402 citations · 911 across the 9 of their papers we have counts for
9 papers
Efficient Estimation of Unfactorizable Systematic Uncertainties
Alexis Romero, Kyle Cranmer, Daniel Whiteson
Accurate assessment of systematic uncertainties is an increasingly vital task in physics studies, where large, high-dimensional datasets, like those collected at the Large Hadron C…
Deep Learning and its Application to LHC Physics
Dan Guest, Kyle Cranmer, Daniel Whiteson
Machine learning has played an important role in the analysis of high-energy physics data for decades. The emergence of deep learning in 2012 allowed for machine learning tools whi…
Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes
Meghan Frate, Kyle Cranmer, Saarik Kalia +2
We describe a procedure for constructing a model of a smooth data spectrum using Gaussian processes rather than the historical parametric description. This approach considers a ful…
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…
Observing Ultra-High Energy Cosmic Rays with Smartphones
Daniel Whiteson, Michael Mulhearn, Chase Shimmin +3
We propose a novel approach for observing cosmic rays at ultra-high energy (~eV) by repurposing the existing network of smartphones as a ground detector array. Extensive…
Higgs Working Group Report of the Snowmass 2013 Community Planning Study
S. Dawson, A. Gritsan, H. Logan +131
This report summarizes the work of the Energy Frontier Higgs Boson working group of the 2013 Community Summer Study (Snowmass). We identify the key elements of a precision Higgs ph…