Decoupling candidate dual AGN from chance superpositions in the GOTHIC survey via a deep-learning framework
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 arc...
| Authors | Bhavesh Mukheja, Snehanshu Saha, Anwesh Bhattacharya et al. |
|---|---|
| Primary category | astro-ph.GA |
| Published | 25 August 2026 |
| Last updated at source | 25 August 2026 |
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