🚀 Deep Learning for Top Quark Tagging – New Accepted Solution

[robertogl]

🎉 New Accepted Solution: Top Quark Detection with Deep Learning and Big Data

Submitted by from .

approaches this challenge by developing a deep convolutional neural network (CNN) capable of distinguishing top quark decay signatures from background events in particle collision data. This project demonstrates how MATLAB’s Deep Learning Toolbox can be applied to high-energy physics datasets, leveraging the efficiency of GPU computation and data preprocessing workflows to achieve precise classification of particle events.

Using simulated collider datasets, constructs a CNN architecture that automatically extracts discriminative features from jet images representing energy depositions. The workflow integrates MATLAB’s datastore functionality to handle large-scale data efficiently and applies advanced visualization tools to interpret model performance. The training and validation process highlight the model’s accuracy and robustness, showing strong capability to identify top quark events compared to background noise.

In addition, documents each step with clarity, from dataset preparation to hyperparameter tuning and evaluation. The repository provides reproducible scripts and insightful plots, reflecting ’s deep understanding of both the physics and machine learning aspects of the challenge. Congratulations to for producing a technically impressive and well-documented contribution!

Important

Why it was accepted: Exceptional clarity, well-structured workflow, and successful demonstration of deep learning applied to particle physics classification with MATLAB.

Tip

Technical highlights:

• Use of Deep Convolutional Neural Networks for jet image classification.
• Efficient handling of large collider datasets using MATLAB datastores.
• Detailed visualization of confusion matrices and training progress.
• Integration of GPU acceleration for faster training.
• Strong documentation ensuring full reproducibility.

At a glance

• Challenge: Detecting top quark events using deep learning and big data.
• Dataset/Models: Jet images from simulated collider experiments processed as 2D inputs to CNN.
• Toolboxes/Methods: Deep Learning Toolbox, Image Processing Toolbox, GPU Coder (optional).
• Key results: Achieved high classification accuracy with clear feature separability.
• Deployment: MATLAB scripts ready for MATLAB Online and local GPU execution.

Accepted to the MATLAB & Simulink Challenge Project Hub. Congratulations again to from for their remarkable contribution!