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The Bispectrum of Intrinsic Alignments: II. Precision Comparison Against Dark Matter Simulations

Title: The Bispectrum of Intrinsic Alignments: II. Precision Comparison Against Dark Matter Simulations
Authors: Bakx, Thomas; Kurita, Toshiki; Eggemeier, Alexander; Chisari, Nora Elisa; Vlah, Zvonimir; Sub String Theory Cosmology and ElemPart; Theoretical Physics
Publication Year: 2026
Subject Terms: Astronomy and Astrophysics
Description: We measure three-dimensional bispectra of halo intrinsic alignments (IA) and dark matter overdensities in real space from N-body simulations for halos of mass 1012 − 1012.5 M⊙ /h. We show that their multipoles with respect to the line of sight can be accurately described by a tree-level perturbation theory model on large scales (k ≲ 0.11 h/Mpc) at z = 0. For these scales and in a simulation volume of 1 (Gpc/h)3, we detect the bispectrum monopole BδδE00 at SNR ∼ 30 and the two quadrupoles B11 δδE and BδδE20 at SNR ∼ 25 and SNR ∼ 15, respectively. We also report similar SNR for the lowest order multipoles of BδEE and BEEE, although these are largely driven by stochastic contributions. We show that the first and second order EFT parameters are consistent with those obtained from fitting the IA power spectrum analysis at next-to-leading order, without requiring any priors to break degeneracies for the quadratic bias parameters. Moreover, the inclusion of higher multipole moments of BδδE greatly reduces the errors on second order bias parameters, by factors of 5 or more. The IA bispectrum thus provides an effective means of determining higher order shape bias parameters, thereby characterizing the scale dependence of the IA signal. We also detect parity-odd bispectra such as BδδB and BδEB at ∼ 10σ significance or more for k < 0.15 h/Mpc and they are consistent with the parity-even sector. Furthermore, we check that the Gaussian covariance approximation works reason-ably well on the scales we consider here. These results lay the groundwork for using the bispectrum of IA in cosmological analyses.
Document Type: article in journal/newspaper
File Description: application/pdf
Language: English
ISSN: 2565-6120
Relation: https://dspace.library.uu.nl/handle/1874/480800
Availability: https://dspace.library.uu.nl/handle/1874/480800
Rights: info:eu-repo/semantics/OpenAccess
Accession Number: edsbas.72A761B
Database: BASE