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Identification of gravitational lenses obscured by foreground light in the KiDS dataset using U-Nets and ResNets — arXiv:2609.21660

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.21660
License (as archived): CC0-1.0
Archived: 2026-10-10T03:04:37.370Z
SHA-256 of the archived text: eae608fba4eae1c281d7f8f777731630722588c3ec8efdcbaee4c4963c185984
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How to cite

Identification of gravitational lenses obscured by foreground light in the KiDS dataset using U-Nets and ResNets — arXiv:2609.21660 — arXiv (en), https://arxiv.org/abs/2609.21660 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_38c4db07aefe (Metraipolis archive dc_38c4db07aefe, sha256 eae608fba4eae1c281d7f8f777731630722588c3ec8efdcbaee4c4963c185984)

BibTeX

@misc{dc38c4db07aefe,
  title = {Identification of gravitational lenses obscured by foreground light in the KiDS dataset using U-Nets and ResNets — arXiv:2609.21660},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.21660},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_38c4db07aefe; sha256 eae608fba4eae1c281d7f8f777731630722588c3ec8efdcbaee4c4963c185984},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.21660. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

*Context.* Many lensing images are often obscured by foreground light from the central galaxies, making them challenging to detect. *Aims.* To address the limitations of previous lens search efforts, particularly for samples with smaller $R_E$ or faint lensed images, we developed a composite convolutional neural network framework that utilizes both U-Net and ResNet architectures for feature extraction and classification. *Methods.* We propose a hybrid search method that combines U-Net and ResNet architectures to enhance the detection of foreground galaxy-obscured lenses. Our approach consists of two main stages: first, the U-Net model separates the foreground galaxy light from potential lensing signals, creating residual images that highlight the lensing features. Next, the ResNet module performs binary classification on these residual images to detect lensing signals. *Results.* We evaluated the hybrid search method with real observational data to demonstrate its effectiveness, achieving a recall of 71.5% and a 4.5% false positive rate at a confidence threshold of 0.6. Applying this method to over 638,398 galaxy samples from the Kilo-Degree Survey Data Release 4 and conducting thorough inspections, we identify 88 Class A, 322 Class B, and 1,758 Class C candidates. *Conclusions.* This hybrid approach significantly enhances the completeness of existing strong gravitational lensing searches and shows great potential for improving future astronomical surveys.

Tema degli esploratori: fisica e astronomia (retro-catalogo arXiv: annunci del 2026-09-21 in astro-ph.IM).
Categorie arXiv: Instrumentation and Methods for Astrophysics (astro-ph.IM) ; Astrophysics of Galaxies (astro-ph.GA).
Pagina del lavoro: https://arxiv.org/abs/2609.21660 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
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