A two-stage deep learning pipeline (HT-LCNN detector + VGG6 classifier) trained on augmented real and simulated data detects streaks in OmegaCAM frames with F1 > 0.95 on test sets and 0.99 precision on real 2023 data, uncovering 25,335 streaks including >20% uncatalogued objects across 1.2 million f
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astro-ph.IM 2years
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UNVERDICTED 2representative citing papers
M-EPDet reports 98.31% recall for genuine sources while rejecting 92.99% of instrumental artifacts and 98.18% of cosmic ray events, reducing candidate volume by 99.25% in EP-WXT on-orbit data.
citing papers explorer
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Streak detection in the VST/OmegaCAM archive using deep learning
A two-stage deep learning pipeline (HT-LCNN detector + VGG6 classifier) trained on augmented real and simulated data detects streaks in OmegaCAM frames with F1 > 0.95 on test sets and 0.99 precision on real 2023 data, uncovering 25,335 streaks including >20% uncatalogued objects across 1.2 million f
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M-EPDet: Real-Time Real-Bogus Classification and Transient Candidate Judgement for the EP-WXT Pipeline via Multi-Modal Data
M-EPDet reports 98.31% recall for genuine sources while rejecting 92.99% of instrumental artifacts and 98.18% of cosmic ray events, reducing candidate volume by 99.25% in EP-WXT on-orbit data.