AI 中文总结
该研究提出将紧凑型卷积神经网络（cCN）作为形态学验证阶段的混合低表面亮度星系检测流程，在HSC-SSP成像中高效筛选出5156个候选体，为LSBG搜寻提供了稳健且计算可扩展的方法。
AI 中文摘要
低表面亮度星系（LSBGs）追踪星系形成中弥散且观测极具挑战性的区域，该区域在光学巡天中仍极易受选择效应影响。我们提出一种混合LSBG检测流程，将紧凑型卷积神经网络（cCNs）作为形态学验证阶段整合至应用于HSC-SSP成像的多步检测框架中。采用SEP进行宽松低阈值源检测，随后进行基于cCN的形态学过滤、符合物理动机的一致性检查，以及使用galfitm的参数化表面亮度建模。cCN被刻意限制为约10^4个可训练参数，以匹配弥散发射的空间尺度并确保巡天规模下的计算效率。cCN将初始检测得到的约77×10^6个天体数量减少了四个数量级以上，同时恢复了所有通过检测阶段的文献中的LSBGs。最终得到的包含5156个候选体的黄金目录，在颜色-颜色和结构参数空间中占据LSBGs的特征位置，与已发表的基于HSC-SSP的样本高度吻合。与SDSS和DESI光谱红移的交叉匹配确认该流程识别出了真实的近邻弥散星系。紧凑型神经网络为LSBG搜寻提供了稳健且计算可扩展的形态学过滤器，有效衔接了经典检测流程与大型通用深度架构。该流程受检测限制而非选择限制，识别出初始源提取阶段是未来改进的主要目标。
英文摘要
Low-surface-brightness galaxies (LSBGs) trace a diffuse and observationally challenging regime of galaxy formation that remains highly susceptible to selection effects in optical surveys. We present a hybrid LSBG detection pipeline that integrates compact convolutional neural networks (cCNs) as a morphological validation stage within a multi-step detection framework applied to HSC-SSP imaging. Permissive low-threshold source detection with SEP is followed by cCN-based morphological filtering, physically motivated consistency checks, and parametric surface-brightness modelling with galfitm. The cCN is deliberately constrained to ${\sim}10^4$ trainable parameters to match the spatial scales of diffuse emission and ensure computational efficiency at survey scale. The cCN reduces the initial detection set of ${\sim}77\times10^6$ objects by more than four orders of magnitude while recovering essentially all literature LSBGs that pass the detection stage. The resulting Gold catalog of 5156 candidates occupies the characteristic loci of LSBGs in colour-colour and structural parameter space, in close agreement with previously published HSC-SSP based samples. Cross-matching with SDSS and DESI spectroscopic redshifts confirms that the pipeline identifies genuine nearby diffuse galaxies. Compact neural networks provide a robust and computationally scalable morphological filter for LSBG searches, effectively bridging classical detection pipelines and large general-purpose deep architectures. The pipeline is detection-limited rather than selection-limited, identifying the initial source extraction stage as the primary target for future improvement.
Comments9 pages, 4 figures
