Abstract
We present a novel integrator based on normalizing flows which can be used to improve the unweighting efficiency of Monte-Carlo event generators for collider physics simulations. In contrast to machine learning approaches based on surrogate models, our method generates the correct result even if the underlying neural networks are not optimally trained. We exemplify the new strategy using the example of Drell-Yan type processes at the LHC, both at leading and partially at next-to-leading order QCD.
Highlights
Numerical simulation programs are a cornerstone of collider physics
With more and more data available from the Large Hadron Collider (LHC) and the high-luminosity upgrade, the task of simulating collisions at high precision becomes a matter of concern for the high-energy physics community
In this publication we propose a novel idea to address the problem: We replace standard adaptive algorithms like VEGAS [6,7], by the extension [26,27] of a nonlinear independent components estimation (NICE) technique [28,29], known as a normalizing flow
Summary
Numerical simulation programs are a cornerstone of collider physics. They are used for the planning of future experiments, analysis of current measurements and, reinterpretation based on an improved theoretical understanding of nature. In this publication we propose a novel idea to address the problem: We replace standard adaptive algorithms like VEGAS [6,7], by the extension [26,27] of a nonlinear independent components estimation (NICE) technique [28,29], known as a normalizing flow This algorithm is combined with a recursive multichannel [30,31] to form a generic integrator for collider event generation. While the training of neural networks during the adaptation stage of the normalizing flow integrator is a very time consuming operation, event generation is inexpensive, because no gradients need to be.
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