PySM 3: Sky emission simulations for Cosmic Microwave Background experiments

Pynkowski is a Python package to compute Minkowski Functionals, as well as their expected values for different fields.

A conditional Wasserstein Generative Adversarial Network with gradient penalty (cWGAN-GP) for stochastic generation of galaxy properties in wide-field surveys

Port of healpix routines to JAX

Set of functions to help to project healpix maps into a flat 2D grid

A beginner's attempt to visualization astronomical data

Neural network layers and networks to study the topology of the universe. Based on graph convolutions and written in the jax environment

Box topology pixelization for cosmology

A Hemispherical Comparison code using HEALPiX and Kolmogorov–Smirnov test for probing isotropy

Analysing and selection of PGCC's and ISM

Algorithm for planning observations of the LIGO-Virgo-KAGRA localization region

A tensor network simulation of a discrete 3D triclinic lattice cosmology verified with raw CMB satellite data.

Analysis of Cosmic Microwave Background Maps Using HEALPix, NaMaster, and Planck Data - EP491 UG Project (IIT BHU)

Python pipeline for cosmic web analysis using SDSS DR18 — HEALPix mapping, TDA persistent homology, Information Bottleneck efficiency (η_IB), and T-Web classification of voids, sheets, filaments, and knots.

Deep learning for modeling the detection probability of galaxies in wide-field surveys

This project generates the largest and most detailed dark matter map from the Cosmic Microwave Background (CMB) using data from the Atacama Cosmology Telescope (ACT). It leverages advanced gravitational lensing reconstruction algorithms and high-performance computing to visualize the invisible mass distribution across the universe.