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Found 4 papers, 0 papers with code
Reducing false positives in strong lens detection through effective augmentation and ensemble learning
Our experiments, employing variations of DenseNet and EfficientNet, achieved a best false positive rate (FP rate) of $10^{-4}$, while successfully identifying over 88 per cent of genuine gravitational lenses in the test dataset.
A machine learning based approach to gravitational lens identification with the International LOFAR Telescope
We present a novel machine learning based approach for detecting galaxy-scale gravitational lenses from interferometric data, specifically those taken with the International LOFAR Telescope (ILT), which is observing the northern radio sky at a frequency of 150 MHz, an angular resolution of 350 mas and a sensitivity of 90 uJy beam-1 (1 sigma).
DECORAS: detection and characterization of radio-astronomical sources using deep learning
In addition, DECORAS performs source characterization in terms of the position, effective radius and peak brightness of the detected sources.
Dissecting the Gravitational Lens B1608+656. I. Lens Potential Reconstruction
We present a pixelated approach to modeling simultaneously the lens potential and source intensity of strong gravitational lens systems with extended source-intensity distributions.
