Blog
Identification of highly boosted decays with the ATLAS detector using deep neural networks
This thesis introduces two jet tagging algorithms to identify highly boosted decays using the ATLAS detector at the LHC. Based on the Deep Neural Network (DNN) architecture, the first algorithm's performance is comparable to an existing algorithm designed for highly boosted decays. The DNN jet tagger is also multifunctional and highly effective for identifying decays. Notably, it displayed enhanced rejection rates for background -jets. The second algorithm leverages an Adversarial Neural Network (ANN) architecture for mass-decorrelated classification. While it exhibited a slight performance decrease compared to the DNN-based tagger, it demonstrated a reduction in mutual information between the mass feature and scalar discriminant metric, substantiating its capability for mass-decorrelated jet identification.
Chronogeometric Fatalism and the Philosophy of Time in Special Relativity
Minkowski space-time undermines Newtonian absolutes and supports an eternalist metaphysics. We conclude that relativity most strongly favors chronogeometric fatalism, where all points in space-time, past, present, and future, are equally real.