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The algorithm for Monte Carlo simulation of parton-level events based on an Artificial Neural Network (ANN) proposed in arXiv:1810.11509 is used to perform a simulation of $Hto 4ell$ decay. Improvements in the training algorithm have been implemented to avoid numerical instabilities. The integrated decay width evaluated by the ANN is within 0.7% of the true value and unweighting efficiency of 26% is reached. While the ANN is not automatically bijective between input and output spaces, which can lead to issues with simulation quality, we argue that the training procedure naturally prefers bijective maps, and demonstrate that the trained ANN is bijective to a very good approximation.
We review the main software and computing challenges for the Monte Carlo physics event generators used by the LHC experiments, in view of the High-Luminosity LHC (HL-LHC) physics programme. This paper has been prepared by the HEP Software Foundation
We present MadFlow, a first general multi-purpose framework for Monte Carlo (MC) event simulation of particle physics processes designed to take full advantage of hardware accelerators, in particular, graphics processing units (GPUs). The automation
Monte Carlo simulations are widely used in many areas including particle accelerators. In this lecture, after a short introduction and reviewing of some statistical backgrounds, we will discuss methods such as direct inversion, rejection method, and
We describe the Monte Carlo (MC) simulation package of the Borexino detector and discuss the agreement of its output with data. The Borexino MC ab initio simulates the energy loss of particles in all detector components and generates the resulting sc
SABRE (Sodium-iodide with Active Background REjection) is a direct dark matter search experiment based on an array of radio-pure NaI(Tl) crystals surrounded by a liquid scintillator veto. Twin SABRE experiments in the Northern and Southern Hemisphere