scholarly journals Exhaustive neural importance sampling applied to Monte Carlo event generation

2020 ◽  
Vol 102 (1) ◽  
Author(s):  
Sebastian Pina-Otey ◽  
Federico Sánchez ◽  
Thorsten Lux ◽  
Vicens Gaitan
Keyword(s):  
Monte Carlo ◽  
Importance Sampling ◽  
Monte Carlo Event ◽  
Event Generation

scholarly journals Angular distributions in Monte Carlo event generation of weak single-pion production

2021 ◽  
Vol 103 (5) ◽  
Author(s):  
K. Niewczas ◽  
A. Nikolakopoulos ◽  
J. T. Sobczyk ◽  
N. Jachowicz ◽  
R. González-Jiménez
Keyword(s):  
Monte Carlo ◽  
Pion Production ◽  
Angular Distributions ◽  
Monte Carlo Event ◽  
Event Generation

UNIBAB, version 2.0: Monte Carlo event generation for large angle Bhabha scattering at LEP

1994 ◽  
Vol 79 (3) ◽  
pp. 466-486 ◽  
Author(s):  
H. Anlauf ◽  
P. Manakos ◽  
T. Mannel ◽  
T. Ohl ◽  
H. Meinhard ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Large Angle ◽  
Monte Carlo Event ◽  
Bhabha Scattering ◽  
Version 2.0 ◽  
Event Generation

scholarly journals An efficient Monte Carlo event generation method for multiperipheral models

1971 ◽  
Vol 8 (1) ◽  
pp. 144-152 ◽  
Author(s):  
Jerome H Friedman ◽  
Gerald R Lynch ◽  
Clifford G Risk ◽  
Thomas A Zang
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Event ◽  
Event Generation

scholarly journals Monte Carlo event generation of neutrino-electron scattering

2021 ◽  
Author(s):  
Dmitry Zhuridov ◽  
Jan Sobczyk ◽  
Cezary Juszczak ◽  
Kajetan Niewczas
Keyword(s):  
Monte Carlo ◽  
Electron Scattering ◽  
Monte Carlo Event ◽  
Event Generation

scholarly journals Event generation with Sherpa 2.2

2019 ◽  
Vol 7 (3) ◽  
Author(s):  
Enrico Bothmann ◽  
Gurpreet Singh Chahal ◽  
Stefan Höche ◽  
Johannes Krause ◽  
Frank Krauss ◽  
...  
Keyword(s):  
Monte Carlo ◽  
High Energy ◽  
General Purpose ◽  
Particle Collision ◽  
Monte Carlo Event ◽  
Event Generator ◽  
Particle Collisions ◽  
Monte Carlo Event Generator ◽  
Event Generation ◽  
High Energy Collider

Sherpa is a general-purpose Monte Carlo event generator for the simulation of particle collisions in high-energy collider experiments. We summarise essential features and improvements of the Sherpa 2.2 release series, which is heavily used for event generation in the analysis and interpretation of LHC Run 1 and Run 2 data. We highlight a decade of developments towards ever higher precision in the simulation of particle-collision events.

scholarly journals Drone Ground Impact Footprints with Importance Sampling: Estimation and Sensitivity Analysis

2021 ◽  
Vol 11 (9) ◽  
pp. 3871
Author(s):  
Jérôme Morio ◽  
Baptiste Levasseur ◽  
Sylvain Bertrand
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Importance Sampling ◽  
Loss Of Control ◽  
Minimum Volume ◽  
Multiple Importance Sampling ◽  
Impact Position ◽  
Engine Failure ◽  
Main Engine ◽  
Ground Impact

This paper addresses the estimation of accurate extreme ground impact footprints and probabilistic maps due to a total loss of control of fixed-wing unmanned aerial vehicles after a main engine failure. In this paper, we focus on the ground impact footprints that contains 95%, 99% and 99.9% of the drone impacts. These regions are defined here with density minimum volume sets and may be estimated by Monte Carlo methods. As Monte Carlo approaches lead to an underestimation of extreme ground impact footprints, we consider in this article multiple importance sampling to evaluate them. Then, we perform a reliability oriented sensitivity analysis, to estimate the most influential uncertain parameters on the ground impact position. We show the results of these estimations on a realistic drone flight scenario.

Reliability Assessment of Power System Using Importance Sampling Technique Based on Layer Optimization Simulation

2011 ◽  
Vol 88-89 ◽  
pp. 554-558 ◽  
Author(s):  
Bin Wang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Power System ◽  
Importance Sampling ◽  
System Reliability ◽  
Sampling Method ◽  
Sampling Technique ◽  
Test System ◽  
Importance Sampling Method ◽  
Importance Sampling Technique

An improved importance sampling method with layer simulation optimization is presented in this paper. Through the solution sequence of the components’ optimum biased factors according to their importance degree to system reliability, the presented technique can further accelerate the convergence speed of the Monte-Carlo simulation. The idea is that the multivariate distribution’ optimization of components in power system is transferred to many steps’ optimization based on importance sampling method with different optimum biased factors. The practice is that the components are layered according to their importance degree to the system reliability before the Monte-Carlo simulation, the more forward, the more important, and the optimum biased factors of components in the latest layer is searched while the importance sampling is carried out until the demanded accuracy is reached. The validity of the presented is verified using the IEEE-RTS79 test system.

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