Hybrid-basis inference for large-scale galaxy clustering: combining spherical and Cartesian Fourier analyses

Mike Shengbo Wang, Santiago Avila, Davide Bianchi, Robert Crittenden, Will J. Percival

Research output: Contribution to journalArticlepeer-review

1 Downloads (Pure)

Abstract

Future precision cosmology from large-scale structure experiments including the Dark Energy Spectroscopic Instrument (DESI) and Euclid will probe wider and deeper cosmic volumes than those covered by previous surveys. The Cartesian power spectrum analysis of anisotropic galaxy clustering based on the Fourier plane wave basis makes a number of assumptions, including the local plane-parallel approximation, that will no longer be valid on very large scales and may degrade cosmological constraints. We propose an approach that utilises a hybrid basis: on the largest scales, clustering statistics are decomposed into spherical Fourier modes which respect the natural geometry of both survey observations and physical effects along the line of sight, such as redshift-space distortions, the Alcock-Paczyńsky and light-cone effects; on smaller scales with far more clustering modes, we retain the computational benefit of the power spectrum analysis aided by fast Fourier transforms. This approach is particularly suited to the likelihood analysis of local primordial non-Gaussianity ƒNL through the scale-dependent halo bias, and we demonstrate its applicability with N-body simulations. We also release our public code HARMONIA for galaxy clustering likelihood inference in spherical Fourier or hybrid-basis analyses.
Original languageEnglish
Article number22
Number of pages39
JournalJournal of Cosmology and Astroparticle Physics
Volume2020
Issue number10
DOIs
Publication statusPublished - 8 Oct 2020

Keywords

  • astro-ph.CO
  • cosmological parameters from LSS
  • galaxy clustering
  • power spectrum
  • redshift surveys
  • RCUK
  • STFC
  • ST/S000550/1

Fingerprint

Dive into the research topics of 'Hybrid-basis inference for large-scale galaxy clustering: combining spherical and Cartesian Fourier analyses'. Together they form a unique fingerprint.

Cite this