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<p>The Shack-Hartmann wavefront sensor (SHWS) is a widely used non-interferometric wavefront measurement technique. However, for high-slope wavefronts, spot crosstalk and asymmetric distortion cause severe matching ambiguity and centroiding errors. This creates an inherent conflict between dynamic range and reconstruction accuracy. To address this, a graph-theoretic computational model named G-SHWS is proposed. By minimizing the global pairing cost of a bipartite graph constructed between fitted and actual spots, G-SHWS drives the fitted distribution to approximate the true distribution and maps the subaperture attribution of the fitted spots to the actual spots, achieving precise spot-subaperture matching under severe aliasing. Furthermore, incorporating a Graph Attention Network (GAT) embedded with SHWS matching topology, the model utilizes a graph structure to explicitly encode the matching relationships obtained from the matching process, and combines the spatial features and intensity morphology of spots to achieve high-precision reconstruction of strongly distorted wavefronts, effectively circumventing the inherent centroiding errors under large aberrations. Experimental results demonstrate that G-SHWS extends the measurable range of SHWS to 21 times the conventional limit while maintaining a reconstruction error of less than <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(0.05{\rm{\lambda }}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>0.05</mn> <mi mathvariant="normal">λ</mi> </mrow> </math></EquationSource> </InlineEquation>, and remains robust under severe spot loss. These advancements significantly enhance the SHWS’s ability to measure complex aberrations, providing a reliable computational framework for large dynamic range wavefront sensing.</p>

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Large dynamic range Shack-Hartmann wavefront sensing based on a graph-theoretic computational model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月15日

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  • Lintong Du,
  • Rui Xu,
  • Shuxin Liu,
  • Rongjun Shao,
  • Lin Li,
  • Yuhang Zhang,
  • Ziqiang Li,
  • Yuan Qu,
  • Dapeng Tian,
  • Jiamiao Yang

摘要

The Shack-Hartmann wavefront sensor (SHWS) is a widely used non-interferometric wavefront measurement technique. However, for high-slope wavefronts, spot crosstalk and asymmetric distortion cause severe matching ambiguity and centroiding errors. This creates an inherent conflict between dynamic range and reconstruction accuracy. To address this, a graph-theoretic computational model named G-SHWS is proposed. By minimizing the global pairing cost of a bipartite graph constructed between fitted and actual spots, G-SHWS drives the fitted distribution to approximate the true distribution and maps the subaperture attribution of the fitted spots to the actual spots, achieving precise spot-subaperture matching under severe aliasing. Furthermore, incorporating a Graph Attention Network (GAT) embedded with SHWS matching topology, the model utilizes a graph structure to explicitly encode the matching relationships obtained from the matching process, and combines the spatial features and intensity morphology of spots to achieve high-precision reconstruction of strongly distorted wavefronts, effectively circumventing the inherent centroiding errors under large aberrations. Experimental results demonstrate that G-SHWS extends the measurable range of SHWS to 21 times the conventional limit while maintaining a reconstruction error of less than \(0.05{\rm{\lambda }}\) 0.05 λ , and remains robust under severe spot loss. These advancements significantly enhance the SHWS’s ability to measure complex aberrations, providing a reliable computational framework for large dynamic range wavefront sensing.