Conveners
Machine Learning Inference and Interpretation
- Anja Butter
- Jesse Thaler (MIT)
Bayesian neural networks allow us to keep track of uncertainties, for example in top tagging, by learning a tagger output together with an error band. We illustrate the main features of Bayesian versions of established deep-learning taggers. We show how they capture statistical uncertainties from finite training samples, systematics related to the jet energy scale, and stability issues through...
Unfolding is the procedure by which the "recorded" detector-level distribution of an observable is corrected for detector effects and other sources of noise to obtain the "true" particle-level distribution. In high-energy particle physics, unfolding is a ubiquitous part of measurements at the LHC. The current state-of-the-art procedure, Iterated Bayesian Unfolding (IBU), is typically applied...
ML tools based on generative models, such as cycleGANs and invertible architectures, can be used to address the problem of unfolding detector effects, a challenge for data analysis at hadronic colliders.
In this talk, we review how machine learning is changing the way we are thinking about jets. First, we present a simplified model to aid in machine learning research for jet physics, that captures the essential ingredients of parton shower generators in full physics simulations. We study how to unify generation and inference, where we aim to invert the generative model to estimate the...
One of the most common applications of machine learning in high energy physics is in event selection (and categorization). The physics goals of event selection and categorization are to improve the significance of a potential excess (for signal discovery/upper limit setting analyses), and to reduce the uncertainty of a parameter measurement (parameter measurement analyses).
Event selection...
In this talk, we present exploratory work to enable benchmark tests for physics challenges, such as “The Machine Learning Landscape of Top Taggers” comparison or the LHCOlympics2020. We introduce the “Reproducible Open Benchmarks for Data Analysis Platform” (ROB) for this task and we aim to show a demo where ROB is implemented on a sample case. Given a benchmark workflow, users would provide...
We present a set of tools based on Layerwise Relevance Propagation (LRP) to achieve eXplainable AI (XAI) for jets.
