Related papers: MAGE-HEP: Monte Carlo Analysis and Graphical Envir…
Over the past few decades, machine learning has been widely used to learn complex tasks. Reinforcement Learning (RL), inspired by human behavior, is a great example, as it involves developing specific behaviours for specific tasks. To…
We announce the forthcoming public release of Version 1.1 of MGGPOD, a user-friendly suite of Monte Carlo codes built around the widely used GEANT (Version 3.21) package. MGGPOD is capable of simulating ab initio the physical processes…
This article provides an introduction to the principles of particle physics event generators that are based on the Monte Carlo method. Following some preliminaries, instructions on how to build a basic parton-level Monte Carlo event…
Tethering methods allow us to perform Monte Carlo simulations in ensembles with conserved quantities. Specifically, one couples a reservoir to the physical magnitude of interest, and studies the statistical ensemble where the total…
Current applications have produced graphs on the order of hundreds of thousands of nodes and millions of edges. To take advantage of such graphs, one must be able to find patterns, outliers and communities. These tasks are better performed…
Active inference is a Bayesian framework for understanding biological intelligence. The underlying theory brings together perception and action under one single imperative: minimizing free energy. However, despite its theoretical utility in…
We present a method which extends Monte Carlo studies to situations that require a large dynamic range in particle number. The underlying idea is that, in order to calculate the collisional evolution of a system, some particle interactions…
This article is motivated by studying the interaction of magnetic moments in high entropy alloys (HEAs), which plays an important role in guiding HEA designs in materials science. While first principles simulations can capture magnetic…
The goal of multi-task learning is to learn to conduct multiple tasks simultaneously based on a shared data representation. While this approach can improve learning efficiency, it may also cause performance degradation due to task conflicts…
High Energy Physics processes, such as hard scattering, parton shower, and hadronization, occur at colliders around the world, e.g., the Large Hadron Collider in Europe. The various steps are also components within corresponding Monte-Carlo…
Large language model-based (LLM-based) multi-agent systems (MAS) are increasingly used to extend agentic problem solving via role specialization and collaboration. MAS workflows can be naturally modeled as directed computation graphs, where…
Monte Carlo event generators are essential components of almost all experimental analyses and are also widely used by theorists and experiments to make predictions and preparations for future experiments. They are all too often used as…
The Large Hadron Collider's high luminosity era presents major computational challenges in the analysis of collision events. Large amounts of Monte Carlo (MC) simulation will be required to constrain the statistical uncertainties of the…
Safe and interpretable motion planning in complex urban environments needs to reason about bidirectional multi-agent interactions. This reasoning requires to estimate the costs of potential ego driving maneuvers. Many existing planners…
We present the {\tt KKMCee 5.00.2} Monte Carlo event generator for lepton and quark pair production for the high energy electron-positron annihilation process. It is still the most sophisticated event generator for such processes. Its…
The accuracy of Monte Carlo simulations in reproducing the scientific performance of space telescopes (e.g. angular resolution) is mandatory for a correct design of the mission. A brand-new Monte Carlo simulator of the Astrorivelatore Gamma…
In modern High Energy Physics (HEP) experiments visualization of experimental data has a key role in many activities and tasks across the whole data chain: from detector development to monitoring, from event generation to reconstruction of…
Metal hydrides are promising candidates for hydrogen storage applications. From a materials discovery perspective, an accurate, efficient computational workflow is urgently required that can rapidly analyze/predict thermodynamic properties…
Polyhedral nanocrystals are building blocks for nanostructured materials that find applications in catalysis and plasmonics. Synthesis efforts and self-assembly experiments have been assisted by computer simulations that predict phase…
Recently a two-Higgs-doublet model with maximal symmetry under generalised CP transformations, the MCPM, has been proposed. The theory features a unique fermion mass spectrum which, although not describing nature precisely, provides a good…