Event generation with normalizing flows
TL;DR: In this paper, a novel integrator based on normalizing flows is proposed to improve the unweighting efficiency of Monte Carlo event generators for collider physics simulations, in contrast to machine learning approaches based on surrogate models, which generate the correct result even if the underlying neural networks are not optimally trained.
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Abstract: We present a novel integrator based on normalizing flows which can be used to improve the unweighting efficiency of Monte Carlo event generators for collider physics simulations. In contrast to machine learning approaches based on surrogate models, our method generates the correct result even if the underlying neural networks are not optimally trained. We exemplify the new strategy using the example of Drell-Yan type processes at the LHC, both at leading and partially at next-to-leading order QCD.
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Citations
Invertible networks or partons to detector and back again
Marco Bellagente,Anja Butter,Gregor Kasieczka,Tilman Plehn,Armand Rousselot,Ramon Winterhalder,Lynton Ardizzone,Ullrich Köthe +7 more
- 18 Nov 2020
TL;DR: In this paper, a conditional INN is used to invert a detector simulation in terms of high-level observables, specifically for ZW production at the LHC, allowing for a per-event statistical interpretation.
Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows
Bob Stienen,Rob Verheyen +1 more
- 17 Feb 2021
TL;DR: In this paper, autoregressive flows are used to generate particle collider events with variable, and even negative event weights, with the usual maximum likelihood loss function supplemented by an event weight.
Neural Network-Based Approach to Phase Space Integration
Matthew D. Klimek,Matthew D. Klimek,Maxim Perelstein +2 more
- 19 Oct 2020
TL;DR: In this article, a Neural Network (NN) algorithm was proposed to integrate and sample probability distributions (differential cross sections or decay rates) on multi-dimensional phase spaces, which achieved unweighting efficiencies of between 30% and 75%.
GANplifying Event Samples
Anja Butter,Sascha Diefenbacher,Gregor Kasieczka,Benjamin Nachman,Tilman Plehn +4 more
- 10 Jun 2021
TL;DR: In this article, a critical question concerning GNNs applied to event generation in particle physics is if the generated events add statistical precision beyond the training sample, and the authors quantify their impact through an amplification factor or equivalent numbers of sampled events.
Machine learning in the search for new fundamental physics
G. Karagiorgi,Bowen Wang,Anna Bondarenko,Nabeel Taha Ali Belal , Khalid Akbar Abdullah,Sayed Khalil Kohi +4 more
TL;DR: A review of the state-of-the-art methods and applications for new physics searches in the context of terrestrial high-energy physics experiments, including the Large Hadron Collider, rare event searches and neutrino experiments, can be found in this paper .
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James Bergstra,Yoshua Bengio +1 more
TL;DR: This paper shows empirically and theoretically that randomly chosen trials are more efficient for hyper-parameter optimization than trials on a grid, and shows that random search is a natural baseline against which to judge progress in the development of adaptive (sequential) hyper- parameter optimization algorithms.
The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
Johan Alwall,Rikkert Frederix,Stefano Frixione,Valentin Hirschi,Fabio Maltoni,Olivier Mattelaer,Hua-Sheng Shao,Tim Stelzer,Paolo Torrielli,Marco Zaro,Marco Zaro +10 more
TL;DR: MadGraph5 aMC@NLO as discussed by the authors is a computer program capable of handling all these computations, including parton-level fixed order, shower-matched, merged, in a unified framework whose defining features are flexibility, high level of parallelisation and human intervention limited to input physics quantities.
Event generation with SHERPA 1.1
Tanju Gleisberg,Stefan Höche,Frank Krauss,Marek Schönherr,Steffen Schumann,Frank Siegert,J. Winter +6 more
TL;DR: Sherpa as mentioned in this paper is a general-purpose tool for the simulation of particle collisions at high-energy colliders and contains a very flexible tree-level matrix-element generator for the calculation of hard scattering processes within the Standard Model and various new physics models.
Event generation with SHERPA 1.1
T. Gleisberg,Stefan Hoeche,Frank Krauss,Marek Schoenherr,Steffen Schumann,Frank Siegert,J. Winter +6 more
TL;DR: Sherpa as discussed by the authors is a general-purpose tool for the simulation of particle collisions at high-energy colliders and contains a very flexible tree-level matrix-element generator for the calculation of hard scattering processes within the Standard Model and various new physics models.
A new algorithm for adaptive multidimensional integration
TL;DR: A new general purpose algorithm for multidimensional integration is described, an iterative and adaptive Monte Carlo scheme that is considerably more efficient than several others currently in use for a number of sample integrals of high dimension.
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