neutrino-physics

A Deep learning library for neutrino telescopes

Scalable Particle Imaging with Neural Embeddings

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

K₇ (formerly GIFT), the founding framework of the Arithmon program. Standard Model parameters as topological invariants of a G₂ manifold. Zero free parameters, formally verified, falsifiable.

Python toolkit for CEvNS calculations in an effective field theory framework, associated with arXiv:2409.04703 and arXiv:2601.19883. Includes event-rate calculations, detector responses, EFT matching, and statistical analyses for COHERENT.

Application of ML for Neutrino Physics experiment LEGEND-200

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

C++/ROOT data analysis pipeline for characterizing the cosmic muon flux during the filling phase of the JUNO Central Detector.

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

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

An analysis framework to carry out BDT-based reweighting between neutrino event samples in the form of Nuisance FlatTrees.

Master thesis preparation project on reducing simulation-to-data discrepancies in IceCube using transformer-based event classification, direction reconstruction, and GBDT reweighting.

To explain the origin of Neutrino Mass and their huge separation from charged leptons ( 10 6 times lighter).

Convolutional networks (and CapsNET) for SuperNEMO tracker

Deep-learning classification of simulated NOvA neutrino detector images. Three CNN architectures compared; a multi-view CNN reaches 84% accuracy (macro F1 0.82) with ~100x fewer parameters.

UM Neutrino website

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.

Python-based charge propagation model for gaseous particle detectors

A modular CAFAna/SBNAna-based selection framework for the BNB CC1eNp0pi channel in ICARUS, supporting both Pandora (legacy) and NuGraph2 reconstruction workflows for the ICARUS experiment.

A quantum computing implementation of two- and three-flavor neutrino oscillation physics, including CP violation and MSW matter effects, validated against published literature and executed on real IBM Quantum hardware.