39 citations · 79 across the 12 of their papers we have counts for
12 papers
Fisher Forecasting for the DESC with
Paul Rogozenski, Sankarshana Srinivasan, Javier Sánchez +15
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) has begun its ten-year survey of the entire visible southern hemisphere. To ensure robust cosmological measurem…
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…
Impact and interplay of CDM analysis choices for LSST cosmic shear
N. C. Robertson, C. Heymans, J. Zuntz +9
We forecast cosmological parameter constraints for a cosmic shear analysis of the Rubin Observatory Legacy Survey of Space and Time (LSST), defining an analysis framework that can…
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…
Joint Modelling of Astrophysical Systematics for Cosmology with LSST Cosmic Shear
Nikolina Šarčević, C. Danielle Leonard, Markus M. Rau +1
We present a novel framework for jointly modelling the weak lensing source galaxy redshift distribution and the intrinsic alignment of galaxies via a shared luminosity function (LF…
Photometric redshifts and intrinsic alignments: degeneracies and biases in 32pt analysis
C. Danielle Leonard, Markus Michael Rau, Rachel Mandelbaum
We present a systematic study of cosmological parameter bias in weak lensing and large-scale structure analyses for upcoming imaging surveys induced by the interplay of intrinsic a…
Accuracy requirements on intrinsic alignments for Stage-IV cosmic shear
Anya Paopiamsap, Natalia Porqueres, David Alonso +2
In the context of cosmological weak lensing studies, intrinsic alignments (IAs) are one the most complicated astrophysical systematic to model, given the poor understanding of the…
An Empirical Model For Intrinsic Alignments: Insights From Cosmological Simulations
Nicholas Van Alfen, Duncan Campbell, Jonathan Blazek +5
We extend current models of the halo occupation distribution (HOD) to include a flexible, empirical framework for the forward modeling of the intrinsic alignment (IA) of galaxies.…
Constraints on dark matter to dark radiation conversion in the late universe with DES-Y1 and external data
Angela Chen, Dragan Huterer, Sujeong Lee +81
We study a phenomenological class of models where dark matter converts to dark radiation in the low redshift epoch. This class of models, dubbed DMDR, characterizes the evolution o…