An adaptive parameter optimization method for astronomical image alignment using Bayesian optimization. I. A hierarchical search strategy for FWHM and SNR

@misc{zhang2026adaptive,
  title = {{An adaptive parameter optimization method for astronomical image alignment using Bayesian optimization. I. A hierarchical search strategy for FWHM and SNR}},
  author = {Yongjie Zhang and Yigong Zhang and Zhenjun Zhang and Xiangming Cheng and Lei Xiong and Xiaoguang Yu and Jie Su and Jiancheng Wang and Haoyang Guo},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.07023},
  eprint = {2609.07023},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.07023},
}

TY  - UNPB
TI  - An adaptive parameter optimization method for astronomical image alignment using Bayesian optimization. I. A hierarchical search strategy for FWHM and SNR
AU  - Yongjie Zhang
AU  - Yigong Zhang
AU  - Zhenjun Zhang
AU  - Xiangming Cheng
AU  - Lei Xiong
AU  - Xiaoguang Yu
AU  - Jie Su
AU  - Jiancheng Wang
AU  - Haoyang Guo
PY  - 2026
DA  - 2026/09/07
PB  - arXiv
DO  - 10.48550/arxiv.2609.07023
UR  - https://arxiv.org/abs/2609.07023
AB  - The alignment and stacking of astronomical images are fundamental steps for detecting faint objects and performing high?precision astrometry. In traditional alignment workflows, the extraction of source lists is critically dependent on key parameters such as the Full Width at Half Maximum (FWHM) and the Signal-to-Noise Ratio (SNR) threshold. These parameters are often selected manually through an inefficient trial-error process that lacks objectivity and does not guarantee optimal results. We present an adaptive method for optimizing astronomical image alignment parameters based on Bayesian Optimization (BO). We frame the parameter search as an optimization problem, with an objective function designed to maximize the number of successfully matched source pairs. By employing a hierarchical search strategy, we perform an efficient global search for FWHM and SNR to automatically determine the optimal combination for a given observational dataset. Experimental results demonstrate that our method effectively handles image data with varying seeing conditions and back?ground noise levels. It rapidly converges to a robust set of alignment parameters, achieving sub-pixel accuracy and significantly improving the automation level and success rate of the alignment process. This work may provide a useful basis for developing large-scale, automated astronomical data processing pipelines
ER  -