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20232026
most citedEuclid: The Early Release Observations Lens Search Experiment

20 citations · 122 across the 25 of their papers we have counts for

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6 papers · 1 filter

astro-ph.IM2026

Euclid Quick Data Release (Q1). AstroVink: A vision transformer approach to find strong gravitational lens systems

Euclid Collaboration, S. H. Vincken, K. Rojas +302

We present AstroVink, a vision transformer classifier designed for automated identification of strong lens candidates in Euclid imaging. We build upon the DINOv2 encoder, fine tune…

astro-ph.IM2025

Euclid Quick Data Release (Q1). From simulations to sky: Advancing machine-learning lens detection with real Euclid data

Euclid Collaboration, N. E. P. Lines, T. E. Collett +301

In the era of large-scale surveys like Euclid, machine learning has become an essential tool for identifying rare yet scientifically valuable objects, such as strong gravitational…

astro-ph.IM2025★ 1 cited

Euclid Quick Data Release (Q1): From images to multiwavelength catalogues: the Euclid MERge Processing Function

Euclid Collaboration, E. Romelli, M. Kümmel +345

The Euclid satellite is an ESA mission that was launched in July 2023. \Euclid is working in its regular observing mode with the target of observing an area of $14\,000~\text{deg}^…

astro-ph.IM2024

Euclid: Searches for strong gravitational lenses using convolutional neural nets in Early Release Observations of the Perseus field

R. Pearce-Casey, B. C. Nagam, J. Wilde +204

The Euclid Wide Survey (EWS) is predicted to find approximately 170 000 galaxy-galaxy strong lenses from its lifetime observation of 14 000 deg^2 of the sky. Detecting this many le…

astro-ph.IM2023★ 2 cited

Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers

D. Huppenkothen, M. Ntampaka, M. Ho +19

Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of…

astro-ph.IM2023★ 18 cited

Astronomaly at scale: searching for anomalies amongst 4 million galaxies

Verlon Etsebeth, Michelle Lochner, Mike Walmsley +1

Modern astronomical surveys are producing datasets of unprecedented size and richness, increasing the potential for high-impact scientific discovery. This possibility, coupled with…