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

Classifying whether the given event was a signal or a background noise in the process of decay for Higgs particle acceleration.

Basic exploration of Higgs boson data

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

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.

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

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.

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

Website for Particle Physics Domain (UCSD Capstone)

Machine Learning Project for the course Machine Learning at EPFL.

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

This is a report on what are the things that I have learned from the Kaggle course intro to deep learning.

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.

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.

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).

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

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

Capstone project for Udacity Machine Learning Nanodegree