2026/01/12 by Saee Dhawalikar, Shadab Alam, Aseem Paranjape +1
Physics and Astronomy · #Astronomy and Astrophysical Research #Cosmology #Cosmology and Gravitation Theories #Dark matter #Galaxies: Formation, Evolution, Phenomena #Halo #Halo mass function #Matter power spectrum #Planck #Redshift #Universe #astro-ph.CO
paper · pdf · doi:10.1088/1475-7516/2026/07/028
published in Journal of Cosmology and Astroparticle Physics 2026(07), 028 (Institute of Physics)
openalex publication_date 2026/07/01 · openalex created_date 2026/07/09 · openalex updated_date 2026/08/05
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 four cosmological parameters around Planck 2018 constraints, with seed-matched initial conditions enabling cosmological parameter derivatives. It is planned to ultimately extend to six parameters. Each simulation evolves 2048 3 particles in a periodic box of side length 200 h -1 Mpc, yielding a particle mass of m p = 8.1 × 10 7 h -1 M ⊙ in the fiducial Planck 2018 cosmology. This resolution enables robust identification of dark matter halos down to M min = 3.2 × 10 9 h -1 M ⊙ , which represents a factor of ∼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 th nearest neighbour statistics, showing excellent agreement with theoretical predictions and significant sensitivity to Ω m variations. We implement a custom compression scheme reducing storage requirements by a factor of ∼3 while maintaining sub-percent clustering accuracy. Key data products will be made publicly available.