BSc Dissertation - Breaking Omnifold

This repository contains the code used in the dissertation Breaking Omnifold by Eliott Menard. It includes:

1. Data Generation Code:

   • Generates synthetic datasets as described in the dissertation.
   • Features detailed annotations for ease of understanding and reproducibility.
   • Note: The T-SNE visualizations are currently incomplete and may not provide meaningful insights due to time constraints.

2. Modified Omnifold Implementation:

   • Based on the original Omnifold code from hep-lbdl/OmniFold.
   • Incorporates the modifications discussed in the dissertation for re-weighting.
   • Includes an additional change aimed at optimizing resource usage for lower-end devices, albeit with some trade-offs in performance.
   • Implements an iterative trial framework for training neural networks, as outlined in the dissertation.
   • Note: This code lacks thorough documentation, and the implemented changes may not be immediately apparent.

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Notes:

• The data generation code is well-annotated and functional.
• The Omnifold code modifications, while functional, are not properly commented. Additional work may be needed to fully understand the changes.

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References:

• The original Omnifold implementation can be found at hep-lbdl/OmniFold.
• For more details on the Omnifold methodology, refer to the official paper: OmniFold: A Method to Simultaneously Unfold All Observables, published in Physical Review Letters.

Feel free to reach out or open an issue if you have questions or suggestions for improvements!