Code for Bayesian Analysis

DeepSphere: a graph-based spherical CNN (TensorFlow)

DerivKit - a robust Python toolkit for stable numerical derivatives

Python code for learning cosmology using different methods and mock data

Flexible, fully bayesian stacking software for modelling of astronomical data sets

Correlation functions versus field-level inference in cosmology: example with log-normal fields

Repository containing tutorials about how to use Cobaya for cosmological inference at PhD Schools

CosmicFishPie: Python Fisher Matrix code for Cosmological probes

Mock CMB likelihood class for Cobaya sampler (https://github.com/CobayaSampler/cobaya), and several specific experiment examples

Main tools and results from arxiv:

Collection of Jupyter notebooks demonstrating statistical methods for cosmological data analysis, including Bayesian inference & basic frequentist tools

JAX-powered Hi-Fi mocks

Interactive exploration of equivariant neural networks on homogeneous spaces, with a focus on the sphere S² as SO(3)/SO(2). From Lecture 8 of the Lie groups course with Quantum Formalism

A Bayesian Python code to confront the quasar data set with models beyond the standard model of elementary particle physics and models beyond the $\Lambda$CDM standard cosmology.

This essay presents a clear and intuitive overview of the full Siamese Universe framework. It explains how a shared quantum vacuum, a minimal phase desynchronization, and CPT symmetry together generate matter, structure, and cosmic complexity. A causal narrative linking physical principles and observable signatures.

The universe may operate as a self-executing algorithm where structure precedes matter. Reality’s “errors”—from cosmic anomalies to quantum correlations—are reflections of its code. Through holography, recursion, and informational self-replication, the cosmos continuously rewrites its own laws.

Testing a CPT-symmetric twin-universe framework where a slight phase desynchronization (≈5%) between Siamese universes generates the observed matter–antimatter asymmetry. Includes numerical scans, CMB–FRB anisotropy tests, and reproducible data analysis scripts.

The codes for computing the scale-dependent peak height function and the scale-dependent valley depth function of the cosmic-log density field.

Flexible, fully bayesian stacking software for modelling of astronomical data sets

Neural-Network Emulator for Reionization and Optical depth