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The generation of unit-weight events for complex scattering processes presents a severe challenge to modern Monte Carlo event generators. Even when using sophisticated phase-space sampling techniques adapted to the underlying transition…

High Energy Physics - Phenomenology · Physics 2022-05-18 Katharina Danziger , Timo Janßen , Steffen Schumann , Frank Siegert

We present a probabilistic generative model for timing deviations in expressive music performance. The structure of the proposed model is equivalent to a switching state space model. The switch variables correspond to discrete note…

Artificial Intelligence · Computer Science 2011-06-27 A. T. Cemgil , B. Kappen

In this talk the newly developped Monte-Carlo event generator {\tt APACIC++} suitable to describe multijet-events in high-energetic electron-positron annihilations is presented. A new ansatz to match the corresponding matrix elements for…

High Energy Physics - Phenomenology · Physics 2014-11-17 F. Krauss , R. Kuhn , G. Soff

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…

High Energy Physics - Phenomenology · Physics 2012-07-02 Johann Brehmer

We present an FPGA-based study of matrix-element acceleration for Monte Carlo event generation, using MadGraph5_aMC@NLO as a benchmark framework. Two complementary scenarios are considered. First, we implement the full matrix-element…

As the LHC moves to higher energies and luminosity, the demand for computing resources increases accordingly and will soon outpace the growth of the Worldwide LHC Computing Grid. To meet this greater demand, event generation Monte Carlo was…

High Energy Physics - Phenomenology · Physics 2017-01-11 J. T. Childers , T. D. Uram , T. J. LeCompte , M. E. Papka , D. P. Benjamin

We report a novel Monte Carlo scheme that greatly enhances the power of parallel-tempering simulations. In this method, we boost the accumulation of statistical averages by including information about all potential parallel tempering trial…

Statistical Mechanics · Physics 2007-05-23 Ivan Coluzza Daan Frenkel

A Monte Carlo simulator is presented to reproduce data of nucleus-nucleus interactions at high energies. The program is designed in a microscopic point of view, where the cascade approach is applied. Moreover, each nucleon from both the…

High Energy Physics - Phenomenology · Physics 2007-05-23 N. M. Hassan , N. El-Harby , M. T. Hussein

Existing random phase screen (RPS) models for forward multiple-scattering media fail to incorporate ballistic light. In this letter, we redesign the angular spectrum of the screen by means of Monte-Carlo simulation based on an assumption…

Optics · Physics 2020-03-18 Mu Qiao , Xin Yuan

This paper presents implementation details and empirical results for a hybrid message passing and shared memory paralleliziation of the adaptive integral method (AIM). AIM is implemented on a (near) petaflop supercomputing cluster of…

Computational Engineering, Finance, and Science · Computer Science 2010-10-08 Fangzhou Wei , Ali E. Yılmaz

In this paper, we consider an approach to the parallelizing of the algorithms realizing the modified probability changigng method with adaptation and partial rollback procedure for constrained pseudo-Boolean optimization problems. Existing…

Distributed, Parallel, and Cluster Computing · Computer Science 2012-09-03 Lev Kazakovtsev

The pricing of American style and multiple exercise options is a very challenging problem in mathematical finance. One usually employs a Least-Square Monte Carlo approach (Longstaff-Schwartz method) for the evaluation of conditional…

Computational Finance · Quantitative Finance 2011-01-19 Gilles Pagès , Benedikt Wilbertz

The modelling of multiple parton interactions in Monte Carlo event generators is a crucial part not only for the dressing of signal processes but also to describe data with a minimum bias on the event selection. Much work has and will be…

High Energy Physics - Phenomenology · Physics 2020-06-24 Johannes Bellm , Stefan Gieseke , Patrick Kirchgaesser

Monte Carlo event generators are an essential tool for data analysis in collider physics. To include subleading quantum corrections, these generators often need to produce negative weight events, which leads to statistical dilution of the…

High Energy Physics - Phenomenology · Physics 2020-10-21 Benjamin Nachman , Jesse Thaler

Discrepancies play an important role in the study of uniformity properties of point sets. Their probability distributions are a help in the analysis of the efficiency of the Quasi Monte Carlo method of numerical integration, which uses…

High Energy Physics - Phenomenology · Physics 2007-05-23 A. F. W. van Hameren

We present an algorithm for unweighted event generation in the partonic process pp -> WZ (j) with leptonic decays at next-to-leading order in alpha_S. Monte Carlo programs for processes such as this frequently generate events with negative…

High Energy Physics - Phenomenology · Physics 2009-10-31 M. Dobbs , M. Lefebvre

A technique for reducing the number of integrals in a Monte Carlo calculation is introduced. For integrations relying on classical or mean-field trajectories with local weighting functions, it is possible to integrate analytically at least…

Statistical Mechanics · Physics 2024-05-17 Jarod Tall , Steven Tomsovic

A new Monte Carlo program for photohadronic interactions of relativistic nucleons with an ambient photon radiation field is presented. The event generator is designed to fulfil typical astrophysical requirements, but can also be used for…

Astrophysics · Physics 2011-05-05 A. Muecke , Ralph Engel , J. P. Rachen , R. J. Protheroe , Todor Stanev

Embarrassingly parallel Markov Chain Monte Carlo (MCMC) exploits parallel computing to scale Bayesian inference to large datasets by using a two-step approach. First, MCMC is run in parallel on (sub)posteriors defined on data partitions.…

Machine Learning · Statistics 2022-03-31 Daniel Augusto de Souza , Diego Mesquita , Samuel Kaski , Luigi Acerbi

The Dirichlet process (DP) is a fundamental mathematical tool for Bayesian nonparametric modeling, and is widely used in tasks such as density estimation, natural language processing, and time series modeling. Although MCMC inference…

Machine Learning · Statistics 2013-04-09 Dan Lovell , Jonathan Malmaud , Ryan P. Adams , Vikash K. Mansinghka
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