5 papers
Smokescreen: A Python package for data vector blinding and encryption in cosmological analyses
Arthur Loureiro, Jessica Muir, Jonathan Blazek +10
Smokescreen is an open-source Python library for data-vector concealment (blinding) in cosmological analyses. Data-vector blinding works by applying cosmology-dependent shifts to t…
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…
Extracting intrinsic alignments in the Dark Energy Survey's year 1 data, using the self-calibration method and LSST-DESC tools
Eske M. Pedersen, Leonel Medina-Varela, Emily Phillips Longley +4
We present the implementation of a Self-Calibration of Intrinsic Alignments of galaxies as an extension to the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) Da…
Forecasting the Impact of Source Galaxy Photometric Redshift Uncertainties on the LSST pt Analysis
Tianqing Zhang, Husni Almoubayyed, Rachel Mandelbaum +7
Photometric redshifts of the source galaxies are a key source of systematic uncertainty in the Rubin Observatory Legacy Survey of Space and Time (LSST)'s galaxy clustering and weak…
Meta-learning for cosmological emulation: Rapid adaptation to new lensing kernels
Charlie MacMahon-Gellér, C. Danielle Leonard, Philip Bull +1
Theoretical computation of cosmological observables is an intensive process, restricting the speed at which cosmological data can be analysed and cosmological models constrained, a…