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Acevedo Barroso

As seen in: Caltech
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Articles

Euclid Quick Data Release (Q1). XLI. AgileLens: A scalable CNN-based pipeline for strong gravitational lens identification

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By Euclid Collaboration, Acevedo Barroso, da Silva
| Caltech
Abstract We present an end-to-end, iterative pipeline for efficient identification of strong galaxy-galaxy lensing systems, applied to the Euclid Q1 imaging data. Starting from the VIS catalogues, we rejected point sources, applied a magnitude cut (IE ≤ 24) on the deflectors, and ran a pixel-level artefact-and-noise filter to build 96 × 96 pixel cutouts. The VIS+NISP colour composites were constructed with a VIS-anchored luminance scheme that preserves VIS morphology and NISP colour contrast.
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Euclid Quick Data Release (Q1). XXX. The Strong Lensing Discovery Engine E – Ensemble classification of strong gravitational lenses: lessons for Data Release 1

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By Euclid Collaboration, Acevedo Barroso, Domínguez Sánchez
| Caltech
Acknowledgement We gratefully acknowledge the Zooniverse team for their invaluable work on the host platform for Space Warps. This project would not be possible without the immense contributions of citizen scientists. Those contributing classifications for this project are listed on The Team. We greatly appreciate their commitment, enthusiasm, and time.
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Euclid Quick Data Release (Q1). XXVIII. The Strong Lensing Discovery Engine C – Finding lenses with machine learning

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By Euclid Collaboration, Domínguez Sánchez, Acevedo Barroso
| Caltech
Abstract Strong gravitational lensing has the potential to provide a powerful probe of astrophysics and cosmology, but fewer than 1000 strong lenses have been confirmed so far. With a 0.″16 resolution covering a third of the sky, the Euclid telescope will revolutionise the identification of strong lenses, with 170 000 lenses forecasted to be discovered amongst the 1.5 billion galaxies it will observe.
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