231 citations · 327 across the 5 of their papers we have counts for
5 papers
Learning to Identify Semi-Visible Jets
Taylor Faucett, Shih-Chieh Hsu, Daniel Whiteson
We train a network to identify jets with fractional dark decay (semi-visible jets) using the pattern of their low-level jet constituents, and explore the nature of the information…
Learning to Isolate Muons
Julian Collado, Kevin Bauer, Edmund Witkowski +3
Distinguishing between prompt muons produced in heavy boson decay and muons produced in association with heavy-flavor jet production is an important task in analysis of collider ph…
Learning to Identify Electrons
Julian Collado, Jessica N. Howard, Taylor Faucett +3
We investigate whether state-of-the-art classification features commonly used to distinguish electrons from jet backgrounds in collider experiments are overlooking valuable informa…
Mapping Machine-Learned Physics into a Human-Readable Space
Taylor Faucett, Jesse Thaler, Daniel Whiteson
We present a technique for translating a black-box machine-learned classifier operating on a high-dimensional input space into a small set of human-interpretable observables that c…
Parameterized Machine Learning for High-Energy Physics
Pierre Baldi, Kyle Cranmer, Taylor Faucett +2
We investigate a new structure for machine learning classifiers applied to problems in high-energy physics by expanding the inputs to include not only measured features but also ph…