Published August 15, 2017
| Submitted
Report
Open
Efficient Monte Carlo Integration Using Boosted Decision Trees and Generative Deep Neural Networks
- Creators
- Bendavid, Joshua
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.
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.Attached Files
Submitted - 1707.00028.pdf
Files
1707.00028.pdf
Files
(1.2 MB)
Name | Size | Download all |
---|---|---|
md5:256b1e95462352e6958a2f8586d365f7
|
1.2 MB | Preview Download |
Additional details
- Eprint ID
- 80408
- Resolver ID
- CaltechAUTHORS:20170815-093542721
- DE-SC0011925
- Department of Energy (DOE)
- DE-AC02-07CH11359
- Department of Energy (DOE)
- Created
-
2017-08-15Created from EPrint's datestamp field
- Updated
-
2023-06-02Created from EPrint's last_modified field