activity
20242026
collaborators

109 papers

astro-ph.GA2026

Euclid Quick Data Release (Q1). Searching for radio-selected \Euclid-dark galaxies in the EDF-N

Euclid Collaboration, M. Giulietti, I. Prandoni +308

We present and investigate the properties of a sample of radio-selected, Euclid-dark galaxies, identified from LOFAR HBA observations at 144 MHz within the Euclid Deep Field-North…

astro-ph.CO2026

Euclid preparation. First investigation of the impact of cross-contamination on spectroscopic redshift measurements with pixel-level simulations

Euclid Collaboration, F. Passalacqua, S. Anselmi +299

We present a study on simulated data focused on understanding the performance of the spectroscopic redshift measurements with the Near-Infrared Spectrometer and Photometer (NISP) i…

astro-ph.GA2026

Euclid Quick Data Release (Q1): The geometry of dark matter halos from extragalactic streams

Euclid Collaboration, N. Starkman, J. Nibauer +295

Wide-field surveys like Euclid mark a new era of extragalactic stellar stream studies. With a large number of streams, it is now possible to constrain the dark matter halos of gala…

astro-ph.CO2026

Euclid. Populating a dark universe with galaxies using SciPIC

Euclid Collaboration, E. J. Gonzalez, J. Carretero +305

High-fidelity galaxy mocks are crucial for validating analysis pipelines and for cosmological inference. In this context, the Science Pipeline at PIC (SciPIC) is a pipeline specifi…

astro-ph.GA2026

Euclid Quick Data Release (Q1): The impact of AGN emission on SED-derived physical properties

Euclid Collaboration, B. Laloux, A. Bongiorno +309

The Euclid Quick Data Release (Q1) is a powerful dataset to study active galactic nuclei (AGN) and their host galaxies. Deriving their physical properties through multi-component s…

astro-ph.CO2026

Euclid preparation. CII. Non-Gaussianity of 2-pt statistics likelihood: Parameter inference with a non-Gaussian likelihood in Fourier and configuration space

Euclid Collaboration, S. Gouyou Beauchamps, J. Bel +277

In this work we account for this skewness in parameter inference by modelling the likelihood through an Edgeworth expansion which involves the complete skewness tensor, composed of…