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Related papers: Gaining analytic control of parton showers

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Sequential Monte Carlo methods, also known as particle methods, are a popular set of techniques for approximating high-dimensional probability distributions and their normalizing constants. These methods have found numerous applications in…

Computation · Statistics 2021-06-23 Jeremy Heng , Adrian N. Bishop , George Deligiannidis , Arnaud Doucet

It is widely believed that the prediction accuracy of decision tree models is invariant under any strictly monotone transformation of the individual predictor variables. However, this statement may be false when predicting new observations…

Machine Learning · Statistics 2016-11-16 Tal Galili , Isaac Meilijson

This paper develops a unified methodology for probabilistic analysis and optimal control design for jump diffusion processes defined by polynomials. For such systems, the evolution of the moments of the state can be described via a system…

Optimization and Control · Mathematics 2017-02-03 Andrew Lamperski , Khem Raj Ghusinga , Abhyudai Singh

We present a Monte Carlo simulation of the perturbative Quantum Chromodynamics (pQCD) shower developing after a hard process embedded in a heavy-ion collision. The main assumption is that the cascade of branching partons traverses a medium…

High Energy Physics - Phenomenology · Physics 2008-11-26 Thorsten Renk

Power-law distributions are typical macroscopic features occurring in almost all complex systems observable in nature. As a result, researchers in quantitative analyses must often generate random synthetic variates obeying power-law…

Physics and Society · Physics 2014-11-11 Filippo Radicchi

Statistical postprocessing is used to translate ensembles of raw numerical weather forecasts into reliable probabilistic forecast distributions. In this study, we examine the use of permutation-invariant neural networks for this task. In…

Machine Learning · Statistics 2024-01-22 Kevin Höhlein , Benedikt Schulz , Rüdiger Westermann , Sebastian Lerch

Practitioners monitoring deployed probabilistic models face a fundamental trap: any fixed-sample test applied repeatedly over an unbounded stream will eventually raise a false alarm, even when the model remains perfectly stable. Existing…

Methodology · Statistics 2026-03-16 Tristan Farran

Stochastic filtering refers to estimating the probability distribution of the latent stochastic process conditioned on the observed measurements in time. In this paper, we introduce a new class of convergent filters that represent the…

Methodology · Statistics 2023-03-27 Zheng Zhao , Juha Sarmavuori

We develop a potential algorithm to relate the depth development of ultra high energy extensive air showers and the time delay for individual muons. The time distributions sampled at different positions at ground level by a large air shower…

Astrophysics · Physics 2009-11-10 L. Cazon , R. A. Vazquez , E. Zas

In this work we present a new subtraction method for next-to-leading order calculations that is particularly convenient even when narrow resonances are present. The method is particularly suitable for the implementation of next-to-leading…

High Energy Physics - Phenomenology · Physics 2016-01-27 Tomáš Ježo , Paolo Nason

The leading-order accurate description of top quark pair production, as usually employed in standard Monte Carlo event generators, gives no rise to the generation of a forward--backward asymmetry. Yet, non-negligible -- differential as well…

High Energy Physics - Phenomenology · Physics 2015-06-15 Jan Winter , Peter Z. Skands , Bryan R. Webber

We quantify uncertainties in the Monte-Carlo simulation of inclusive and dijet final states, which arise from using the MC@NLO technique for matching next-to-leading order parton level calculations and parton showers. We analyse a large…

High Energy Physics - Phenomenology · Physics 2013-11-18 Stefan Hoeche , Marek Schonherr

Probabilistic programs are typically normal-looking programs describing posterior probability distributions. They intrinsically code up randomized algorithms and have long been at the heart of modern machine learning and approximate…

Programming Languages · Computer Science 2023-02-14 Lutz Klinkenberg , Tobias Winkler , Mingshuai Chen , Joost-Pieter Katoen

This paper sets out a forecasting method that employs a mixture of parametric functions to capture the pattern of fertility with respect to age. The overall level of cohort fertility is decomposed over the range of fertile ages using a…

Applications · Statistics 2019-09-23 Jason Hilton , Erengul Dodd , Jonathan J. Forster , Peter W. F. Smith , Jakub Bijak

We consider $W^\pm Z$ production in hadronic collisions and present high-precision predictions in QCD and electroweak (EW) perturbation theory matched to parton showers. To this end, we match next-to-next-to-leading order QCD corrections to…

High Energy Physics - Phenomenology · Physics 2022-11-30 Jonas M. Lindert , Daniele Lombardi , Marius Wiesemann , Giulia Zanderighi , Silvia Zanoli

We present the implementation and validation of the techniques used to efficiently evaluate parametric and perturbative theoretical uncertainties in matrix-element plus parton-shower simulations within the Sherpa event-generator framework.…

High Energy Physics - Phenomenology · Physics 2016-11-11 Enrico Bothmann , Marek Schönherr , Steffen Schumann

We formulate PanScales parton showers for hadron collisions so as to achieve next-to-leading logarithmic (NLL) accuracy across a broad set of observables. We do so specifically for colour singlet production. Relative to the existing…

High Energy Physics - Phenomenology · Physics 2022-11-23 Melissa van Beekveld , Silvia Ferrario Ravasio , Gavin P. Salam , Alba Soto-Ontoso , Gregory Soyez , Rob Verheyen

We present a formalism for a fully coherent QED parton shower. The complete multipole structure of photonic radiation is incorporated in a single branching kernel. The regular on-shell 2 to 3 kinematic picture is kept intact by dividing the…

High Energy Physics - Phenomenology · Physics 2018-01-17 Ronald Kleiss , Rob Verheyen

Skewness and kurtosis are fundamental statistical moments commonly used to quantify asymmetry and tail behavior in probability distributions. Despite their widespread application in statistical mechanics, condensed matter physics, and…

Mathematical Physics · Physics 2025-06-23 Carlo De Michele , Samuele De Bartolo

In search and recommendation systems, predictive models often suffer from temporal instability when certain input features introduce volatility in output scores. This instability can degrade model reliability and user experience especially…

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