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2016

Event generator tuning using Bayesian optimization

Ilten, Philip, Williams, Mike, Yang, Yunjie

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Monte Carlo event generators contain a large number of parameters that must be determined by comparing the output of the generator with experimental data.

Generating enough events with a fixed set of parameter values to enable making such a comparison is extremely CPU intensive, which prohibits performing a simple brute-force grid-based tuning of the parameters.
Bayesian optimization is a powerful method designed for such black-box tuning applications.
In this article, we show that Monte Carlo event generator parameters can be accurately obtained using Bayesian optimization and minimal expert-level physics knowledge.

Event generator tuning using Bayesian optimization · Around

Built on

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C. E. Rasmussen and C. K. I. Williams, · 2006
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Eur. Phys. J. C65
A. Buckley, H. Hoeth, H. Lacker, H. Schulz, and J. E. von Seggern, · 2010
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