Cosmological information in skew spectra of biased tracers in redshift space

Abstract

Extracting the non-Gaussian information encoded in the higher-order clustering statistics of the large-scale structure is key to fully realizing the potential of upcoming galaxy surveys. We investigate the information content of the redshift-space weighted skew spectra of biased tracers as efficient estimators for 3-point clustering statistics. The skew spectra are constructed by correlating the observed galaxy field with an appropriately-weighted square of it. We perform numerical Fisher forecasts using two synthetic datasets: the halo catalogs from the Quijote N-body simulations and the galaxy catalogs from the Molino suite. The latter serves to understand the effect of marginalization over a more complex matter-tracer biasing relation. Compared to the power spectrum multipoles, we show that the skew spectra substantially improve the constraints on six parameters of the νΛCDM model, {Ω m , Ω b , h, ns , σ8 , Mν }. Imposing a small-scale cutoff of kmax = 0.25 Mpc-1 h, the improvements in parameter constraints from skew spectra alone range from 23% to 62% for the Quijote halos and from 32% to 71% for the Molino galaxies. Compared to the previous analysis of the bispectrum monopole on the same data and using the same range of scales, the skew spectra of Quijote halos provide competitive constraints. At the same time, the skew spectra outperform the bispectrum monopole for all cosmological parameters for the Molino catalogs. This may result from additional anisotropic information, particularly enhanced in the Molino sample, that is captured by the skew spectra but not by the bispectrum monopole. Our stability analysis of the numerical derivatives shows comparable convergence rates for the power spectrum and the skew spectra, indicating potential underestimation of parameter uncertainties by at most 30%.

Probing massive neutrinos with the Minkowski functionals of the galaxy distribution

“…We note the combination of the MFs and power spectrum only slightly improves the M ν constraint from the MFs alone, by 2% (from 0.059 to 0.058 eV). This finding is consistent with other works based on the Molino mock catalogs: for the bispectrum monopole [46], marked power spectrum [47], and skew spectra [74], when combined with the power spectrum, the M ν constraint from the statistics alone is improved by 3%, 9%, and 8%, respectively. This may indicate the M ν constraint is dominated by information from higher order statistics.…”

“…The Molino catalogs have been used to quantify information content embedded in the power spectrum and bispectrum [46], the combination of power spectra measured from the pre-and post-reconstructed galaxy samples [73], the marked power spectra of the galaxy field that up-weight low-density regions [47], and the weighted skew spectra [74]. In section 7.1, we directly compare our results with those obtained with the bispectrum and marked power spectra, and in section 7.2, we will present a comparison with the constraints from the cold dark matter obtained in our previous work [38].…”

“…The size of each bin is 2π/L, where L = 1h −1 Gpc is the size of the simulation box. We take the same choice of k max as previous works that used the Molino catalogs for Fisher matrix forecasts [46,47,73,74], hence the constraints from the power spectrum may serve as a benchmark for the comparison of the MFs with other statistics.…”

“…However, the Gaussian process smoothing can be so aggressive that important physical signatures may be smeared out [79]. In addition, the GP smoothing may artificially reduce the parameter constraints for statistics with a complex non-diagonal covariance matrix [74]. It may be the same case for the MFs since the covariance of the MFs has a complex structure as well.…”

SimBIG: mock challenge for a forward modeling approach to galaxy clustering

“…Similar forecasts made using other higher-order statistics, e.g. the marked power spectrum [44,45], the reconstructed power spectrum [46], the skew spectra [47], wavelet statistics [48], find consistent improvements over the power spectrum. Despite the advantages that these observables promise, the limitations above prevent most of them from being analyzed within the standard galaxy clustering analysis framework.…”