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Related papers: SANC integrator in the progress: QCD and EW contri…

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The new version of the SANCphot integrator has been prepared for fast and stable numerical calculations up to two loops for polarized light-by-light scattering. One-loop modules based on the helicity formalism with massive particles and…

High Energy Physics - Phenomenology · Physics 2024-09-17 Serge Bondarenko , Aidos Issadykov , Lidia Kalinovskaya , Andrey Sapronov , Diana Seitova

The calculation of NLO QCD corrections to the $t\bar{t}\to W^{+}W^{-}b\bar{b}\to e^{+}\nu_e \mu^{-}\bar{\nu}_{\mu}b\bar{b}$ process with complete off-shell effects, is briefly summarized. Besides the total cross section and its scale…

High Energy Physics - Phenomenology · Physics 2011-11-24 Malgorzata Worek

We propose a multilevel Markov chain Monte Carlo (MCMC) method for the Bayesian inference of random field parameters in PDEs using high-resolution data. Compared to existing multilevel MCMC methods, we additionally consider level-dependent…

Numerical Analysis · Mathematics 2025-08-19 Pieter Vanmechelen , Geert Lombaert , Giovanni Samaey

Three alternative designs of the European Spallation Source (ESS) high energy linac are described. The most promising ones are either a normalconducting (nc) coupled cavity linac (CCL) up tofinal energy or a change at 407 MeV to only one…

Accelerator Physics · Physics 2007-05-23 M. Pabst , K. Bongardt , A. Letchford

In this contribution we give a short overview of the new Coherent Exclusive Exponentiation (CEEX) which is implemented in the new \KK MC event generator for the process $e^+e^-\to f\bar{f} +n\gamma$, $f=\mu,\tau,d,u,s,c,b$ with validity for…

High Energy Physics - Phenomenology · Physics 2009-10-31 S. Jadach , B. F. L. Ward , S. Was

SecDec is a program which can be used for the factorization of dimensionally regulated poles from parametric integrals, in particular multi-loop integrals, and the subsequent numerical evaluation of the finite coefficients. Here we present…

High Energy Physics - Phenomenology · Physics 2015-09-22 S. Borowka , G. Heinrich , S. P. Jones , M. Kerner , J. Schlenk , T. Zirke

We describe recent results obtained as part of the High Energy Monte Carlo Grand Challenge (HEMCGC) project concerning the behaviour of lattice QCD with light dynamical Wilson quarks. We show that it is possible to reach regions of…

High Energy Physics - Lattice · Physics 2007-05-23 A. D. Kennedy , R. G. Edwards

This contribution lists challenges of Monte Carlo event generators for future lepton, especially linear colliders. A lot of the recent development benefits from the achievements at the Large Hadron Collider (LHC), but several aspects are…

High Energy Physics - Phenomenology · Physics 2024-12-19 Jürgen Reuter

This letter considers cascaded model predictive control (MPC) as a computationally lightweight method for controlling a tandem-rotor helicopter. A traditional single MPC structure is split into separate outer and inner-loops. The outer-loop…

Optimization and Control · Mathematics 2023-06-30 Faraaz Ahmed , Ludwik Sobiesiak , James Richard Forbes

Event generators simulate particle interactions using Monte Carlo techniques, providing the primary connection between experiment and theory in experimental high energy physics. These software packages, which are the first step in the…

We present the computation of the differential cross section for the process $pp(\bar{p}) \to (W^+\,W^-\,b\,\bar{b} \to)\;e^+\,\nu_e\,\mu^-\,\bar{\nu}_\mu\,b\, \bar{b}+X$ at NLO QCD accuracy matched to Shower Monte Carlo (SMC) simulations…

High Energy Physics - Phenomenology · Physics 2014-08-22 M. V. Garzelli , A. Kardos , Z. Trocsanyi

The computational cost of traditional first-principles method quickly becomes prohibitively expensive as the number of atoms increases. This challenge is further amplified by the need to evaluate finite-temperature properties with Monte…

Materials Science · Physics 2025-08-13 Xianglin Liu , Kai Yang , Yongxiang Liu , Fanli Zhou , Dengdong Fan , Zongrui Pei , Pengxiang Xu , Yonghong Tian

This book aims to provide a graduate-level introduction to advanced topics in Markov chain Monte Carlo (MCMC) algorithms, as applied broadly in the Bayesian computational context. Most, if not all of these topics (stochastic gradient MCMC,…

Machine Learning · Statistics 2024-07-18 Paul Fearnhead , Christopher Nemeth , Chris J. Oates , Chris Sherlock

We propose a data-driven Model Predictive Control (MPC) framework that employs a transformer encoder to generate multi-step predictions. To handle the nonconvex attention mechanism, we derive difference of convex (DC) representations of the…

Optimization and Control · Mathematics 2026-05-15 Xingxiao Chen , Mark Cannon

Nested integration problems arise in various scientific and engineering applications, including Bayesian experimental design, financial risk assessment, and uncertainty quantification. These nested integrals take the form $\int f\left(\int…

Numerical Analysis · Mathematics 2025-06-17 Arved Bartuska , André Gustavo Carlon , Luis Espath , Sebastian Krumscheid , Raúl Tempone

The interpretation of experimental measurements at the LHC requires accurate theoretical predictions for exclusive observables, and in particular the summation of soft and collinear radiation to all orders in perturbation theory. We report…

High Energy Physics - Phenomenology · Physics 2015-02-13 Michael Czakon , Michael Krämer , Malgorzata Worek

Sequential Monte Carlo (SMC) methods are a class of techniques to sample approximately from any sequence of probability distributions using a combination of importance sampling and resampling steps. This paper is concerned with the…

Statistics Theory · Mathematics 2012-03-05 Pierre Del Moral , Arnaud Doucet , Ajay Jasra

The standard hybrid Monte Carlo algorithm uses the second order integrator at the molecular dynamics step. This choice of the integrator is not always the best. Using the Wilson fermion action, we study the performance of the hybrid Monte…

High Energy Physics - Lattice · Physics 2009-11-07 Tetsuya Takaishi

Particle Markov Chain Monte Carlo (PMCMC) is a general computational approach to Bayesian inference for general state space models. Our article scales up PMCMC in terms of the number of observations and parameters by generating the…

Methodology · Statistics 2023-07-04 David Gunawan , Chris Carter , Robert Kohn