Extracting intrinsic alignments in the Dark Energy Survey’s year 1 data, using the self-calibration method and LSST-DESC tools
Year: 2026
Type: article
Abstract: 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) Dark Energy Science Collaboration (DESC)’s weak lensing 3x2pt pipeline (TXPipe). As a demonstration, we have run this pipeline on the Dark Energy Survey (DES) year one data set. We find indications of a non-zero intrinsic alignment signal in the higher redshift bins, while in the lower bins our results look more uncertain. We believe this is caused by known issues with the individual galaxies photo-z estimation. This effect is particularly harmful for the self-calibration method, since it has high requirements for reliable estimation of the photo-zs, and the need for individual galaxy point estimates and tomographic binning to match. We show how different methods of recreating the redshift probability distribution can affect the detection of intrinsic alignment.
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,046
Funders: National Science Foundation, U.S. Department of Energy, University of Pennsylvania, Ohio State University, University of Chicago, University of Portsmouth, UK Research and Innovation, University College London, University of Sussex, Deutsche Forschungsgemeinschaft, Ministério da Ciência, Tecnologia e Inovação, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Financiadora de Estudos e Projetos, Institut de Física d'Altes Energies, University of Illinois at Urbana-Champaign, Office of Science, Science and Technology Facilities Council, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas, Integrated Electronics Engineering Center, Binghamton University, Argonne National Laboratory, Fermilab, Lawrence Berkeley National Laboratory, SLAC National Accelerator Laboratory
Awards: Galaxy Intrinsic Alignments for LSST with the Multi-Estimator Method, RAISE: ADAPT : Novel AI/ML methods to derive CMB temperature and polarization power spectra from uncleaned maps, IMPLEMENTING, INTEGRATING AND APPLYING CODES AND METHODS FOR DARK ENERGY PARAMETER CONSTRAINT ANALYSIS INCLUDING INTRINSIC ALIGNMENT SYSTEMATIC MITIGATION FOR DARK ENERGY SURVEYS, G8318672306, RESEARCH IN HIGH ENERGY PHYSICS
OpenAlex ID: W7124430361