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Efficient Monte Carlo Integration Using Boosted Decision Trees and Generative Deep Neural Networks

Bendavid, Joshua (2017) Efficient Monte Carlo Integration Using Boosted Decision Trees and Generative Deep Neural Networks. . (Submitted) https://resolver.caltech.edu/CaltechAUTHORS:20170815-093542721

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Abstract

New machine learning based algorithms have been developed and tested for Monte Carlo integration based on generative Boosted Decision Trees and Deep Neural Networks. Both of these algorithms exhibit substantial improvements compared to existing algorithms for non-factorizable integrands in terms of the achievable integration precision for a given number of target function evaluations. Large scale Monte Carlo generation of complex collider physics processes with improved efficiency can be achieved by implementing these algorithms into commonly used matrix element Monte Carlo generators once their robustness is demonstrated and performance validated for the relevant classes of matrix elements.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://arxiv.org/abs/1707.00028arXivDiscussion Paper
Additional Information:This project is supported by the United States Department of Energy, Office of High Energy Physics Research under Contract No. DE-SC0011925 and Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359.
Funders:
Funding AgencyGrant Number
Department of Energy (DOE)DE-SC0011925
Department of Energy (DOE)DE-AC02-07CH11359
Record Number:CaltechAUTHORS:20170815-093542721
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20170815-093542721
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:80408
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:15 Aug 2017 16:42
Last Modified:03 Oct 2019 18:30

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