研究目的
To improve the detection of faint and small celestial bodies in the solar system by applying a shifting and stacking method to sequential optical images, enhancing the detection efficiency of faint moving objects.
研究成果
The shifting and stacking method effectively enhances the detection of faint moving objects, raising the detection limit to magnitudes fainter than 21. It was validated through experiments with known and unknown asteroids, demonstrating practical applicability in astronomical surveys.
研究不足
The method is constrained by the accuracy of velocity estimation and image alignment, which can affect SNR improvement. Factors like image quality variations due to zenith distance changes and skylight background may limit detection efficiency, especially for very faint objects.
1：Experimental Design and Method Selection:
The method involves shifting and stacking sequential images to enhance the signal-to-noise ratio (SNR) of moving objects. It uses a false position method to pre-estimate apparent velocities and iteratively determines accurate positions based on SNR and image elongation.
2：Sample Selection and Data Sources:
Sequential optical images from the China Near Earth Object Survey Telescope (CNEOST) were used, specifically 600 images observed over three days in February
3：List of Experimental Equipment and Materials:
20 The Near Earth Object Telescope (a Schmidt telescope of 1.04/1.20/1.80 m) equipped with a CCD of 10Kx10K pixels (pixel size 9 μm x 9 μm, subtending 1.029 arcseconds per pixel).
4：04/20/80 m) equipped with a CCD of 10Kx10K pixels (pixel size 9 μm x 9 μm, subtending 029 arcseconds per pixel).
Experimental Procedures and Operational Workflow:
4. Experimental Procedures and Operational Workflow: Images were preprocessed to remove field stars and background. Subimages were cut based on estimated velocities, stacked to align moving objects, and iterated to refine velocities using SNR and elongation criteria.
5：Data Analysis Methods:
SNR calculations were performed using a model accounting for photon noise, dark current noise, and readout noise. Statistical analysis of velocity distributions and iterative fitting were used to determine object positions.
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