Small-scale signatures of primordial non-Gaussianity in k-Nearest Neighbour cumulative distribution functions
William R. Coulton, Tom Abel, Arka Banerjee

TL;DR
This study investigates the use of k-Nearest Neighbor cumulative distribution functions (kNN-CDFs) to detect primordial non-Gaussianity in cosmological simulations, revealing distinct responses for certain halo masses and potential for analytical modeling.
Contribution
It introduces the application of kNN-CDFs to cosmological data for primordial non-Gaussianity detection and demonstrates their sensitivity and analytical modeling within the halo occupation distribution framework.
Findings
kNN-CDFs respond distinctly to equilateral primordial non-Gaussianity for halos with mass < 10^{14} M_sun/h.
The responses are consistent in redshift space and can be distinguished from galaxy modeling effects.
Results can be modeled analytically via counts-in-cells mapping.
Abstract
Searches for primordial non-Gaussianity in cosmological perturbations are a key means of revealing novel primordial physics. However, robustly extracting signatures of primordial non-Gaussianity from non-linear scales of the late-time Universe is an open problem. In this paper, we apply k-Nearest Neighbor cumulative distribution functions, kNN-CDFs, to the \textsc{quijote-png} simulations to explore the sensitivity of kNN-CDFs to primordial non-Gaussianity. An interesting result is that for halo samples with M/h, the kNN-CDFs respond to \textit{equilateral} PNG in a manner distinct from the other parameters. This persists in the galaxy catalogs in redshift space and can be differentiated from the impact of galaxy modelling, at least within the halo occupation distribution (HOD) framework considered here. kNN-CDFs are related to counts-in-cells and, through mapping…
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Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · demographic modeling and climate adaptation · Stochastic processes and financial applications
