AI 中文总结
该研究利用BTK构建基准测试，对比SourceExtractor、SCARLET、DeepDISC三款解混器在LSST模拟混合星系图像中的性能，为解混器研发提供参考。
AI 中文摘要
混合现象将成为LSST数据下游科学分析中系统不确定度的主要来源。我们利用混合工具包（Blending ToolKit，BTK）对多款解混器的性能进行基准测试，开展严格的端到端测试。该基准测试涵盖SourceExtractor、SCARLET、DeepDISC等关键解混算法，旨在对比它们处理LSST/Rubin模拟中混合星系图像的有效性。研究重点在于表征算法在未识别混合（即多个星系被误判为单个天体）场景下的性能，此类情况会引入系统偏差，进而传播到星系巡天的下游宇宙学分析中。借助BTK创建定制化、可复现混合的能力，我们针对源分离、亮度等不同混合条件，系统测试这些解混器。该工具包的标准化评估指标包括检测精度、分割准确率及源重构，可全面评估各算法的优势与局限。各解混器均存在可能影响其在实际巡天条件下真实性能的性能缺陷。研究发现，SCARLET具备高分割与重构性能，DeepDISC对暗弱及低信噪比（SNR）源的检测召回率较强，SourceExtractor的峰值定位能力准确但分割与重构性能较低。该基准测试为现有解混器的性能提供了宝贵见解，并指出了未来的发展方向。
英文摘要
Blending will be a major source of systematic uncertainty in downstream science analyses of LSST data. We benchmark the performance of several deblenders, leveraging the Blending ToolKit (BTK) to perform rigorous, end-to-end testing. This benchmark incorporates key deblending algorithms, including SourceExtractor, SCARLET, and DeepDISC, with the goal of comparing their effectiveness in handling blended galaxy images from LSST/Rubin simulations. A key focus is characterizing algorithm performance in the regime of unrecognized blends, where multiple galaxies are misidentified as a single object, as these cases introduce systematic biases that propagate into downstream cosmological analyses for galaxy surveys. By utilizing BTK's ability to create customized, reproducible blends, we systematically test these deblenders against different blending conditions, such as source separation and brightness. The toolkit's standardized evaluation metrics, including detection precision, segmentation accuracy, and source reconstruction, are comprehensive assessments of each algorithm's strengths and limitations. Each deblender has performance caveats that may impact their true performance in real survey conditions. We find that SCARLET has high segmentation and reconstruction performance, whereas DeepDISC has strong detection recall for faint and low-SNR sources, and SourceExtractor has accurate peak finding abilities but low segmentation and reconstruction performance. This benchmark provides valuable insights into the performance of existing deblenders and highlights areas for future development.
Comments16 pages, 13 figures, 4 tables
