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From the 1 of 34 linked papers with an AI index.

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33 papers

astro-ph.CO2026

The Dark Energy Camera All Data Everywhere cosmic shear project V: Constraints on cosmology and astrophysics from 270 million galaxies across 13,000 deg of the sky

D. Anbajagane, C. Chang, A. Drlica-Wagner +78

The paper presents cosmological and astrophysical constraints derived from cosmic shear measurements of 270 million galaxies covering 13,000 deg², combining data from the DECADE pr…

astro-ph.CO2026

Dark Energy Survey Year 6 Results: Redshift Calibration of the Weak Lensing Source Galaxies

B. Yin, A. Amon, A. Campos +95

Determining the distribution of redshifts for galaxies in wide-field photometric surveys is essential for robust cosmological studies of weak gravitational lensing. We present the…

astro-ph.CO2026

Constraints on Dynamical Dark Energy from Multiple Probes in the Full Dark Energy Survey

DES Collaboration, T. M. C. Abbott, M. Adamow +166

We present results on dark energy evolution, assuming a time-dependent equation of state , from growth and geometric probes using the full six-year Dark Energy S…

astro-ph.GA2026

DELVE Milky Way Satellite Galaxy Census I: Satellite Population and Survey Selection Function in DES, DELVE, and Pan-STARRS

C. Y. Tan, A. Drlica-Wagner, A. B. Pace +84

The properties of Milky Way satellite galaxies have important implications for galaxy formation, reionization, and the fundamental physics of dark matter. However, the population o…

astro-ph.CO2026

Dark Energy Survey Year 6 Results: Clustering-redshifts and importance sampling of Self-Organised-Maps realizations for pt samples

W. d'Assignies, G. M. Bernstein, B. Yin +92

This work is part of a series establishing the redshift framework for the pt analysis of the Dark Energy Survey Year 6 (DES Y6). For DES Y6, photometric redshift distribu…

astro-ph.CO2026

Dark Energy Survey Year 3 results: Simulation-based CDM inference from weak lensing and galaxy clustering maps with deep learning: Analysis design

A. Thomsen, J. Bucko, T. Kacprzak +99

Data-driven approaches using deep learning are emerging as powerful techniques to extract non-Gaussian information from cosmological large-scale structure. This work presents the f…