用深度学习从星系图像中精准识别双黑洞候选体，大幅减少误判。
Decoupling candidate dual AGN from chance superpositions in the GOTHIC survey via a deep-learning framework
- 基于YOLOv11框架，自动区分真实双核与星光重叠假象。
- 模型精度达91.9%，筛选出约2.96万组可信双核候选体。
- 适合研究星系合并与黑洞对演化、需高分辨率验证的学者。
双活动星系核（DAGN）是星系并合与超大质量黑洞配对的关键阶段，但在大规模成像巡天中难以识别，因投影效应和空间分辨率限制，紧凑前景恒星与未解析结构常伪装成双核，导致误判。本文重新分析了GOTHIC巡天中46,061个被排除的候选体，主要因双核落入SDSS光纤孔径或间距超限。训练基于YOLOv11定向边界框架构的监督式深度学习模型，利用标注的SDSS影像数据，分离真实双核与恒星污染及虚假对齐。最终模型在双核类别上验证精度为0.919，召回率0.905，F1值0.912，剔除星主导与混叠检测后得到29,605个双核候选体。结构化视觉检查显示54.5%–62%符合真实双核特征，推断约(1.4–1.8)×10⁴个可能系统。结合YOLO分离与确定性GOTHIC中心测量，限定在紧凑区域（d ≤ 6.87''），得保守候选集约13,672个，校准间距达∼0.56''。对最紧凑（≤1 kpc）系统的光谱分析显示其以被动吸收线星系为主，无分辨双峰发射，需更高分辨率后续观测确认。该目录为统计优化的候选列表，非已确认的DAGN，但深度学习显著降低污染并扩展潜在DAGN普查范围。
原文摘要 · Abstract (English)
Dual active galactic nuclei (DAGN) mark a critical phase in the evolution of merging galaxies and the pairing of supermassive black holes, yet they remain difficult to identify in large imaging surveys because of projection effects and limited spatial resolution. Compact foreground stars and unresolved substructure can mimic dual nuclei through chance superposition, complicating automated detection. We revisit the 46,061 galaxies flagged but rejected as DAGN candidates by the GOTHIC pipeline, primarily because the two nuclei fell within the SDSS fibre aperture or exceeded its separation threshold. We train a supervised deep-learning framework based on the YOLOv11 oriented-bounding-box architecture on annotated SDSS imaging to separate genuine dual nuclei from foreground stellar contaminants and other spurious alignments. The final model attains a validation precision of 0.919, recall of 0.905, and $F_1$ of 0.912 for the dual-nuclei class, and yields 29,605 dual-nucleus candidates after removing star-dominated and blended detections. Structured visual inspection indicates that $54.5$--$62\%$ are consistent with genuine dual nuclei, implying $\sim(1.4$--$1.8)\times10^{4}$ plausible systems. Cross-calibrating the YOLO separation against the deterministic GOTHIC centroid measurement and restricting to the compact regime ($d \le 6.87''$) gives a conservative subset of $\sim 13{,}672$ candidates, reaching calibrated separations of $\sim 0.56''$. Spectroscopy of the most compact ($\le 1$~kpc) systems shows they are dominated by passive, absorption-line galaxies with no resolved double-peaked emission, so confirmation requires higher-resolution follow-up. The catalogue is a statistically refined list of candidates, not confirmed DAGN. Nonetheless, deep-learning detection substantially reduces contamination and expands the plausible DAGN census.
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