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We discuss recent work on methods for incorporating nonleading QCD corrections in parton shower algorithms.

High Energy Physics - Phenomenology · Physics 2007-05-23 F. Hautmann

Frustrated spin systems generically suffer from the negative sign problem inherent to Monte Carlo methods. Since the severity of this problem is formulation dependent, optimization strategies can be put forward. We introduce a phase pinning…

Strongly Correlated Electrons · Physics 2021-08-18 Toshihiro Sato , Fakher F. Assaad

The EM algorithm is a powerful tool for maximum likelihood estimation with missing data. In practice, the calculations required for the EM algorithm are often intractable. We review numerous methods to circumvent this intractability, all of…

Computation · Statistics 2024-01-03 William Ruth

In many applications, such as plasma edge simulation of a nuclear fusion reactor, a coupled PDE/kinetic description is required, which is usually solved with a coupled finite-volume/Monte-Carlo method. Different procedures have been…

Numerical Analysis · Mathematics 2020-12-17 Bert Mortier , Martine Baelmans , Giovanni Samaey

When parton distributions are falling steeply as the momentum fractions of the partons increases, there are effects that occur at each order in $\alpha_s$ that combine to affect hard scattering cross sections and need to be summed. We show…

High Energy Physics - Phenomenology · Physics 2016-11-03 Zoltan Nagy , Davison E. Soper

We present a new algorithm for an analytic parton shower. While the algorithm for the final-state shower has been known in the literature, the construction of an initial-state shower along these lines is new. The aim is to have a parton…

High Energy Physics - Phenomenology · Physics 2012-04-17 W. Kilian , J. Reuter , S. Schmidt , D. Wiesler

We will show an application of neural networks to extract information on the structure of hadrons. A Monte Carlo over experimental data is performed to correctly reproduce data errors and correlations. A neural network is then trained on…

High Energy Physics - Phenomenology · Physics 2019-08-14 Andrea Piccione , Joan Rojo

We report on re-calculation of the next-to-leading order DGLAP evolution kernels performed in a scheme suited for Monte Carlo simulations of parton cascades (parton showers).

High Energy Physics - Phenomenology · Physics 2016-08-17 A. Kusina , O. Gituliar , S. Jadach , M. Skrzypek

The Fast Fourier Transform (FFT) is widely used in applications such as MRI, CT, and interferometry; however, because of its dependence on uniformly sampled data, it requires the use of gridding techniques for practical implementation. The…

Numerical Analysis · Mathematics 2025-12-22 Federico Achini , Paola Causin , Sara Vanini , Ke Chen , Simone Scacchi

Particle splitting methods are considered for the estimation of rare events. The probability of interest is that a Markov process first enters a set $B$ before another set $A$, and it is assumed that this probability satisfies a large…

Probability · Mathematics 2007-11-14 Thomas Dean , Paul Dupuis

Transverse-momentum dependent parton distributions can be introduced gauge-invariantly in QCD from high-energy factorization. We discuss Monte Carlo applications of these distributions to parton showers and jet physics, with a view to the…

High Energy Physics - Phenomenology · Physics 2009-02-18 F. Hautmann , H. Jung

We present a novel method to combine QCD calculations at next-to-next-to-leading order (NNLO) with parton shower (PS) simulations, that can be applied to the production of heavy systems in hadronic collisions, such as colour singlets or a…

High Energy Physics - Phenomenology · Physics 2022-01-11 Pier Francesco Monni , Paolo Nason , Emanuele Re , Marius Wiesemann , Giulia Zanderighi

We present the general expressions for the resummation, up to next-to-leading logarithmic accuracy, of Sudakov-type logarithms in processes with an arbirtrary number of hard-scattering partons. These results document the formulae used by…

High Energy Physics - Phenomenology · Physics 2010-04-05 R. Bonciani , S. Catani , M. L. Mangano , P. Nason

We introduce an exact Monte Carlo approach to the statistics of discrete quantum systems which does not rely on the standard fragmentation of the imaginary time, or any small parameter. The method deals with discrete objects, kinks,…

Condensed Matter · Physics 2009-10-28 N. V. Prokof'ev , B. V. Svistunov , I. S. Tupitsyn

We present a protocol for Interleaved Randomized Benchmarking of arbitrary quantum gates using Monte Carlo sampling of quantum states. It is generally applicable, including non-Clifford gates while preserving key advantages of Randomized…

Quantum Physics · Physics 2017-07-11 Tobias Chasseur , Daniel M. Reich , Christiane P. Koch , Frank K. Wilhelm

Automatic differentiation (AD) has driven recent advances in machine learning, including deep neural networks and Hamiltonian Markov Chain Monte Carlo methods. Partially observed nonlinear stochastic dynamical systems have proved resistant…

Methodology · Statistics 2024-07-04 Kevin Tan , Giles Hooker , Edward L. Ionides

We introduce a new method to price American-style options on underlying investments governed by stochastic volatility (SV) models. The method does not require the volatility process to be observed. Instead, it exploits the fact that the…

Computational Finance · Quantitative Finance 2012-07-26 Bhojnarine R. Rambharat , Anthony E. Brockwell

Here we present the derivation, description and results of a Monte Carlo-based algorithm for simulating inelastic scattering of photo-electrons when passing through some scattering medium, such as a gas atmosphere or a solid material. The…

Computational Physics · Physics 2021-01-06 Lukas Pielsticker , Robert Schlögl , Mark Greiner

The proportional veto principle, which captures the idea that a candidate vetoed by a large group of voters should not be chosen, has been studied for ranked ballots in single-winner voting. We introduce a version of this principle for…

Computer Science and Game Theory · Computer Science 2025-05-05 Daniel Halpern , Ariel D. Procaccia , Warut Suksompong

We provide details in understanding the Watt spectrum sampling method. The algorithm is given in "R12" from "3rd Monte Carlo Sampler" without detailed derivation. We rederive the algorithm by optimizating the sampling efficiency of the…

Computational Physics · Physics 2024-07-03 Jilang Miao , Miaomiao Jin
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