Related papers: Fast evaluation of theoretical uncertainties with …
The extension of interpolation-grid frameworks for perturbative QCD calculations at next-to-next-to-leading order (NNLO) is presented for deep inelastic scattering (DIS) processes. A fast and flexible evaluation of higher-order predictions…
Any NLO calculation of a QCD final-state observable involves Monte Carlo integration over a large number of events. For DIS and hadron colliders this must usually be repeated for each new PDF set, making it impractical to consider many…
MCgrid is a software package that provides access to the APPLgrid interpolation tool for Monte Carlo event generator codes, allowing for fast and flexible variations of scales, coupling parameters and PDFs in cutting edge leading and…
We present the interface between MadGraph5_aMC@NLO, a self-contained program that calculates cross sections up to next-to-leading order accuracy in an automated manner, and APPLgrid, a code that parametrises such cross sections in the form…
Standard methods for higher-order calculations of QCD cross sections in hadron-induced collisions are time-consuming. The fastNLO project uses multi-dimensional interpolation techniques to convert the convolutions of perturbative…
Precise theoretical predictions are vital for the interpretation of Standard Model measurements and facilitate conclusive searches for New Physics phenomena at the LHC. In this contribution I highlight some of the ongoing efforts in the…
We present an interface between PineAPPL and Matrix, which allows fully differential cross sections to be calculated in the form of interpolation grids, accurate at next-to-next-to-leading order (NNLO) in QCD and next-to-leading order in…
Updated predictions are presented for high energy neutrino and antineutrino charged and neutral current cross-sections within the conventional DGLAP formalism of NLO QCD using modern PDF fits. PDF uncertainties from model assumptions and…
We present a method for very fast repeated computations of higher-order cross sections in hadron-induced processes for arbitrary parton density functions. A full implementation of the method for computations of jet cross sections in…
Without proper control of numerical and methodological errors in theoretical predictions at the per mille level it is not possible to study the effect of input parameters in current hadron-collider measurements at the required precision. We…
We present a determination of the strong coupling $\alpha_s(m_Z)$ from a global dataset including both fixed-target and collider data from deep-inelastic scattering and a variety of hadronic processes, with a simultaneous determination of…
Sherpa is a hyperparameter optimization library for machine learning models. It is specifically designed for problems with computationally expensive, iterative function evaluations, such as the hyperparameter tuning of deep neural networks.…
Poor computing efficiency of precision event generators for LHC physics has become a bottleneck for Monte-Carlo event simulation campaigns. We provide solutions to this problem by focusing on two major components of general-purpose event…
The use of machine learning algorithms in theoretical and experimental high-energy physics has experienced an impressive progress in recent years, with applications from trigger selection to jet substructure classification and detector…
We release fastNLO tables with NNLO QCD top-quark pair differential distributions corresponding to 8 TeV ATLAS [Eur. Phys. J. C 76 538 (2016)] and CMS [Eur. Phys. J. C 75 542 (2015)] measurements. This is the first time fastNLO tables with…
Evaluating parton density systematic uncertainties in Monte~Carlo event generator predictions has long been achieved by reweighting between the original and systematic PDFs for the initial state configurations of the individual simulated…
Machine learning has recently been widely adopted to address the managerial decision making problems, in which the decision maker needs to be able to interpret the contributions of individual attributes in an explicit form. However, there…
Many next-to-leading order QCD predictions are available through Monte Carlo (MC) simulations. Usually, multiple CPU hours are needed to calculate predictions at a required precision, which is unfeasible for global PDF analyses. This…
The parameters in Monte Carlo (MC) event generators are tuned on experimental measurements by evaluating the goodness of fit between the data and the MC predictions. The relative importance of each measurement is adjusted manually in an…
We introduce new efficient integral representations and methods for evaluation of pdfs, cpds and quantiles of stable distributions. For wide regions in the parameter space, absolute errors of order $10^{-15}$ can be achieved in 0.005-0.1…