Autoencoder Compression in High Energy Physics
Abstract: Situated in Geneva, Switzerland, the Large Hadron Collider is largest particle accelerator in the world, and as such, its operation carries with it some of the greatest technical challenges ever faced. Among them are the huge demands put on data storage capacity by experiments in particle physics, both in terms of rate and volume of data. Several systems are employed to manage and reduce the flow of data generated at the collider experiment stations. This comes at the cost of a reduced amount of material available for study. This thesis analyses a relatively novel method of compressing, and thereby reducing the storage requirements of, data describing jets - showers of particles created in collisions between protons in the ATLAS experiment at the Large Hadron Collider. The main tool used for this compression is an artificial neural network of a type called an autoencoder. Such compression has previously been shown to be possible on single jets. As a continuation of that work, this thesis investigates whether it is possible to compress groups of jets with better results than when compressing them individually. To that end, several autoencoder models are trained on jet groups of different configurations. These autoencoders are shown to be able to replicate the results of previous, single-jet studies, but the errors introduced during compression increase when jets are compressed in a group. This holds true for jets from the same proton-proton collision as well as jets randomly selected from a larger dataset. It is demonstrated that groups specifically made to contain jets with almost identical values of one variable can be compressed at a higher ratio than individual jets, with only slightly increased errors. However, this process carries with it the requirement of access to a large dataset, which is not possible if applied in a particle physics experiment, where data is gathered detection by detection.
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