An AICrowd Challenge: Logistic Regression classifier that predicts whether an event's decay signature was the one of a Higgs Boson
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Updated
Oct 22, 2022 - Jupyter Notebook
An AICrowd Challenge: Logistic Regression classifier that predicts whether an event's decay signature was the one of a Higgs Boson
Training Higgs Dataset with Keras - https://doi.org/10.5281/zenodo.13133945
Study of Higgs boson to tau-tau decay channel classification using shallow neural networks.
A 1st year statistics of measurements project for the undergraduate physics course at Imperial College London.
This is a report on what are the things that I have learned from the Kaggle course intro to deep learning.
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).
Basic exploration of Higgs boson data
Adaptation of adversarial techniques from https://arxiv.org/pdf/1703.03507 for Higgs physics
GPU-based ML to classify Higgs boson signal from background in particle physics using RAPIDS framework
Capstone project for Udacity Machine Learning Nanodegree
Classifying whether the given event was a signal or a background noise in the process of decay for Higgs particle acceleration.
Machine Learning Project for the course Machine Learning at EPFL.
Higgs transverse momentum distributions in Momentum and Mellin space.
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..
Higgs Boson Classification project for the Machine Learning course CS-433 at EPFL
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.
Code for the Higgs Boson Machine Learning Challenge organised by CERN & EPFL
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