results, the accuracy of photometry and astrometry results would be affected by different observation conditions. To solve this problem, we further propose to use reference stars to train our deep neural network with transfer learning strategy when observation conditions change. The photometry framework proposed in this paper could be used as an end-to-end quick data processing framework for WFSATs, which can further increase response speed and scientific outputs of WFSATs.

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Point spread function modelling for wide-field small-aperture telescopes with a denoising autoencoder

Cited by 20 publications

References 31 publications

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

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

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

The Deep Neural Network based Photometry and Astrometry Framework for Wide Field Small Aperture Telescopes

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