Point Spread Function Modelling for Wide Field Small Aperture Telescopes with a Denoising Autoencoder

Abstract

The point spread function reflects the state of an optical telescope and it is important for data post-processing methods design. For wide field small aperture telescopes, the point spread function is hard to model, because it is affected by many different effects and has strong temporal and spatial variations. In this paper, we propose to use the denoising autoencoder, a type of deep neural network, to model the point spread function of wide field small aperture telescopes. The denoising autoencoder is a pure data based point spread function modelling method, which uses calibration data from real observations or numerical simulated results as point spread function templates. According to real observation conditions, different levels of random noise or aberrations are added to point spread function templates, making them as realizations of the point spread function, i.e., simulated star images. Then we train the denoising autoencoder with realizations and templates of the point spread function. After training, the denoising autoencoder learns the manifold space of the point spread function and can map any star images obtained by wide field small aperture telescopes directly to its point spread function, which could be used to design data post-processing or optical system alignment methods.

arXiv comment10 pages, 10 figures, Published by MNRAS

References in corpus (16)

• Adam: A Method for Stochastic Optimization
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• scikit-image: Image processing in Python
• SparsePak: A Formatted Fiber Field Unit for The WIYN Telescope Bench Spectrograph. I. Design, Construction, and Calibration
• TensorLy: Tensor Learning in Python
• HELIOS-K: An Ultrafast, Open-source Opacity Calculator for Radiative Transfer
• The Stability of the Point Spread Function of the Advanced Camera for Surveys on the Hubble Space Telescope and Implications for Weak Gravitational Lensing
• Principal Component Analysis of the Time- and Position-Dependent Point Spread Function of the Advanced Camera for Surveys
• Principal Component Analysis with Noisy and/or Missing Data
• Towards Precision LSST Weak-Lensing Measurement - I: Impacts of Atmospheric Turbulence and Optical Aberration
• Building the Evryscope: Hardware Design and Performance
• PSF modelling for very wide-field CCD astronomy
• The Point Spread Function Reconstruction by Using Moffatlets - I
• The Multilinear Structure of ReLU Networks
• Spatial Frequency Loss for Learning Convolutional Autoencoders
• On the Transformation of Latent Space in Autoencoders

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• EmulART: Emulating Radiative Transfer -- A pilot study on autoencoder based dimensionality reduction for radiative transfer models
• Neural Posterior Estimation for Cataloging Astronomical Images with Spatially Varying Backgrounds and Point Spread Functions
• Periodic Astrometric Signal Recovery through Convolutional Autoencoders
• Rethinking the modeling of the instrumental response of telescopes with a differentiable optical model