The “jet topics” framework identifies (or defines) underlying classes of jets directly from data with little to no input from simulation or theory. Due to a mathematical connection between mixed samples of jets and emergent themes in documents, methods from topic modeling and blind source separation can be used to extract jet topics from data. Any machine-learned jet tagger, treated as a...
In applications of machine learning to particle physics, a persistent challenge is how to go beyond discrimination to learn about the underlying physics.
In this talk, we will present a new framework: JUNIPR, Jets from UNsupervised Interpretable PRobabilistic models, which uses unsupervised learning to learn the intricate high-dimensional contours of the data upon which it is trained, without...
Machine learning (ML) has rapidly become a core tool for LHC physics, due to the great volume and complexity of the data that this machine collects. Given that it is not a-priori known what form new physics (if any) might take, there has been a surge of interest in the past year in approaches that would enable an ML algorithm to look for new physics directly in the LHC data without reference...
In the current era of high energy particle collider experiments, we are faced with an overwhelming amount of data and the limiting uncertainty in new physics searches can often come from theory and not experiment. In our efforts to develop new approaches to extract complex signals from large backgrounds, BDTs, neural networks and other machine learning techniques are becoming increasingly...
Autoencoders as tools for new physics discovery. The key idea of the autoencoder is that it learns to map background events back to themselves, but fails to reconstruct anomalous events that it has never encountered before. The reconstruction error can then be used as an anomaly threshold. An illustrative example of background QCD jets versus tops will be discussed.
Novelty detection is the machine learning task to recognize data belonging to an unknown pattern. Complementary to supervised learning, it allows to analyze data without a priori knowledge on signal or model-independently. In this talk, we would demonstrate the potential role of novelty detection in collider physics, using autoencoder-based deep neural network. Explicitly, we develop a set of...
