A method and system for image quality compensation of astronomical spectrometer based on deep learning

Abstract

The invention discloses an image quality compensation method and system of an astronomical spectrometer based on deep learning. The method comprises the following steps of S1, collecting a calibrationspectrogram of the astronomical spectrometer and temperature data at a corresponding moment; S2, determining the spectral drift amount of each spectrogram; S3, inputting the temperature data and spectral drift amount data into a computer for deep learning, taking temperature and an instrument running time of the astronomical spectrometer as input and the spectral drift amount as output to obtaina temperature-spectral drift amount neural network; S4, when the astronomical spectrometer is running, predicting the magnitude of the current spatial direction drift and the magnitude of the dispersion direction drift; and S5, performing reverse equivalent regulation according to the predicted magnitude of the drift amount. According to the image quality compensation method and system disclosed by the invention, by reading historical calibration data and the temperature data for deep learning, the magnitude of the spectrogram drift amount is predicted in real time. The compensation accuracy of the method is high, and the data measurement accuracy of telescope heaven touring and the telescope running efficiency can be effectively improved.

Description

technical field [0001] The invention relates to the field of spectral imaging of astronomical spectrometers and instruments of astronomical spectrometers, in particular to an image quality compensation method and system for optical fiber spectrometers. Background technique [0002] Radial velocity is a very important measurement parameter in astronomical observations. According to existing measurements and theoretical analysis, the stability of the spectrometer is one of the most important factors affecting the measurement accuracy of radial velocity. The ambient temperature of the spectrometer is one of the main factors affecting the stability of the astronomical spectrometer. The change of the ambient temperature will cause a certain linear or nonlinear change in the structure of the spectrometer, which will cause the image drift on the CCD target surface, and then cause the performance of the spectrometer to deteriorate. decline. Therefore, an efficient and high-precisio...

AI Technical Summary

Problems solved by technology

The ambient temperature of the spectrometer is one of the main factors affecting the stability of the astronomical spectrometer. The change of the ambient temperature will cause a certain linear or nonlinear change in the structure of the spectrometer, which will cause the image drift on the CCD target surface, which will cause the performance of the spectrometer to deteriorate. decline

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