Deep Autoencoders for Compression in High Energy Physics

Sammanfattning: Current technological limitations make it impossible to store the enormous amount of data produced from proton-proton collisions by the ATLAS detector at CERN's Large Hadron Collider. Therefore, specialised hardware and software is being used to decide what data, or which proton-proton \textit{collision events}, to save and which to discard. A reduction in the storage size of each collision event is desirable as it would allow for more data to be saved and thereby make a larger set of physics analyses possible. The focus of this thesis is to understand whether it is possible to reduce the storage size of previously mentioned collision events using machine learning techniques for dimensionality reduction. This has never before been tried within the ATLAS experiment and is an interesting forward-looking study for future experiments. Specifically, autoencoder neural networks are used to compress a number of variables into a smaller latent space used for storage. Different neural network architectures with varying width and depth are explored. The AEs are trained and validated on experimental data from the ATLAS detector and their performance is tested on an independent signal Monte-Carlo sample. The AEs are shown to successfully compress and decompress simple hadron jet data and preliminary results indicate that the reconstruction quality is good enough for certain applications where high precision is not paramount. The AEs are evaluated by their reconstruction error, the relative error of each compressed variable and the ability to retain good resolution of a dijet mass signal (from the previously mentioned Monte-Carlo sample) after encoding and decoding hadron jet data.