Published 2021
| Version v1
Journal article
Apprentice for Event Generator Tuning
- 1. Argonne National Laboratory, Lemont, IL 60439 (United States)
- 2. Department of Computer Science, Durham University, South Road, Durham DH1 3LE (United Kingdom)
- 3. Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States)
Description
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.
Availability note (English)
Available from https://www.epj-conferences.org/articles/epjconf/pdf/2021/05/epjconf_chep2021_03060.pdf; https://doaj.org/article/2d033c08feb14be7849bd7c9ae99ac4eAdditional details
Identifiers
Publishing Information
- Journal Title
- EPJ. Web of Conferences
- Journal Volume
- 251
- Journal Page Range
- vp.
- ISSN
- 2100-014X
Conference
- Title
- 25. International Conference on Computing in High Energy and Nuclear Physics
- Acronym
- CHEP 2021
- Dates
- 17-21 May 2021
- Place
- Geneva (Switzerland)
INIS
- Country of Publication
- France
- Country of Input or Organization
- France
- INIS RN
- 53090389
- Subject category
- S97: MATHEMATICAL METHODS AND COMPUTING;
- Resource subtype / Literary indicator
- Conference
- Descriptors DEI
- ALGORITHMS; COMPUTERIZED SIMULATION; MINIMIZATION; MONTE CARLO METHOD; MULTIVARIATE ANALYSIS; TUNING
- Descriptors DEC
- CALCULATION METHODS; MATHEMATICAL LOGIC; MATHEMATICS; OPTIMIZATION; SIMULATION; STATISTICS