Code for Bayesian Analysis

DeepSphere: a graph-based spherical CNN (TensorFlow)

Python code for learning cosmology using different methods and mock data

DerivKit - a robust Python toolkit for stable numerical derivatives

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.

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 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.

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.

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

Foundational version of The Siamese Big Bang, introducing a directional CPT-symmetric topology inferred from CHIME/FRB data (N=100, DM≥800, |b|>20°). Shows morphological but marginal evidence (A≈90 pc cm⁻³, φ₀≈155°, p≈0.1) for a cosmological anisotropy along the Siamese axis.

Neural-Network Emulator for Reionization and Optical depth