A Deep learning library for neutrino telescopes

Scalable Particle Imaging with Neural Embeddings

Tree-level completions of LNV operators for neutrino-mass model building

Geometric Information Field Theory. 33 SM predictions from pure topology. 0.24% mean deviation. Zero free parameters. open source, Lean 4 verified, falsifiable.

Application of ML for Neutrino Physics experiment LEGEND-200

Cosmological applications of the SymC framework. Applies χ ≈ 1 stability principles to universe-scale phenomena including cosmic acceleration onset, black hole thermodynamics, and cyclical cosmology. Demonstrates framework consistency from quantum boundaries to cosmic structure.Retry

An open source machine learning framework that provides predictions for all-energy neutrino structure functions.

Tensor based engine for calculating neutrino oscillation probabilities in a fast, flexible, and differentiable way

Fast & accurate three-flavor neutrino oscillation probabilities in Rust/Zig. Port of NuFast by Denton & Parke. ~60ns vacuum, ~95ns matter.

Python-based charge propagation model for gaseous particle detectors

Convolutional networks (and CapsNET) for SuperNEMO tracker

Empirical observation: Standard Model flavor mixing parameters (CKM, PMNS, Weinberg angle) expressed using simple fractions with 3, 11, and 13. Falsifiable predictions for JUNO/DUNE.

Reconstruction library for a 3D LiquidO detector

Monte Carlo based generator of neutrino induced dimuon events. Simulates muons from heavy quark (charm) production and Trident production. Container with all software dependencies provided for future developments.