higgs-boson

Website for Particle Physics Domain (UCSD Capstone)

This is a reoository with the code created for a course on Advanced machine learning in physics. The project was based on the Higgs ML challenge from 2012.

Local Gradio app for Higgs Audio v3 TTS — expression console, voice cloning, fully local on your own GPU.

Book Particle Physics by Christoph Berger and Gregor Herten

FAIR AI model (interaction network) for identifying H(bb) jets at the LHC

Heavily modified version of GABE C++ code for paper https://arxiv.org/abs/2007.10978. Solves coupled differential equations for early universe reheating on finite spatial lattice. Plus helpful mathematica noteboks (made by me).

Code for the Higgs Boson Machine Learning Challenge organised by CERN & EPFL

The goal of the project is to classify an event produced in the particle accelerator as background or signal. A background event is explained by the existing theories and previous observations. A signal event, however, indicates a process that cannot be described by previous observations and leads to the potential discovery of a new particle.

GPU-based ML to classify Higgs boson signal from background in particle physics using RAPIDS framework

🔭 📈 Supervised Machine Learning techniques used to categorise Higgs boson events using data collected from the Large Hadron Collider, CERN.

A collection of deep learning exercises collected while completing an Intro to Deep Learning course. We use TensorFlow and Keras to build and train neural networks for structured data.

Supervised classification algorithms employed to explore and identify Higgs bosons from particle collisions, like the ones produced in the ​Large Hadron Collider​. HIGGS dataset is used.​.

A PyROOT-based analysis of the Higgs boson discovery channel (H -> ZZ* -> 4l) using ATLAS 13 TeV Open Data.

Training Higgs Dataset with Keras - https://doi.org/10.5281/zenodo.13133945

Analysis of Higgs boson production mechanisms (ggF, VBF, WH, ZH) using MadGraph simulations and ATLAS Open Data.

Adaptation of adversarial techniques from https://arxiv.org/pdf/1703.03507 for Higgs physics

Machine learning pipeline for classifying Higgs boson signal vs. background events in CERN proton–proton collision data using stacked ensemble models (XGBoost, LightGBM, CatBoost).

Smart pipeline to perform TTS/TTT/STS/STT functionalities as a router

Analysis software for resonant Higgs boson pair production decaying to bottom quark anti-quark pairs in LHC Run II. https://cds.cern.ch/record/2141024/files/HIG-16-002-pas.pdf

Repository for 2020/2021 Physics MSci project using TensorFlow to construct machine learning algorithms for detecting invisible Higgs Boson decays at the CMS detector (LHC) CERN.