collaborators

30 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

Density-Shear Baryon Acoustic Oscillation as a Cosmological Consistency Check

Kwan Chuen Chan, Yin Li, Jamie McCullough

Tensions often arise between different datasets in cosmology, and consistency tests can serve as a powerful tool for diagnosing potential issues. Density-shear Baryon Acoustic Osci…

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

Brightest Cluster Galaxy ellipticity as proxy for halo shape: Orientation bias, assembly bias, and potential selection effects in SZ-selected clusters

Radhakrishnan Srinivasan, Tae-hyeon Shin, Anja von der Linden +93

The orientation of triaxial galaxy clusters with respect to the line-of-sight is expected to be one of the prime sources of scatter and potential bias in optical observables (e.g.,…

astro-ph.CO2026

Data Release 1 of the Dark Energy Spectroscopic Instrument

DESI Collaboration, M. Abdul Karim, A. G. Adame +306

In 2021 May the Dark Energy Spectroscopic Instrument (DESI) collaboration began a 5-year spectroscopic redshift survey to produce a detailed map of the evolving three-dimensional s…

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…