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.

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.

🔭 📈 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.​.

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

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

An AICrowd Challenge: Logistic Regression classifier that predicts whether an event's decay signature was the one of a Higgs Boson

Study of Higgs boson to tau-tau decay channel classification using shallow neural networks.

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

Higgs Boson Classification project for the Machine Learning course CS-433 at EPFL

A 1st year statistics of measurements project for the undergraduate physics course at Imperial College London.

Higgs transverse momentum distributions in Momentum and Mellin space.

Machine Learning Project for the course Machine Learning at EPFL.

Basic exploration of Higgs boson data

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