Event generator tuning using Bayesian optimization
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1610.08328.pdf
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2.06 MB
Format
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Checksum (MD5)
580ebbb69d105d4440a95619fa267307
Author(s)
Ilten, P.
Williams, M.
Yang, Y.
Ilten, Philip J
Williams, Michael
Yang, Yang
Date Issued
April 2017
Journal
Journal of Instrumentation
Publisher
IOP Publishing
Citation
Ilten, P. et al. “Event Generator Tuning Using Bayesian Optimization.” Journal of Instrumentation 12, 4 (April 2017): P04028–P04028 © 2017 IOP Publishing Ltd
Version
Author's final manuscript
Abstract
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. A tune of the PYTHIA 8 event generator using e⁺e⁻ events, where 20 parameters are optimized, can be run on a modern laptop in just two days. Combining the Bayesian optimization approach with expert knowledge should enable producing better tunes in the future, by making it faster and easier to study discrepancies between Monte Carlo and experimental data.
MIT Department
Massachusetts Institute of Technology. Laboratory for Nuclear Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1088/1748-0221/12/04/P04028
