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Register a new userA Positive Resampler for Monte Carlo Events with Negative Weights
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We propose the Positive Resampler to solve the problem associated with event samples from state-of-the-art predictions for scattering processes at hadron colliders typically involving a sizeable number of events contributing with negative weight. The proposed method guarantees positive weights for all physical distributions, and a correct description of all observables. A desirable side product of the method is the possibility to reduce the size of event samples produced by General Purpose Event Generators, thus lowering the resource demands for subsequent computing-intensive event processing steps. We demonstrate the viability and efficiency of our approach by considering its application to a next-to-leading order + parton shower merged prediction for the production of a $W$ boson in association with multiple jets.
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We propose a novel method for the elimination of negative Monte Carlo event weights. The method is process-agnostic, independent of any analysis, and preserves all physical observables. We demonstrate the overall performance and systematic improvement with increasing event sample size, based on predictions for the production of a W boson with two jets calculated at next-to-leading order perturbation theory.
VBFNLO is a flexible parton level Monte Carlo program for the simulation of vector boson fusion (VBF), QCD induced single and double vector boson production plus two jets, and double and triple vector boson production (plus jet) in hadronic collisions at next-to-leading order (NLO) in the strong coupling constant, as well as Higgs boson plus two jet production via gluon fusion at the one-loop level. For the new version -- Version 2.7.0 -- several major enhancements have been included into VBFNLO. The following new production processes have been added: $Wgamma jj$ in VBF, $HHjj$ in VBF, $W$, $Wj$, $WH$, $WHj$, $ppto text{Spin-2}jj$ in VBF (with $text{Spin-2}to WW/ZZtotext{leptons}$) and the QCD induced processes $WZjj$, $Wgamma jj$, $W^pm W^pm jj$ and $Wjj$ production. The implementation of anomalous gauge boson couplings has been extended to all triboson and VBF $VVjj$ processes, with an enlarged set of operators yielding anomalous couplings. Finally, semileptonic decay modes of the vector bosons are now available for many processes, including $VVjj$ in VBF, $VVV$ and $VVgamma$ production.
The algorithm for Monte Carlo simulation of parton-level events based on an Artificial Neural Network (ANN) proposed in arXiv:1810.11509 is used to perform a simulation of $Hto 4ell$ decay. Improvements in the training algorithm have been implemented to avoid numerical instabilities. The integrated decay width evaluated by the ANN is within 0.7% of the true value and unweighting efficiency of 26% is reached. While the ANN is not automatically bijective between input and output spaces, which can lead to issues with simulation quality, we argue that the training procedure naturally prefers bijective maps, and demonstrate that the trained ANN is bijective to a very good approximation.
A fast leading-order Monte Carlo generator for the process $e^+e^-tomu^+mu^-gamma$ is described. In fact, using the $e^+e^-tomu^+mu^-gamma $ process as an example, we provide a pedagogical demonstration of how a Monte Carlo generator can be created from scratch. The $e^+ e^- to mu^+ mu^- gamma$ process was chosen, since in this case we are not faced with either too trivial or too difficult a task. Matrix elements are calculated using the helicity amplitude method. Monte Carlo algorithm uses the acceptance-rejection method with an appropriately chosen simplified distribution that can be generated using an efficient algorithm. We provide a detailed pedagogical exposition of both the helicity amplitude method and the Monte Carlo technique, which we hope will be useful for high energy physics students.
We present a new strategy using artificial intelligence (AI) to build the first AI-based Monte Carlo event generator (MCEG) capable of faithfully generating final state particle phase space in lepton-hadron scattering. We show a blueprint for integrating machine learning strategies with calibrated detector simulations to build a vertex-level, AI-based MCEG, free of theoretical assumptions about femtometer scale physics. As the first steps towards this goal, we present a case study for inclusive electron-proton scattering using synthetic data from the PYTHIA MCEG for testing and validation purposes. Our quantitative results validate our proof of concept and demonstrate the predictive power of the trained models. The work suggests new venues for data preservation to enable future QCD studies of hadrons structure, and the developed technology can boost the science output of physics programs at facilities such as Jefferson Lab and the future Electron-Ion Collider.
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