Particle Physics Seminars at BNL

Machine Learning for LHC Theory

by Anja Butter (Heidelberg University)


Over the next years, measurements at the LHC and the HL-LHC will provide us
with a wealth of data. The best hope of answering fundamental questions like
the nature of dark matter, is to adopt big data techniques for precision
simulations and optimized analyses to extract all relevant information.

LHC physics relies at a fundamental level on our ability to simulate events
efficiently from first principles. In the coming LHC runs, these simulations
will face unprecedented precision requirements to match the experimental
accuracy. Generative models have become a central tool to overcome limitations
from high precision in event generation and high dimensionality of detector
simulations. Such networks can be employed within established simulation tools,
as part of a simulation frameworks, or to compress measured data. Recent
studies have demonstrated that generative networks can perform high-precision
simulations while maintaining control over training stability and associated
uncertainties. Since generative networks in the form of normalizing flows can
be inverted, they also open new avenues in LHC analyses.

Meeting ID: 161 2994 2553
Passcode: 115286

Organized by

Tobias Neumann

Tobias Neumann