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Applications of Data Intensive Science to High Energy Physics. The main essay discusses flavour tagging subatomic particles by training a neural network classifier on data obtained from the Future Circular Collider (FCC). The classifier is a hybrid of a transformer and recurrent neural network (RNN).

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A3 Coursework

This is my repository for the A3 coursework.

The report for the whole project is in pdf format and is located in the report directory.

A separate README file is provided for each question attempted in the respective directories.

The hepenv.yml file is provided for the experiment questions only. A separate conda env is given for the theory question in the directory.

Declaration of Use of Autogeneration Tools

Microsoft Copilot was used in the following cases:

  • A2: The use of tf.image.rot90 was suggested by Copilot to rotate the kernel, and I used this function in the code.
  • B1: I did not use any autogeneration tools in this question.
  • C2: Some of the plots generated in the report were suggested by Copilot. I edited the suggestions to make my own plots.

Note: In C2, I have made use of some of the code provided by the lecturer in the lecture. I thought it is worth mentioning here.

A declaration of the use of generative tools in writing the report is given in the report itself.

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Applications of Data Intensive Science to High Energy Physics. The main essay discusses flavour tagging subatomic particles by training a neural network classifier on data obtained from the Future Circular Collider (FCC). The classifier is a hybrid of a transformer and recurrent neural network (RNN).

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