Abstract
We present powerful new analysis techniques to constrain effective field theories at the LHC. By leveraging the structure of particle physics processes, we extract extra information from Monte Carlo simulations, which can be used to train neural network models that estimate the likelihood ratio. These methods scale well to processes with many observables and theory parameters, do not require any approximations of the parton shower or detector response, and can be evaluated in microseconds. We show that they allow us to put significantly stronger bounds on dimension-six operators than existing methods, demonstrating their potential to improve the precision of the LHC legacy constraints.
- Received 12 May 2018
DOI:https://doi.org/10.1103/PhysRevLett.121.111801
Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.
Published by the American Physical Society
Physics Subject Headings (PhySH)
Viewpoint
Fast-Forwarding the Search for New Particles
Published 12 September 2018
A proposed machine-learning approach could speed up the analysis that underlies searches for new particles in high-energy collisions.
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