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.
Augmented halo model for accurate non-linear matter power spectrum calculations
Python code to interface with halo catalogs and other Abacus N-body data products
A pure Python halo-model implementation for power spectra of any large-scale structure tracer combination.
A pure python implementation of HMcode
Simulator Expansion for Likelihood-Free Inference (SELFI): a python implementation
Full-Shape Power Spectrum and Bispectrum Likelihoods
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
Code for leveraging Information Maximising Neural Networks for optimal cosmological field compression and Bayesian inference for cosmological parameters
Cosmology Group repo for its public webpage, reviews & publications on tensions in ΛCDM, redshift and the CMB
A semi-numerical code to generate the Epoch of Reionization (EoR) neutral Hydrogen (HI) field.
Guide to architect large scale application in Angular
Python code to create a 3D cosmological particle-mesh nbody simulation. Supports parallel computing via Numba/pyFFTW.
interactive cosmological power spectra 🔭 🌎 🛰️ 🚀
LearnIt! is an e-learning social network designed with two different databases: MongoDB (in a cluster of three nodes) and Neo4J
A nonparametric statistics based method for hub and co-expression module identification in large gene co-expression network
BAO Reconstruction code in Julia
A python module to implement the halo mass corrections based on Illustris, IllustrisTNG, and EAGLE given in Beltz-Mohrmann et al. (2020).
Cleaned repository focusing on running RascalC library for semi-analytical galaxy 2-point correlation function covariance matrices
A large scale structure void identifier for galaxy surveys based on the Beta-Skeleton graph