⚛️ ACCEPTED! TOP QUARK TAGGING WITH DEEP CNN! 🏆

[robertogl]

🎉 A STUNNING SUBMISSION FROM ADIT-SMOAK!** 🚀🔬💡

We are absolutely thrilled to announce the acceptance of adit-smoak's groundbreaking submission, "Top-Quark-Tagging-Using-Deep-CNN," to our challenge project! This work is a premier example of applying state-of-the-art Deep Learning to solve a high-stakes problem in high-energy physics, marrying theoretical innovation with robust engineering.

Challenge project completed: Top Quark Detection with Deep Learning and Big Data

Tip

Standout results: Aggregated residual (ResNeXt-style) blocks + Squeeze-and-Excite; robust preprocessing; HDL/FPGA workflow outlined.

👏 HUGE CONGRATULATIONS TO ADIT-SMOAK! 👏

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💥 The Challenge: Hunting the Top Quark

The project's mission is crucial: to create a real-time, highly accurate classifier to distinguish between the decay products of a top quark and the massive amount of standard background data generated by detectors like those at CERN.

Adit-smoak’s approach begins with powerful data preprocessing:

• 🌌 Multi-Channel Jet Images: Raw particle collision data is converted into visual representations.

• 📊 Global Feature Integration: Key physics metrics (Energy/pT Skewness, Kurtosis) are embedded into the image input, providing the CNN with essential context.

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🧠 Engineering Highlights: Innovation in the Architecture

The core of this submission is a meticulously designed Convolutional Neural Network (CNN) that focuses on performance and reliability.

✨ 1. Aggregated Residual Transformations (The Feature Powerhouse)
This network moves beyond standard residual blocks by implementing Aggregated Residual Transformations with grouped convolutions. This architectural choice ensures superior feature extraction:

⚙️ “Channels are divided into different convolution blocks and each block extracts features better than one block extracting over all the channels.”

👀 2. Squeeze-and-Excite (SE) Blocks (Channel-Level Attention)
To manage the complexity of deep, multi-channel processing, SE Blocks were integrated. These blocks act as an attention mechanism, dynamically weighing the importance of each feature channel:

⭐ The SE Blocks calculate the "importance" of each channel, amplifying significant features and reducing the impact of insignificant channels on the output.

💻 3. The Deployment Edge: Ready for Real-Time
Demonstrating true engineering excellence, the repository includes a clear path for deployment. This highlights a crucial focus on low-latency, real-time filtering for physics experiments:

• ✅ HDL Code Generation: Files are included to convert the network into Hardware Description Language.

• ✅ FPGA Deployment: Code to deploy the solution directly onto an FPGA.

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🖼️ Architecture Spotlight

A look at one of the custom, highly efficient blocks used in the Deep CNN:

A visual of the Aggregated Residual + Squeeze-and-Excite block, the heart of the classifier.

We are incredibly proud to host this high-caliber work. Adit-smoak has provided not just a solution, but a template for future high-performance computing in scientific data analysis.

Explore the full project and dive into the code:
🔗 Repository Link: adit-smoak/Top-Quark-Tagging-Using-Deep-CNN