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Field-level inference provides a means to optimally extract information from upcoming cosmological surveys, but requires efficient sampling of a high-dimensional parameter space. This work applies Microcanonical Langevin Monte Carlo (MCLMC)…

Cosmology and Nongalactic Astrophysics · Physics 2023-07-20 Adrian E. Bayer , Uros Seljak , Chirag Modi

We review progress in calculating one-loop scattering amplitudes required for next-to-leading-order corrections to QCD processes. The underlying technical developments include the spinor helicity formalism, color decompositions,…

High Energy Physics - Phenomenology · Physics 2010-11-15 Z. Bern , L. Dixon , D. A. Kosower

Markov chain Monte Carlo (MCMC) algorithms are ubiquitous in Bayesian computations. However, they need to access the full data set in order to evaluate the posterior density at every step of the algorithm. This results in a great…

Machine Learning · Statistics 2016-09-21 Mike Giles , Tigran Nagapetyan , Lukasz Szpruch , Sebastian Vollmer , Konstantinos Zygalakis

In this work we develop a new hierarchical multilevel approach to generate Gaussian random field realizations in an algorithmically scalable manner that is well-suited to incorporate into multilevel Markov chain Monte Carlo (MCMC)…

Numerical Analysis · Mathematics 2021-03-05 Hillary R. Fairbanks , Umberto Villa , Panayot S. Vassilevski

We examine a new 2nd order integrator recently found by Omelyan et al. The integration error of the new integrator measured in the root mean square of the energy difference, $\bra\Delta H^2\ket^{1/2}$, is about 10 times smaller than that of…

High Energy Physics - Lattice · Physics 2011-05-25 Tetsuya Takaishi , Philippe de Forcrand

Modern deep learning systems are typically deployed as open-loop function approximators: they map inputs to outputs in a single pass, without regulating how much computation or explanatory effort is spent on a given case. In safety-critical…

Machine Learning · Computer Science 2025-12-02 Vishal Joshua Meesala

On the basis of the exact formulas of QCD perturbation theory and parton model the hadronic production cross-sections for $B_c({B_c}^*)$-mesons ($1^1S_0,1^3S_1,2^1S_0,2^3S_1$-states) are calculated. The method used is the direct calculation…

High Energy Physics - Phenomenology · Physics 2010-11-01 A. V. Berezhnoy , A. K. Likhoded , M. V. Shevlyagin

We present a study of four top quark production at the LHC in the Standard Model Effective Field Theory (SMEFT). The analysis is performed at the tree-level, including all terms from the mixed QCD-EW-coupling cross-section expansion and all…

High Energy Physics - Phenomenology · Physics 2022-11-24 Hesham El Faham

High Energy Jets provides an all-order description of wide-angle QCD emissions, resumming the leading-logarithmic contributions in the high-energy limit. In this contribution, we briefly summarise the approach and its implementation in a…

High Energy Physics - Phenomenology · Physics 2012-09-20 Jennifer M. Smillie

We congratulate Samuel Kou, Qing Zhou and Wing Wong [math.ST/0507080] (referred to subsequently as KZW) for this beautifully written paper, which opens a new direction in Monte Carlo computation. This discussion has two parts. First, we…

Statistics Theory · Mathematics 2016-08-16 Yves F. Atchadé , Jun S. Liu

Hyperdimensional Computing (HDC) is a computation framework based on properties of high-dimensional random spaces. It is particularly useful for machine learning in resource-constrained environments, such as embedded systems and IoT, as it…

Machine Learning · Computer Science 2022-05-18 Igor Nunes , Mike Heddes , Tony Givargis , Alexandru Nicolau

We generalize the multilevel Monte Carlo (MLMC) method of Giles to the simulation of systems of particles that interact via a mean field. When the number of particles is large, these systems are described by a McKean-Vlasov process - a…

Numerical Analysis · Mathematics 2015-08-11 L. F. Ricketson

The problem of optimising functions with intractable gradients frequently arise in machine learning and statistics, ranging from maximum marginal likelihood estimation procedures to fine-tuning of generative models. Stochastic approximation…

Machine Learning · Statistics 2026-01-30 James Cuin , Davide Carbone , Yanbo Tang , O. Deniz Akyildiz

A new next-to-leading order Monte Carlo program for calculation of fully differential single top quark final states is described and first results presented. Both the s- and t-channel contributions are included.

High Energy Physics - Phenomenology · Physics 2009-11-07 B. W. Harris , E. Laenen , L. Phaf , Z. Sullivan , S. Weinzierl

The matrix element (ME) calculation in any Monte Carlo physics event generator is an ideal fit for implementing data parallelism with lockstep processing on GPUs and vector CPUs. For complex physics processes where the ME calculation is the…

Array operations are one of the most concise ways of expressing common filtering and simple aggregation operations that is the hallmark of the first step of a particle physics analysis: selection, filtering, basic vector operations, and…

Databases · Computer Science 2021-09-08 Gordon Watts

Stochastic partial differential equations (SPDEs) are often difficult to solve numerically due to their low regularity and high dimensionality. These challenges limit the practical use of computer-aided studies and pose significant barriers…

Numerical Analysis · Mathematics 2025-02-04 Abdul-Lateef Haji-Ali , Håkon Hoel , Andreas Petersson

We introduce an improved version of the simulation code FEWZ (Fully Exclusive W and Z Production) for hadron collider production of lepton pairs through the Drell-Yan process at next-to-next-to-leading-order (NNLO) in the strong coupling…

High Energy Physics - Phenomenology · Physics 2011-08-04 Ryan Gavin , Ye Li , Frank Petriello , Seth Quackenbush

In Hybrid Monte Carlo(HMC) simulations for full QCD, the gauge fields evolve smoothly as a function of Molecular Dynamics (MD) time. Thus we investigate improved methods of estimating the trial solutions to the Dirac propagator as…

High Energy Physics - Lattice · Physics 2009-10-28 R. C. Brower , A. R. Levi , K. Orginos

We introduce the energy-stepping Monte Carlo (ESMC) method, a Markov chain Monte Carlo (MCMC) algorithm based on the conventional dynamical interpretation of the proposal stage but employing an energy-stepping integrator. The…

Mathematical Physics · Physics 2023-12-13 Ignacio Romero , Michael Ortiz