Quijote-PNG: The Information Content of the Halo Mass Function

Abstract

We study signatures of primordial non-Gaussianity (PNG) in the redshift-space halo field on nonlinear scales using a combination of three summary statistics, namely, the halo mass function (HMF), power spectrum, and bispectrum. The choice of adding the HMF to our previous joint analysis of the power spectrum and bispectrum is driven by a preliminary field-level analysis, in which we train graph neural networks on halo catalogs to infer the PNG f NL parameter. The covariance matrix and the responses of our summaries to changes in model parameters are extracted from a suite of halo catalogs constructed from the Quijote-png N-body simulations. We consider the three main types of PNG: local, equilateral, and orthogonal. Adding the HMF to our previous joint analysis of the power spectrum and bispectrum produces two main effects. First, it reduces the equilateral f NL predicted errors by roughly a factor of 2 while also producing notable, although smaller, improvements for orthogonal PNG. Second, it helps break the degeneracy between the local PNG amplitude, f NL local , and assembly bias, b ϕ , without relying on any external prior assumption. Our final forecasts for the PNG parameters are Δ f NL local = 40 , Δ f NL equil = 200 , Δ f NL ortho = 85 , on a cubic volume of 1 Gpc / h 3 , with a halo number density of n ¯ ∼ 5.1 × 10 − 5 h 3 Mpc − 3 , at z = 1, and considering scales up to k max = 0.5 h Mpc − 1 .

Primordial non-Gaussianities with weak lensing: information on non-linear scales in the Ulagam full-sky simulations

“…For both orthogonal-type f NL we once again find that the sign of the change is flipped at redshift z = 0, for halos with M ≈ 10 14 M ⊙ , as their abundance increases with f NL now. The redshift-and mass-dependence of the sign flip are consistent with findings in [59]. This has also been analytically derived in [95, see their section 4.2] and is due to the fact that for a fixed matter energy density, increasing f NL causes more massive halos to form at the expense of less matter available to form lower mass systems.…”

“…A significant concern in numerical estimates of the Fisher information is the artificial amplification of the information due to numerical noise. This has been documented in previous studies with the Quijote suite [e.g., 41,59]. A simple test of this effect is to vary the number of simulations used in computing either the derivatives or the covariance matrix as used in equation (4.1).…”

Taming assembly bias for primordial non-Gaussianity

“…Recognizing these departures from universality paved the way for further studies in various directions. In particular, the theoretical uncertainties called for new efforts to provide better priors on b φ with the aid of machine learning techniques [30,34], or to break the b φ f NL degeneracy introducing new summary statistics [35]. Furthermore, since samples with different b φ represent an opportunity to employ the multi-tracer technique [36] and provide better f NL constraints, some analyses focused on optimizing sample selection strategies [29,30,37].…”

Constraining primordial non-Gaussianity from large scale structure with the wavelet scattering transform

“…With such goals in mind, several NG summaries have already been considered in LSS analysis. Among those, let us mention for example marked spectra [6][7][8][9][10], power spectra from cosmic web environments [11,12], skew spectra [13][14][15][16], Minkowski functionals [17,18], k-nearest neighbour cumulative distributions functions [19,20], probability distribution function (PDF) of late-time cosmic density fluctuations [21,22], halo mass function probes [23][24][25][26][27][28], and the void abundance [29,30].…”