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We propose and assess an alternative quantum generator architecture in the context of generative adversarial learning for Monte Carlo event generation, used to simulate particle physics processes at the Large Hadron Collider (LHC). We…

Event generators are an indispensable tool for the preparation and analysis of particle-physics experiments. In this contribution, physics principles underlying the construction of such computer programs are discussed. Results, within and…

High Energy Physics - Phenomenology · Physics 2008-12-18 A. Schaelicke , T. Gleisberg , S. Hoeche , S. Schumann , J. Winter , F. Krauss , G. Soff

A grand canonical Monte Carlo (MC) algorithm is presented for studying the lattice gas model (LGM) of multiple protein sequence alignment, which coherently combines long-range interactions and variable-length insertions. MC simulations are…

Biomolecules · Quantitative Biology 2017-07-13 Akira R. Kinjo

This paper presents a novel approach for directly generating full events at detector-level from parton-level information, leveraging cutting-edge machine learning techniques. To address the challenge of multiplicity variations between…

High Energy Physics - Phenomenology · Physics 2024-11-01 Guillaume Quétant , John Andrew Raine , Matthew Leigh , Debajyoti Sengupta , Tobias Golling

Sherpa is a general-purpose Monte Carlo event generator for the simulation of particle collisions in high-energy collider experiments. We summarize essential features and improvements of the Sherpa 2.2 release series, which is heavily used…

Probabilistic modeling provides the capability to represent and manipulate uncertainty in data, models, predictions and decisions. We are concerned with the problem of learning probabilistic models of dynamical systems from measured data.…

Computation · Statistics 2018-03-14 Thomas B. Schön , Andreas Svensson , Lawrence Murray , Fredrik Lindsten

The Multilevel Monte Carlo (MLMC) method has proven to be an effective variance-reduction statistical method for Uncertainty Quantification (UQ) in Partial Differential Equation (PDE) models, combining model computations at different levels…

Mathematical Software · Computer Science 2023-05-24 Santiago Badia , Jerrad Hampton , Javier Principe

Event reconstruction is a central step in many particle physics experiments, turning detector observables into parameter estimates; for example estimating the energy of an interaction given the sensor readout of a detector. A corresponding…

High Energy Physics - Experiment · Physics 2023-01-11 Philipp Eller , Aaron Fienberg , Jan Weldert , Garrett Wendel , Sebastian Böser , D. F. Cowen

We review recent theoretical improvements of Monte Carlo event generators for top-quark pair production and decay at the LHC based on the POWHEG method. We present an event generator that implements spin correlations and off-shell effects…

High Energy Physics - Phenomenology · Physics 2018-01-22 Tomáš Ježo

Status of tau lepton decay Monte Carlo generator TAUOLA, and its main recent applications are reviewed. It is underlined, that in recent efforts on development of new hadronic currents, the multi-dimensional nature of distributions of the…

High Energy Physics - Phenomenology · Physics 2019-07-02 V. Cherepanov , E. Richter-Was , Z. Was

Langevin algorithms are popular Markov chain Monte Carlo (MCMC) methods for large-scale sampling problems that often arise in data science. We propose Monte Carlo algorithms based on the discretizations of $P$-th order Langevin dynamics for…

Machine Learning · Statistics 2025-08-26 Thanh Dang , Mert Gurbuzbalaban , Mohammad Rafiqul Islam , Nian Yao , Lingjiong Zhu

Much recent research has been conducted in the area of Bayesian learning, particularly with regard to the optimization of hyper-parameters via Gaussian process regression. The methodologies rely chiefly on the method of maximizing the…

Machine Learning · Statistics 2014-05-13 James Brofos

A method for correcting smearing effects using machine learning technique is presented. Compared to the standard deconvolution approaches in high energy particle physics, the method can use more than one reconstructed variable to predict…

Data Analysis, Statistics and Probability · Physics 2020-01-30 Bora Işıldak , Alper Hayreter , Aidan R. Wiederhold

STRINGS is a Monte Carlo (MC) event generator for simulating the production and decay of first and second string resonances in proton-proton collisions. STRINGS can also interface with other programs such as Pythia using the Les Houches…

High Energy Physics - Phenomenology · Physics 2025-01-06 Kyle Drury

We present the Monte Carlo event generator {\tt WOPPER} for pair production of $W$'s and their decays at high energy $e^+e^-$ colliders. {\tt WOPPER} includes the effects from finite $W$ width and focusses on the calculation of higher order…

High Energy Physics - Phenomenology · Physics 2009-10-22 H. Anlauf , H. D. Dahmen , P. Manakos , T. Mannel , WOPPER

We present a model for generating spacetime coordinates in the Monte Carlo event generator Herwig 7, and perform colour reconnection by minimizing a boost-invariant distance measure of the system. We compare the model to a series of soft…

High Energy Physics - Phenomenology · Physics 2019-12-20 Johannes Bellm , Cody B Duncan , Stefan Gieseke , Miroslav Myska , Andrzej Siódmok

Ultra-peripheral collisions (UPCs) of heavy ions can be used as a clean environment to study two-photon induced interactions such as dilepton pair photoproduction. Recently, precise data on lepton pair production in UPCs were obtained by…

High Energy Physics - Phenomenology · Physics 2022-05-18 Nazar Burmasov , Evgeny Kryshen , Paul Buehler , Roman Lavicka

We present a new Fortran Monte Carlo generator to simulate black hole events at CERN's Large Hadron Collider. The generator interfaces to the PYTHIA Monte Carlo fragmentation code. The physics of the BH generator includes, but not limited…

High Energy Physics - Phenomenology · Physics 2008-11-26 M. Cavaglia , R. Godang , L. Cremaldi , D. Summers

Since Hamming distances can be calculated by bitwise computations, they can be calculated with less computational load than L2 distances. Similarity searches can therefore be performed faster in Hamming distance space. The elements of…

Machine Learning · Computer Science 2013-03-19 Yui Noma , Makiko Konoshima

We consider nonlinear mixed effects models including high-dimensional covariates to model individual parameters variability. The objective is to identify relevant covariates among a large set under sparsity assumption and to estimate model…

Statistics Theory · Mathematics 2025-08-06 Antoine Caillebotte , Estelle Kuhn , Sarah Lemler