There are 3 repositories under large-scale-structure topic.
Large suite of N-body simulations
This is the public repository for the AbacusSummit suite, intended for specifications of the simulations and instructions for reading the files.
Python code to interface with halo catalogs and other Abacus N-body data products
Augmented halo model for accurate non-linear matter power spectrum calculations
A pure Python halo-model implementation for power spectra of any large-scale structure tracer combination.
A pure python implementation of HMcode
Full-Shape Power Spectrum and Bispectrum Likelihoods
Simulator Expansion for Likelihood-Free Inference (SELFI): a python implementation
Cosmology Group repo for its public webpage, reviews & publications on tensions in ΛCDM, redshift and the CMB
Harmonic-space statistics on the sphere
Code for leveraging Information Maximising Neural Networks for optimal cosmological field compression and Bayesian inference for cosmological parameters
Public release of data products following the BORG SDSS analysis
This is a short astro-physical program showing how to compute the adhesion model, describing the large-scale structure of the Universe, using regular triangulations in CGAL (www.cgal.org), as well as using the Convex Hull algorithm present in Python's Scipy.
Estimators and data for window-free analysis of power spectra and bispectra
A semi-numerical code to generate the Epoch of Reionization (EoR) neutral Hydrogen (HI) field.
Guide to architect large scale application in Angular
A python version of the CosmoMMF package originally written in Julia.
Python code to create a 3D cosmological particle-mesh nbody simulation. Supports parallel computing via Numba/pyFFTW.
interactive cosmological power spectra 🔭 🌎 🛰️ 🚀
A nonparametric statistics based method for hub and co-expression module identification in large gene co-expression network
LearnIt! is an e-learning social network designed with two different databases: MongoDB (in a cluster of three nodes) and Neo4J
Examples for the SelfiSys Python package.
SelfiSys: Assess the Impact of Systematic Effects in Galaxy Surveys.
Code to emulate galaxy power spectrum multipoles predicted by EFT
An efficient, parallelised C++ code to compute the two-point correlation function (2PCF) of gridded density fields directly in configuration space within minutes. The core library is wrapped in Python using SWIG, combining computational speed with user-friendly accessibility.
Systematic comparison of galaxies in cosmic voids versus dense "walls" using DESI DR1 data to investigate environmental quenching mechanisms in galaxy evolution.