Conveners
Decorrelation and Semi/Unsupervised approaches
- Nhan Viet Tran (Fermi National Accelerator Lab. (US))
- Chase Owen Shimmin (Yale University (US))
In this talk, I explore unsupervised and supervised machine learning techniques using CMS Open Data. I introduce a metric between jets based on the earth (or energy) mover's distance: the “work” required to rearrange one event into the other. Using this metric, I will probe the metric space of jets using unsupervised methods. Further, training supervised jet classifiers directly on data can...
Jet substructure tagging of highly boosted heavy resonances decaying to quarks has become an important tool for Standard Model (SM) measurements and searches for beyond the SM physics. Background estimation typically rely on at least 3 data sideband regions that can be separated from the signal region with the physics process of interest by a set of two uncorrelated variables. For searches...
With great classification power comes great responsibility: Now that deep-learning is the de-facto standard for jet classification in high-energy physics, attention needs to be paid to aspects beyond performance. A key issue is the question of decorrelation - how a classifier output can be made independent of other salient variables such as the jet's mass. Achieving reliable decorrelation is...
When the space of collider events is equipped with a metric, many simple-to-use machine learning algorithms can be applied to perform the task of jet tagging. Here we explore several different generalizations of the Energy Mover’s Distance. The computed distance matrices are fed into both supervised and unsupervised learning models, and their performances in distinguishing various types of...
