MC++: A parallel, portable, Monte Carlo neutron transport code in C++

MC++ is an implicit multi-group Monte Carlo neutron transport code written in C++ and based on the Parallel Object-Oriented Methods and Applications (POOMA) class library. MC++ runs in parallel on and is portable to a wide variety of platforms, including MPPs, SMPs, and clusters of UNIX workstations. MC++ is being developed to provide transport capabilities to the Accelerated Strategic Computing Initiative (ASCI). It is also intended to form the basis of the first transport physics framework (TPF), which is a C++ class library containing appropriate abstractions, objects, and methods for the particle transport problem. The transport problem is briefly described, as well as the current status and algorithms in MC++ for solving the transport equation. The alpha version of the POOMA class library is also discussed, along with the implementation of the transport solution algorithms using POOMA. Finally, a simple test problem is defined and performance and physics results from this problem are discussed on a variety of platforms

Simulation in ALICE

ALICE, the experiment dedicated to the study of heavy ion collisions at the LHC, uses an object-oriented framework for simulation, reconstruction and analysis (AliRoot) based on ROOT. Here, we describe the general ALICE simulation strategy and those components of the framework related to simulation. Two main requirements have driven the development of the simulation components. First, the possibility to run different transport codes with the same user code for geometry and detector response has led to the development of the Virtual Monte Carlo concept. Second, simulation has to provide tools to efficiently study events ranging from low-multiplicity pp collisions to Pb-Pb collisions with up to 80000 primary particles per event. This has led to the development of a variety of collaborating generator classes and specific classes for event merging.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, 6 pages, LaTeX, 5 eps figures. PSN TUMT00

A parameter optimisation toolchain for Monte Carlo detector simulation

Monte Carlo detector transport codes are one of the backbones in high-energy physics computing. They simulate the transport of a large variety of different particle types through complex detector geometries based on different physics models. Those simulations are usually configurable through a large set of parameters allowing for some tuning on the client side. Often, tuning the physics accuracy on the one hand and optimising the resource needs on the other hand are competing requirements. In this area, we are presenting a toolchain to tune Monte Carlo transport codes which is capable of automatically optimising large sets of parameters based on user-defined metrics. The toolchain consists of two central components. Firstly, the MCReplayEngine which is a quasi-Monte-Carlo transport engine able to fast replay pre-recorded MC steps. This engine for instance allows one to study the impact of parameter variations on quantities such as hits without the need to perform new full simulations. Secondly, it consists of an automatic and generic parameter optimisation framework called O2Tuner. The toolchain’s application in concrete use-cases will be presented. Its first application in ALICE led to a reduction of CPU time of Monte Carlo detector transport by 30%. In addition, further possible scenarios will be discussed

The Virtual Monte Carlo

The concept of Virtual Monte Carlo (VMC) has been developed by the ALICE Software Project to allow different Monte Carlo simulation programs to run without changing the user code, such as the geometry definition, the detector response simulation or input and output formats. Recently, the VMC classes have been integrated into the ROOT framework, and the other relevant packages have been separated from the AliRoot framework and can be used individually by any other HEP project. The general concept of the VMC and its set of base classes provided in ROOT will be presented. Existing implementations for Geant3, Geant4 and FLUKA and simple examples of usage will be described.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, 8 pages, LaTeX, 6 eps figures. PSN THJT006. See http://root.cern.ch/root/vmc/VirtualMC.htm

Bayesian Estimation of Intrinsic Tissue Oxygenation and Perfusion from RGB Images

Multispectral imaging (MSI) can potentially assist the intra-operative assessment of tissue structure, function and viability, by providing information about oxygenation. In this paper, we present a novel technique for recovering intrinsic MSI measurements from endoscopic RGB images without custom hardware adaptations. The advantage of this approach is that it requires no modification to existing surgical and diagnostic endoscopic imaging systems. Our method uses a radiometric colour calibration of the endoscopic camera's sensor in conjunction with a Bayesian framework to recover a per-pixel measurement of the total blood volume (THb) and oxygen saturation (SO2) in the observed tissue. The sensor's pixel measurements are modelled as weighted sums over a mixture of Poisson distributions and we optimise the variables SO2 and THb to maximise the likelihood of the observations. To validate our technique, we use synthetic images generated from Monte Carlo (MC) physics simulation of light transport through soft tissue containing sub-surface blood vessels. We also validate our method on in vivo data by comparing it to a MSI dataset acquired with a hardware system that sequentially images multiple spectral bands without overlap. Our results are promising and show that we are able to provide surgeons with additional relevant information by processing endoscopic images with our modelling and inference framework

Unified Gas-kinetic Wave-Particle Methods III: Multiscale Photon Transport

In this paper, we extend the unified gas-kinetic wave-particle (UGKWP) method to the multiscale photon transport. In this method, the photon free streaming and scattering processes are treated in an un-splitting way. The duality descriptions, namely the simulation particle and distribution function, are utilized to describe the photon. By accurately recovering the governing equations of the unified gas-kinetic scheme (UGKS), the UGKWP preserves the multiscale dynamics of photon transport from optically thin to optically thick regime. In the optically thin regime, the UGKWP becomes a Monte Carlo type particle tracking method, while in the optically thick regime, the UGKWP becomes a diffusion equation solver. The local photon dynamics of the UGKWP, as well as the proportion of wave-described and particle-described photons are automatically adapted according to the numerical resolution and transport regime. Compared to the $S_n$ -type UGKS, the UGKWP requires less memory cost and does not suffer ray effect. Compared to the implicit Monte Carlo (IMC) method, the statistical noise of UGKWP is greatly reduced and computational efficiency is significantly improved in the optically thick regime. Several numerical examples covering all transport regimes from the optically thin to optically thick are computed to validate the accuracy and efficiency of the UGKWP method. In comparison to the $S_n$ -type UGKS and IMC method, the UGKWP method may have several-order-of-magnitude reduction in computational cost and memory requirement in solving some multsicale transport problems.Comment: 27 pages, 15 figures. arXiv admin note: text overlap with arXiv:1810.0598