collaborators

12 papers

astro-ph.CO2026

Euclid preparation. Refining input galaxy shape distributions for shear calibration simulations

Euclid Collaboration, H. Jansen, N. Martinet +282

The Euclid Wide Survey (EWS) will cover the majority of the extragalactic sky with a resolution similar to the Hubble Space Telescope. This unprecedented data set will introduce a…

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.CO2026

Euclid preparation. Galaxy power spectrum and bispectrum modelling

Euclid Collaboration, K. Pardede, A. Eggemeier +306

Higher-order correlation functions of the large-scale galaxy distribution offer access to information beyond that contained in standard 2-point statistics such as the power spectru…

astro-ph.GA2026

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

Euclid Collaboration, X. Xu, R. Chen +308

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 VIS catalog…

astro-ph.CO2026

Euclid preparation. Impact of redshift distribution uncertainties on the joint analysis of photometric galaxy clustering and weak gravitational lensing

Euclid Collaboration, K. A. Bertmann, A. Porredon +290

One of the mission's key projects is the so-called 32pt analysis, that is, the combination of cosmic shear, photometric galaxy clustering, and galaxy-gala…

astro-ph.GA2026

OJALÁ: Optimizing J-PAS Astronomy for Large-scale Analysis. A foundation model for the SED of galaxies, QSOs and stars

G. Martínez-Solaeche, R. M. González Delgado, R. García-Benito +37

The advent of large-scale surveys requires efficient ML techniques to exploit the information of massive datasets. We present OJALA, a transformer-based autoregressive foundation m…