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/2d033c08feb14be7849bd7c9ae99ac4e

Additional details

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