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Information Content of Higher-Order Galaxy Correlation Functions

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arxiv 2102.01696 v1 pith:XYATRRY5 submitted 2021-02-02 astro-ph.CO

Information Content of Higher-Order Galaxy Correlation Functions

classification astro-ph.CO
keywords correlationfieldfunctionsgalaxypowerspectrumhigher-orderinitial
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The shapes of galaxy N-point correlation functions can be used as standard rulers to constrain the distance-redshift relationship and thence the expansion rate of the Universe. The cosmological density fields traced by late-time galaxy formation are initially nearly Gaussian, and hence all the cosmological information can be extracted from their 2-Point Correlation Function (2PCF) or its Fourier-space analog the power spectrum. Subsequent nonlinear evolution under gravity, as well as halo and then galaxy formation, generate higher-order correlation functions. Since the mapping of the initial to the final density field is, on large scales, invertible, it is often claimed that the information content of the initial field's power spectrum is equal to that of all the higher-order functions of the final, nonlinear field. This claim implies that reconstruction of the initial density field from the nonlinear field renders analysis of higher-order correlation functions of the latter superfluous. We here show that this claim is false when the N-point functions are used as standard rulers. Constraints available from joint analysis of the galaxy power spectrum and bispectrum (Fourier-space analog of the 3-Point Correlation Function) can, in some cases, exceed those offered by the initial power spectrum even when the reconstruction is perfect. We provide a mathematical justification for this claim and also demonstrate it using a large suite of N-body simulations. In particular, we show that for the z = 0 real-space matter field in the limit of vanishing shot noise, taking modes up to k_max = 0.2 h/Mpc, using the bispectrum alone offers a factor of two reduction in the variance on the cosmic distance scale relative to that available from the power spectrum.

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Cited by 2 Pith papers

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  1. Hermes - Towards an Optimal High-Performance Algorithm for Cosmic Statistics of Large Data Sets

    astro-ph.CO 2026-07 conditional novelty 6.0

    Hermes/PyHermes reconstructs catalogues in a scaling-function basis and unifies CIC, 2PCF, 3PCF, marked, and operator-based cosmic statistics as reusable window operations with FFT/MPI/GPU scaling.

  2. Learning Cosmology from Nearest Neighbour Statistics

    astro-ph.CO 2025-11 conditional novelty 6.0

    Nearest-neighbour distance maps, combined with kNN-CDFs in a hybrid neural network, constrain Ωm and σ8 from Quijote halos with R2=0.80 and 0.93, matching or beating point-cloud methods at a fraction of the compute.