相关论文: Solving constrained Markovian evolution in QCD wit…
We revisit the challenging problem of finding an efficient Monte Carlo (MC) algorithm solving the constrained evolution equations for the initial-state QCD radiation. The type of the parton (quark, gluon) and the energy fraction x of the…
A new class of the constrained Monte Carlo (CMC) algorithms for the QCD evolution equation was recently discovered. The constraint is imposed on the type and the total longitudinal energy of the parton exiting QCD evolution and entering a…
This work covers methodology of solving QCD evolution equation of the parton distribution using Markovian Monte Carlo (MMC) algorithms in a class of models ranging from DGLAP to CCFM. One of the purposes of the above MMCs is to test the…
With the imminent start of LHC experiments, development of phenomenological tools, and in particular the Monte Carlo programs and algorithms, becomes urgent. A new algorithm for the generation of a parton shower initiated by the single…
We present the exact and precise (~0.1%) numerical solution of the QCD evolution equations for the parton distributions in a wide range of $Q$ and $x$ using Monte Carlo (MC) method, which relies on the so-called Markovian algorithm. We…
We present the program EvolFMC v.2 that solves the evolution equations in QCD for the parton momentum distributions by means of the Monte Carlo technique based on the Markovian process. The program solves the DGLAP-type evolution as well as…
A general formalism was introduced for reorganization of QCD evolution equations and derivation of hierarchical solution to DGLAP equation. The hierarchical solution separates two types of parton emissions: the flavour changing emissions…
We discuss precision Monte Carlo (MC) calculations for solving the QCD evolution equations up to the next-to-leading-order (NLO) level. They employ forward Markovian Monte Carlo algorithms, which provide rigorous solutions of the above…
A systematic extension of the Monte Carlo (MC) algorithm, that solves the DGLAP equation, into the so-called the one-loop CCFM evolution is presented. Modifications are related to a z-dependent coupling constant; transverse momentum…
We present precision Monte Carlo calculations solving the QCD evolution equations up to the next-to-leading-order (NLO) level. They employ forward Markovian Monte Carlo (FMC) algorithms, which provide the rigorous solutions of the QCD…
The task of Monte Carlo simulation of the evolution of the parton distributions in QCD and of constructing new parton shower Monte Carlo algorithms requires new way of organizing solutions of the QCD evolution equations, in which…
We are reporting on the ongoing effort of the Monte Carlo (MC) modelling of NLO DGLAP QCD evolution in the fully unintegrated form. The resulting parton shower MC is performing on its own the NLO QCD evolution, contrary to all known…
We present two Monte Carlo algorithms of the Markovian type which solve the modified QCD evolution equations at the NLO level. The modifications with respect to the standard DGLAP evolution concern the argument of the strong coupling…
Presently available perturbative QCD calculations combining hard process matrix element with the Parton Shower Monte Carlo programs feature hard process matrix element calculated often beyond the leading order (LO), that is including…
The Markov Chain Monte Carlo (MCMC) algorithm is a widely recognised as an efficient method for sampling a specified posterior distribution. However, when the posterior is multi-modal, conventional MCMC algorithms either tend to become…
The next-to-leading order (NLO) evolution of the parton distribution functions (PDF's) in QCD is the "industry standard" in the lepton-hadron and hadron-hadron collider data analysis. The standard NLO DGLAP evolution is formulated for…
A novel class of non-reversible Markov chain Monte Carlo schemes relying on continuous-time piecewise-deterministic Markov Processes has recently emerged. In these algorithms, the state of the Markov process evolves according to a…
Many problems in the physical sciences, machine learning, and statistical inference necessitate sampling from a high-dimensional, multi-modal probability distribution. Markov Chain Monte Carlo (MCMC) algorithms, the ubiquitous tool for this…
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…
We propose a Markov Chain Monte Carlo (MCMC) algorithm based on Gibbs sampling with parallel tempering to solve nonlinear optimal control problems. The algorithm is applicable to nonlinear systems with dynamics that can be approximately…