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arXiv Machine Learning By Bhavesh Mukheja, Snehanshu Saha, Anwesh Bhattacharya, Mousumi Das, Fran\c{c}oise Combes, Sudhanshu Barway

Decoupling candidate dual AGN from chance superpositions in the GOTHIC survey via a deep-learning framework

Read the original on arXiv Machine Learning →

The study presents a deep‑learning approach (YOLOv11) trained on SDSS images to distinguish genuine dual active galactic nuclei (DAGN) from chance superpositions and foreground stars in the GOTHIC survey. Applying the model to 46,061 previously rejected candidates yields 29,605 dual‑nucleus candidates, with 54.5–62 % likely genuine, and a conservative subset of ~13,672 compact systems. The resulting catalogue refines the DAGN candidate list, reducing contamination and expanding the plausible census, though spectroscopic confirmation remains needed.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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