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Deep learning framework identifies dual active galactic nuclei candidates

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a deep-learning framework using the YOLOv11 architecture to identify dual active galactic nuclei (DAGN) from the GOTHIC survey. This model was trained on annotated Sloan Digital Sky Survey (SDSS) imaging to distinguish genuine DAGN candidates from foreground stars and other spurious alignments. The framework achieved a validation precision of 0.919 and recall of 0.905, identifying over 29,000 potential DAGN candidates, with a significant portion estimated to be genuine systems. AI

IMPACT This research demonstrates a new method for identifying astronomical phenomena using deep learning, potentially improving the efficiency and accuracy of astrophysical research.

RANK_REASON This is a research paper detailing a novel application of deep learning to an astronomical survey. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep learning framework identifies dual active galactic nuclei candidates

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This is a research paper detailing a novel application of deep learning to an astronomical survey. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · 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

    arXiv:2608.24164v1 Announce Type: cross Abstract: 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 effect…

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