Sahyadri: A simulation suite for the cosmology dependence of the Cosmic Web

TL;DR

Sahyadri is a high-resolution cosmological simulation suite with systematic parameter variations, enabling precise studies of the low-redshift Universe and improving on existing suites in resolution and parameter coverage.

Contribution

It introduces Sahyadri, a novel simulation suite with enhanced resolution and parameter variation capabilities for cosmological research, surpassing prior simulation sets.

Findings

Accurately measures matter power spectrum and halo mass function.

Demonstrates sensitivity of statistics to cosmological parameters.

Achieves high-resolution identification of dark matter halos.

Abstract

We present Sahyadri, a suite of cosmological $N$-body simulations designed to enable precision studies of the low-redshift Universe with next-generation spectroscopic surveys. Sahyadri includes systematic variations of six cosmological parameters around Planck 2018 constraints, with seed-matched initial conditions enabling cosmological parameter derivatives. Each simulation evolves $2048^3$ particles in a periodic box of side length $200$ $h^{-1}$ Mpc, yielding a particle mass of $m_{\rm{p}} = 8.1 \times 10^{7}\,h^{-1}\,M_{\odot}$ in the fiducial Planck 2018 cosmology. This resolution enables robust identification of dark matter halos down to $M_{\rm min} = 3.2 \times 10^{9}$ $h^{-1}$ $M_\odot$, which represents a factor of $\sim$25 improvement over the AbacusSummit suite, and is over two orders of magnitude better than the Quijote and Aemulus suites. We estimate that approximately 40% of DESI BGS galaxies at redshift $z < 0.15$ - roughly 1.6 million objects - reside in halos accessible to Sahyadri but beyond the reach of existing parameter-varying simulation suites. We demonstrate Sahyadri's capabilities through measurements of the matter power spectrum, halo mass function and power spectrum, and beyond 2-point statistics such as the Voronoi volume function and $k^{\rm th}$ nearest neighbour statistics, showing excellent agreement with theoretical predictions and significant sensitivity to $\Omega_{\rm m}$ variations. We implement a custom compression scheme reducing storage requirements by a factor of $\sim$3 while maintaining sub-percent clustering accuracy. Key data products will be made publicly available.