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The event generator Sibyll can be used for the simulation of hadronic multiparticle production up to the highest cosmic ray energies. It is optimized for providing an economic description of those aspects of the expected hadronic final…
Artificial intelligence (AI) generative models, such as generative adversarial networks (GANs), variational auto-encoders, and normalizing flows, have been widely used and studied as efficient alternatives for traditional scientific…
The Hadron-Resonance Gas (HRG) approach - used to model hadronic matter at small baryon potentials $\mu_B$ and finite temperature $T$ - is extended to finite and large chemical potentials by introducing interactions between baryons in line…
The aim of this report of the Working Group on Hadronic Interactions and Air Shower Simulation is to give an overview of the status of the field, emphasizing open questions and a comparison of relevant results of the different experiments.…
The highly granular calorimeter prototypes of the CALICE collaboration have provided large data samples with precise three-dimensional information on hadronic showers with steel and tungsten absorbers and silicon, scintillator and gas…
Simulating physics processes and detector responses is essential in high energy physics and represents significant computing costs. Generative machine learning has been demonstrated to be potentially powerful in accelerating simulations,…
The status of thermal model descriptions of particle production in heavy ion collisions is presented. We discuss the formulation of statistical models with different implementation of the conservation laws and indicate their applicability…
Recent experiments of photon-nucleon and meson-nucleon scatterings have accumulated a lot of data for various meson production processes. One of the purposes of those experiments is to search for the missing resonances which are not…
Machine learning technology has the potential to dramatically optimise event generation and simulations. We continue to investigate the use of neural networks to approximate matrix elements for high-multiplicity scattering processes. We…
Generative models aim to learn the distribution of observed data by generating new instances. With the advent of neural networks, deep generative models, including variational autoencoders (VAEs), generative adversarial networks (GANs), and…
Particle accelerator modeling is an important field of research and development, essential to investigating, designing and operating some of the most complex scientific devices ever built. Kinetic simulations of relativistic, charged…
The predictions of hadronic interaction models for cosmic-ray induced air showers contain inherent uncertainties due to limitations of available accelerator data and theoretical understanding in the required energy and rapidity regime.…
The CALICE collaboration has published a detailed study of hadronic interactions using data recorded with the highly granular CALICE silicon-tungsten electromagnetic calorimeter (Si-W ECAL). Approximately 350,000 selected negative pion…
Subsurface simulations use computational models to predict the flow of fluids (e.g., oil, water, gas) through porous media. These simulations are pivotal in industrial applications such as petroleum production, where fast and accurate…
Generative models trained on internet data have revolutionized how text, image, and video content can be created. Perhaps the next milestone for generative models is to simulate realistic experience in response to actions taken by humans,…
The present status of high energy cosmic ray interaction models is discussed, concentrating on recent model updates inspired by the data from Run 1 of the LHC. A special attention is devoted to the remaining differences in the model…
Real-world physical systems are inherently complex, often involving the coupling of multiple physics, making their simulation both highly valuable and challenging. Many mainstream approaches face challenges when dealing with decoupled data.…
In this paper, we present a machine learning-based data generator framework tailored to aid researchers who utilize simulations to examine various physical systems or processes. High computational costs and the resulting limited data often…
The Geant4 photonuclear process is benchmarked by comparing it with experimental data to verify the ability of the Geant4 toolkit to simulate the photo-induced reaction on deformed nuclei in the Giant Dipole Resonance (GDR) region. The…
A generative model is a statistical model that is able to generate new data instances from previously observed ones. In the context of business processes, a generative model creates new execution traces from a set of historical traces, also…