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.
Naturalness, Context, and Code: The Rise of Code Generating Language Models
A review of how advances in context modeling, from n‑grams to transformers, enabled modern language models to generate code.
Unlimited Associative Learning and the Natural Kind Status of Phenomenal Consciousness
Unlimited associative learning as phenomenal consciousness, using a natural kind framework.
Transcendental Idealism and QBism: Knowing through Appearances in Kant and Quantum Theory
Kant's transcendental idealism and its relation to contemporary quantum metaphysics.
No rules of induction: two responses to Hume's problem
Okasha's and Norton's no-rules approaches to Hume's problem of induction and how they might be unified.
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.