| Issue |
EPJ Web Conf.
Volume 251, 2021
25th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2021)
|
|
|---|---|---|
| Article Number | 03060 | |
| Number of page(s) | 9 | |
| Section | Offline Computing | |
| DOI | https://doi.org/10.1051/epjconf/202125103060 | |
| Published online | 23 August 2021 | |
https://doi.org/10.1051/epjconf/202125103060
Apprentice for Event Generator Tuning
1 Argonne National Laboratory, Lemont, IL 60439
2 Department of Computer Science, Durham University, South Road, Durham DH1 3LE, UK
3 Fermi National Accelerator Laboratory, Batavia, IL 60510
4 Lawrence Berkeley National Laboratory, Berkeley, CA 94720
* e-mail: mkrishnamoorthy@anl.gov
** e-mail: holger.schulz@durham.ac.uk
Published online: 23 August 2021
APPRENTICE is a tool developed for event generator tuning. It contains a range of conceptual improvements and extensions over the tuning tool Professor. Its core functionality remains the construction of a multivariate analytic surrogate model to computationally expensive Monte-Carlo event generator predictions. The surrogate model is used for numerical optimization in chi-square minimization and likelihood evaluation. Apprentice also introduces algorithms to automate the selection of observable weights to minimize the effect of mis-modeling in the event generators. We illustrate our improvements for the task of MC-generator tuning and limit setting.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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