Related papers: Accelerating multijet-merged event generation with…
We present a novel Monte-Carlo implementation of the EKRT model, MC-EKRT, for computing partonic initial states in high-energy nuclear collisions. Our new MC-EKRT event generator is based on collinearly factorized, dynamically fluctuating…
A method is detailed for the phase space integration of multi-jets cross sections at lepton colliders, applicable to parton level Monte Carlo's at any order in perturbation theory. Other non-jet objects, massless or massive, can be included…
In this article, we investigate the possibility of enhancing the di-jet resonance searches by tagging the final state radiation (FSR) jet, using an event-level deep neural network. It is found that solely relying on the 4-momenta of the…
At the High Luminosity LHC, selecting important physics processes such as (di-) Higgs production will be a high priority. The Phase-2 Upgrade of the CMS Level-1 Trigger will reconstruct particle candidates and use pileup mitigation for the…
The GR@PPA event generator has been updated to version 2.7. This distribution provides event generators for $V$ ($W$ or $Z$) + jets ($\leq$ 4 jets), $VV$ + jets ($\leq$ 2 jets) and QCD multi-jet ($\leq$ 4 jets) production processes at $pp$…
As the quantities of data recorded by embedded edge sensors grow, so too does the need for intelligent local processing. Such data often comes in the form of time-series signals, based on which real-time predictions can be made locally…
We present a framework for automatically structuring and training fast, approximate, deep neural surrogates of stochastic simulators. Unlike traditional approaches to surrogate modeling, our surrogates retain the interpretable structure and…
High-fidelity physics simulations are powerful tools in the design and optimization of charged particle accelerators. However, the computational burden of these simulations often limits their use in practice for design optimization and…
We introduce multi-jet merging for deep inelastic scattering in the Vincia parton shower in the Monte Carlo event generator Pythia 8. Merging combines event samples of different parton multiplicities with logarithmically enhanced…
The ALPHA algorithm to evaluate the exact, tree-level matrix elements is reviewed in the context of multi-parton processes in QCD. The algorithm is suited for the authomatic calculation of tree-level scattering amplitudes and allows for…
The new version 6.1 of the HERWIG event generator allows one to simulate hadronic final states in electron-positron annihilations using the $O(\alpha_s^2)$ partonic matrix elements (MEs) for $e^+e^-\to q\bar qgg$ and $e^+e^-\to q\bar q…
In this article we generalise POWHEG next-to-leading order parton shower (NLOPS) simulations of vector boson production and vector boson production in association with a single jet, to give matrix element corrected MENLOPS simulations. In…
Multiscale problems are widely observed across diverse domains in physics and engineering. Translating these problems into numerical simulations and solving them using numerical schemes, e.g. the finite element method, is costly due to the…
Machine learning tools are commonly used in modern high energy physics (HEP) experiments. Different models, such as boosted decision trees (BDT) and artificial neural networks (ANN), are widely used in analyses and even in the software…
A new implementation of the POWHEG method into the Monte-Carlo event generator Sherpa is presented, focusing on processes with a simple colour structure. Results for a variety of processes, namely e+e- to hadrons, deep-inelastic…
This paper presents the development of an algorithm, termed the Global-Local Hybrid Surrogate (GLHS), designed to efficiently compute the probability of rare failure events in complex systems. The primary goal is to enhance the accuracy of…
We build a deep neural network based on the Mask R-CNN framework to detect the Higgs jets and top quark jets in any event image. We propose an algorithm to assign the top quark final states at the ground truth level so that the network can…
Predictive estimation, which comprises model calibration, model prediction, and validation, is a common objective when performing inverse uncertainty quantification (UQ) in diverse scientific applications. These techniques typically require…
Neural surrogates promise large speedups over classical solvers for physical dynamics but fail silently at sharp dynamical events such as shocks, fronts, and contact. We present hybrid neural world models for physical dynamics: a recipe for…
High-fidelity simulation models are widely used to analyze complex stochastic systems, but their high computational cost motivates the development of cheaper surrogate models that approximate the simulation model's input-output…