High Energy Physics Simulations Research Articles

Precise Quantum Angle Generator Designed for Noisy Quantum Devices

Precise Quantum Angle Generator Designed for Noisy Quantum Devices

GIPPE-RPT: Geant4 interface for particle physics experiments applied to Radioactive Particle Tracking

Radioactive Particle Tracking (RPT) is a non-invasive experimental technique that tracks the motion of a gamma-emitting radionuclide. Despite the RPT’s high versatility, the lack of dedicated software represents a significant barrier to its wider adoption. This article introduces a new software, GIPPE-RPT, designed to bring the technique closer to a wider group of users. GIPPE-RPT is a user-friendly software that enables the creation, execution, and post-processing of high-energy physics simulations in Geant4 employing a graphical user interface. Under the platform, the user can specify all critical RPT parameters, such as reactor geometry, materials, detector number and type, and tracer type and activity. Multiple configurations can be designed and compared for optimization. GIPPE-RPT also integrates the OpenFOAM solver, which enables the setup and execution of Computational Fluid Dynamics simulations. The simulation results can be imported in Geant4 allowing an accurate description of density profiles inside the reactor. The main software modules, functions, and workflow are demonstrated using a virtual NETL SSCP-I fluidized bed reactor as a test case. The main steps in setting up an RPT experiment in GIPPE-RPT, including domain design, tracer selection, detector placement, and calibration strategy are presented in detail. The performance of six position reconstruction methods implemented in GIPPE-RPT is compared for several RPT scenarios highlighting the strengths and weaknesses of each method. Finally, the benefits of capturing heterogeneous density profiles using simulations are demonstrated by comparing reconstruction errors for test cases with heterogeneous and homogeneous media.

Quantum Algorithm for High Energy Physics Simulations.

Simulating quantum field theories is a flagship application of quantum computing. However, calculating experimentally relevant high energy scattering amplitudes entirely on a quantum computer is prohibitively difficult. It is well known that such high energy scattering processes can be factored into pieces that can be computed using well established perturbative techniques, and pieces which currently have to be simulated using classical Markov chain algorithms. These classical Markov chain simulation approaches work well to capture many of the salient features, but cannot capture all quantum effects. To exploit quantum resources in the most efficient way, we introduce a new paradigm for quantum algorithms in field theories. This approach uses quantum computers only for those parts of the problem which are not computable using existing techniques. In particular, we develop a polynomial time quantum final state shower that accurately models the effects of intermediate spin states similar to those present in high energy electroweak showers with a global evolution variable. The algorithm is explicitly demonstrated for a simplified quantum field theory on a quantum computer.

Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations

The precise simulation of particle transport through detectors remains a key element for the successful interpretation of high energy physics results. However, Monte Carlo based simulation is extremely demanding in terms of computing resources. This challenge motivates investigations of faster, alternative approaches for replacing the standard Monte Carlo technique.We apply Generative Adversarial Networks (GANs), a deep learning technique, to replace the calorimeter detector simulations and speeding up the simulation time by orders of magnitude. We follow a previous approach which used three-dimensional convolutional neural networks and develop new two-dimensional convolutional networks to solve the same 3D image generation problem faster. Additionally, we increased the number of parameters and the neural networks representational power, obtaining a higher accuracy. We compare our best convolutional 2D neural network architecture and evaluate it versus the previous 3D architecture and Geant4 data. Our results demonstrate a high physics accuracy and further consolidate the use of GANs for fast detector simulations.

Resampling algorithms for high energy physics simulations

We demonstrate that the method of interleaved resampling in the context of parton showers can tremendously improve the statistical convergence of weighted parton shower evolution algorithms. We illustrate this by several examples showing significant statistical improvement.

Software Development Strategies for High-Energy Physics Simulations Based on Quantum Field Theory

Let us review the main elements of strategies, which we identified in the development of high energy physics simulations. Based on exemplary Monte Carlo simulation projects, several conjectures for the general aspects of work on scientific software are presented, including openness to change, robustness against external changes, modular development processes, and the constant evolution of tests. The analyzed projects were developed over more than 30 years, much longer than the typical lifetime of computer industry software. Some general observations are drawn together with quantitative evaluation on how the quality of software, measured mainly by the precision of the simulation, grows together with the complexity of software and tests. We find these observations interesting, even if attempts to generalize may remain to a degree speculative, as every researcher is tuning that to the specific needs of their own application. In the future, this can be validated, adjusted, or extended by the experiences gained in other projects.

Three dimensional Generative Adversarial Networks for fast simulation

We present the first application of three-dimensional convolutional Generative Adversarial Network to High Energy Physics simulation. We generate three-dimensional images of particles depositing energy in high granularity calorimeters. This is the first time such an approach is taken in HEP where most of data is three-dimensional in nature but it is customary to convert it into two-dimensional slices. The present work proves the success of using three dimensional convolutional GAN. Energy showers are well reproduced in all dimensions and show a good agreement with standard techniques (Geant4 detailed simulation). We also demonstrate the ability to condition training on several parameters such as particle type and energy. This work aims at proving that deep learning techniques represent a valid fast alternative to standard Monte Carlo approaches. It is part of the GeantV project.

CMS@home: Integrating the Volunteer Cloud and High-Throughput Computing

Volunteer computing has the potential to provide significant additional computing capacity for the LHC experiments. Initiatives such as the CMS@home project are aiming to integrate volunteer computing resources into the experiment’s computational frameworks to support their scientific workloads. This is especially important, as over the next few years the demands on computing capacity will increase beyond what can be supported by general technology trends. This paper describes how a volunteer computing project that uses virtualization to run high energy physics simulations can integrate those resources into their computing infrastructure. The concept of the volunteer cloud is introduced and how this model can simplify the integration is described. An architecture for implementing the volunteer cloud model is presented along with an implementation for the CMS@home project. Finally, the submission of real CMS workloads to this volunteer cloud are compared to identical workloads submitted to the grid.

Quantum computer used for high-energy physics simulation

Quantum computer used for high-energy physics simulation

Quantum computer makes first high-energy physics simulation

Quantum computer makes first high-energy physics simulation

Local simulation algorithms for Coulomb gases with dynamical dielectric effects

We discuss the application of the local lattice technique of Maggs and Rossetto [Phys. Rev. Lett. 88, 196402 (2002)] to problems that involve the motion of objects with different dielectric constants than the background. In these systems, the simulation method produces a spurious interaction force which causes the particles to move in an unphysical manner. We show that this term can be removed using a variant of a method known from high-energy physics simulations, the multiboson method, and demonstrate the effectiveness of this corrective method on a system of neutral particles. We then apply our method to a one-component plasma to show the effect of the spurious interaction term on a charged system.

A New Technique for Sampling Multi-modal Distributions

In this paper we demonstrate that multi-modal probability distribution functions (PDFs) may be efficiently sampled using an algorithm originally developed for numerical integration by Monte Carlo methods. This algorithm can be used to generate an input PDF which can be used as an independence sampler in a Metropolis–Hastings chain to sample otherwise troublesome distributions. Some examples in one, two, and five dimensions are worked out. We also comment on the possible application of our results to event generation in high-energy physics simulations.

Could running experience on spmd computers contribute to the architectural choices for future dedicated computers for high energy physics simulation?

Results obtained on a strongly coupled parallel computer are reported. They concern Monte-Carlo simulation and pattern recognition. Though the calculations were made on an experimental computer of rather low processing power, it is believed that the quoted figures could give useful indications on architectural choices for dedicated computers.