Search papers, labs, and topics across Lattice.
This study employs a supervised deep-learning framework based on the YOLOv11 architecture to improve the identification of dual active galactic nuclei (DAGN) in the GOTHIC survey, addressing challenges posed by projection effects and contamination from foreground stars. By training the model on annotated SDSS imaging, the researchers achieved high validation metrics (precision of 0.919, recall of 0.905, and F1 score of 0.912) and identified approximately 29,605 potential dual-nucleus candidates, with structured visual inspections indicating that 54.5% to 62% are likely genuine. The findings significantly refine the candidate list for DAGN, highlighting the model's ability to reduce contamination and expand the census of plausible systems, although further high-resolution follow-up is necessary for confirmation.
Deep learning can sift through imaging noise to reveal thousands of potential dual active galactic nuclei previously dismissed as false positives.
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.
