collaborators

6 papers

astro-ph.CO2026

Propagating data-driven galaxy redshift distribution uncertainties in 32-pt analyses

Jaime Ruiz-Zapatero, Qianjun Hang, Yun-Hao Zhang +7

Uncertainties in the radial distribution of galaxies, , are one of the major contributions to the error budget of early Stage-IV galaxy survey analy…

astro-ph.CO2026

Euclid preparation. CIV. Impact of galaxy intrinsic alignment modelling choices on Euclid 3x2pt cosmology

Euclid Collaboration, D. Navarro-Gironés, I. Tutusaus +274

The Euclid galaxy survey will provide unprecedented constraints on cosmology, but achieving unbiased results will require an optimal characterisation and mitigation of systematic e…

astro-ph.CO2026

Euclid preparation. LXXXIX. Accurate and precise data-driven angular power spectrum covariances

Euclid Collaboration, K. Naidoo, J. Ruiz-Zapatero +280

We develop techniques for generating accurate and precise internal covariances for measurements of clustering and weak-lensing angular power spectra. These methods have been design…

astro-ph.IM2026

Redshift Assessment Infrastructure Layers (RAIL): Rubin-era photometric redshift stress-testing and at-scale production

The RAIL Team, Jan Luca van den Busch, Eric Charles +30

Virtually all extragalactic use cases of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) require the use of galaxy redshift information, yet the vast majorit…

astro-ph.IM2026

Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration

LSST Dark Energy Science Collaboration, Eric Aubourg, Camille Avestruz +63

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that cha…

astro-ph.CO2025

Flinch: A Differentiable Framework for Field-Level Inference of Cosmological parameters from curved sky data

Andrea Crespi, Marco Bonici, Arthur Loureiro +6

We present Flinch, a fully differentiable and high-performance framework for field-level inference on angular maps, developed to improve the flexibility and scalability of current…