Evaluation of float-truncation based compression techniques for the ATLAS jet trigger

Abstract: Data compression methods allow more data to be stored within a certain storage framework while still keeping the characteristics of the data in question. At the Large Hadron Collider on the grounds of CERN in Switzerland, limited data storage capability is and has always been an urgent problem. At the ATLAS experiment, one technique which allows researchers to save more data within the same storage framework is so called trigger level analysis [TLA]. This thesis work explores float truncation based data compression as an improvement to TLA. It is shown that this compression technique is promising for compressing several variables from TLA datasets, while however generating artifacts in the compressed distributions. This phenomenon is known as double quantization. It is explained how this effect is more or less unavoidable as it is an effect always present when discretizing a continuous distribution several times in succession. Furthermore, this thesis work explores the applicability of chaining float-truncation techniques with machine learning techniques (so called autoencoder compression). It is shown that the original dataset is still well represented after applying the two techniques in succesion.