English
Related papers

Related papers: Markovian MC simulation of QCD evolution at NLO le…

200 papers

Optimal control in non-stationary Markov decision processes (MDP) is a challenging problem. The aim in such a control problem is to maximize the long-term discounted reward when the transition dynamics or the reward function can change over…

Applications · Statistics 2017-03-03 Taposh Banerjee , Miao Liu , Jonathan P. How

We compare the integration error of Monte Carlo (MC) and quasi-Monte Carlo (QMC) methods for approximating the normalizing constant of posterior distributions and certain marginal likelihoods. In doing so, we characterize the dependency of…

Statistics Theory · Mathematics 2025-06-30 Yanbo Tang

We consider the problem of model reduction for Markovian quantum systems whose dynamics are described by a time-dependent Lindblad generator -- notably, as arising in the presence of external control. Our approach, which builds upon Krylov…

Quantum Physics · Physics 2025-10-30 Tommaso Grigoletto , Lorenza Viola , Francesco Ticozzi

The Abelian decomposition of QCD reveals two types of gluons: color-neutral ``neurons" and color-carrying ``chromons". This classification does not alter the overall properties of QCD, but the investigation of different types of gluon…

High Energy Physics - Phenomenology · Physics 2024-01-23 Yirui Yang , Wei Kou , Xiaopeng Wang , Yanbing Cai , Xurong Chen

We discuss prospects for Monte Carlo event generators incorporating the dynamics of transverse momentum dependent (TMD) parton distribution functions. We illustrate TMD evolution in the parton branching formalism, and present Monte Carlo…

High Energy Physics - Phenomenology · Physics 2019-07-09 F. Hautmann

We present a parton shower which implements the DGLAP evolution of parton densities and fragmentation functions at next-to-leading order precision up to effects stemming from local four-momentum conservation. The Monte-Carlo simulation is…

High Energy Physics - Phenomenology · Physics 2017-11-01 Stefan Höche , Frank Krauss , Stefan Prestel

This study proposes a trainable sampling-based solver for combinatorial optimization problems (COPs) using a deep-learning technique called deep unfolding. The proposed solver is based on the Ohzeki method that combines Markov-chain…

Disordered Systems and Neural Networks · Physics 2024-05-03 Ryo Hagiwara , Satoshi Takabe

Based on a new coupling approach, we prove that the transition step of the Hamiltonian Monte Carlo algorithm is contractive w.r.t. a carefully designed Kantorovich (L1 Wasserstein) distance. The lower bound for the contraction rate is…

Probability · Mathematics 2020-07-30 Nawaf Bou-Rabee , Andreas Eberle , Raphael Zimmer

We present a scalable and effective exploration strategy based on Thompson sampling for reinforcement learning (RL). One of the key shortcomings of existing Thompson sampling algorithms is the need to perform a Gaussian approximation of the…

Machine Learning · Computer Science 2024-03-19 Haque Ishfaq , Qingfeng Lan , Pan Xu , A. Rupam Mahmood , Doina Precup , Anima Anandkumar , Kamyar Azizzadenesheli

We address the problem of decomposing graphs in perturbative QCD into terms associated with particular regions. Motivated by asking how to incorporate next-to-leading order (NLO) QCD corrections in parton-shower algorithms, we require that:…

High Energy Physics - Phenomenology · Physics 2010-02-03 J. C. Collins , F. Hautmann

The $Q^2$ dependence of the ratios of the cross sections of deep inelastic lepton--nucleus scattering is studied in the framework of leading twist, lowest order perturbative QCD. The $\log Q^2$ slope of the ratio $F_2^{\rm Sn}/F_2^{\rm C}$…

High Energy Physics - Phenomenology · Physics 2017-08-23 K. J. Eskola , H. Honkanen , V. J. Kolhinen , C. A. Salgado

We study how the rapidity evolution of gluon transverse momentum dependent distribution changes from nonlinear evolution at small $x\ll 1$ to linear double-logarithmic evolution at moderate $x\sim 1$.

High Energy Physics - Phenomenology · Physics 2015-06-23 I. Balitsky , A. Tarasov

We present explicit evaluations of quantum speed limit times pertinent to the Markovian dynamics of an open continuous-variable system. Specifically, we consider the standard setting of a cavity mode of the quantum radiation field weakly…

Quantum Physics · Physics 2021-07-05 Paulina Marian , Tudor A. Marian

Seven next-to-leading order QCD evolution programs are compared. The deviations of the results due to different theoretical prescriptions for truncating the perturbative series are clarified, and a numerical agreement between five codes of…

High Energy Physics - Phenomenology · Physics 2007-05-23 J. Blümlein , M. Botje , C. Pascaud , S. Riemersma , W. L. van Neerven , A. Vogt , F. Zomer

We implement a discretization of the one-dimensional branching Brownian motion in the form of a Monte Carlo event generator, designed to efficiently produce ensembles of realizations in which the rightmost lead particle at the final time…

Statistical Mechanics · Physics 2020-06-30 Anh Dung Le , Alfred H. Mueller , Stéphane Munier

We propose a method for matching the next-to-leading order (NLO) calculation of a given QCD process with a parton shower Monte Carlo (MC) simulation. The method has the following features: fully exclusive events are generated, with…

High Energy Physics - Phenomenology · Physics 2010-11-11 S. Frixione , B. R. Webber

We derive a Markovian master equation that models the evolution of systems subject to driving and control fields. Our approach combines time rescaling and weak-coupling limits for the system-environment interaction with a secular…

Quantum Physics · Physics 2024-11-26 Giovanni Di Meglio , Martin B. Plenio , Susana F. Huelga

We consider modifications of the standard non-linear QCD evolution in an attempt to account for some of the missing ingredients discussed recently, such as correlations, discreteness in gluon emission and Pomeron loops. The evolution is…

High Energy Physics - Phenomenology · Physics 2008-11-26 N. Armesto , J. G. Milhano

Kinetic Monte Carlo (KMC) is an important computational tool in physics and chemistry. In contrast to standard Monte Carlo, KMC permits the description of time dependent dynamical processes and is not restricted to systems in equilibrium.…

Computational Physics · Physics 2020-04-22 William Robert Saunders , James Grant , Eike Hermann Müller , Ian Thompson

Sampling from log-concave distributions is a well researched problem that has many applications in statistics and machine learning. We study the distributions of the form $p^{*}\propto\exp(-f(x))$, where…

Machine Learning · Computer Science 2019-09-13 Ruoqi Shen , Yin Tat Lee