No Arabic abstract
We have developed a detailed Monte Carlo simulation program for the Belle TOF system. Based on GEANT simulation, it takes account of all physics processes in the TOF scintillation counters and readout electronics. The simulation reproduces very well the performance of the Belle TOF system, including the dE/dx response, the time walk effect, the time resolution, and the hit efficiency due to beam background. In this report, we will describe the Belle TOF simulation program in detail.
The MIEZE (Modulation of Intensity with Zero Effort) technique is a variant of neutron resonance spin echo (NRSE), which has proven to be a unique neutron scattering technique for measuring with high energy resolution in magnetic fields. Its limitations in terms of flight path differences have already been investigated analytically for neutron beams with vanishing divergence. In the present work Monte-Carlo simulations for quasi-elastic MIEZE experiments taking into account beam divergence as well as the sample dimensions are presented. One application of the MIEZE technique could be a dedicated NRSE-MIEZE instrument at the European Spallation Source (ESS) in Sweden. The optimisation of a particular design based on Montel mirror optics with the help of Monte Carlo simulations will be discussed here in detail.
For high precision measurements of K decays, the presence of radiated photons cannot be neglected. The Monte Carlo simulations must include the radiative corrections in order to compute the correct event counting and efficiency calculations. In this paper we briefly describe a method for simulating such decays.
We perform quantum Monte Carlo simulations in the background of a classical black hole. The lattice discretized path integral is numerically calculated in the Schwarzschild metric and in its approximated metric. We study spontaneous symmetry breaking of a real scalar field theory. We observe inhomogeneous symmetry breaking induced by inhomogeneous gravitational field.
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 present a method for performing Hamiltonian Monte Carlo that largely eliminates sample rejection for typical hyperparameters. In situations that would normally lead to rejection, instead a longer trajectory is computed until a new state is reached that can be accepted. This is achieved using Markov chain transitions that satisfy the fixed point equation, but do not satisfy detailed balance. The resulting algorithm significantly suppresses the random walk behavior and wasted function evaluations that are typically the consequence of update rejection. We demonstrate a greater than factor of two improvement in mixing time on three test problems. We release the source code as Python and MATLAB packages.