A Positive Resampler for Monte Carlo events with negative weights
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Special Article - Tools for Experiment and Theory
A Positive Resampler for Monte Carlo events with negative weights Jeppe R. Andersen1,a , Christian Gütschow2,b , Andreas Maier3,c , Stefan Prestel4,d 1
Institute for Particle Physics Phenomenology, University of Durham, Durham DH1 3LE, UK Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, UK 3 Deutsches Elektronen-Synchrotron, DESY, Platanenallee 6, 15738 Zeuthen, Germany 4 Theoretical Particle Physics, Department of Astronomy and Theoretical Physics, Lund University, Sölvegatan 14 A, 223 62 Lund, Sweden
2
Received: 20 July 2020 / Accepted: 13 October 2020 © The Author(s) 2020
Abstract 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.
1 Introduction General Purpose Event Generators [1–3] form a crucial component of studies in high-energy physics, since they produce detailed predictions used for the design and calibration of detectors, interpretations of the measurements as well as the investigations of theoretical models. More often than not it is necessary to take into account the effects from the perturbative showering and the hadronisation models implemented in these generators, in order to achieve an accurate prediction for the cuts and observables chosen for experimental measurements. High-accuracy perturbative event generator predictions can be obtained by first matching each jet multiplicity to nexta e-mail:
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b e-mail:
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c e-mail:
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d e-mail:
[email protected] (corresponding author)
to-leading order (NLO) using the methods of e.g. MC@NLO [4] or POWHEG [5], followed by a merging of these exclusive samples using approaches such as MEPS@NLO [6] or UNLOPS [7,8]. The increased accuracy comes at a significant cost in additional computing resources, and these calculations increasingly contribute to the LHC computing footprint. The result of these merged NLO-accurate event generator simulations are event samples containing events of both positive and negative weights, meaning that the correspondence between the number of events in a bin of a distribution and the cross section in that bin is lost.1 Even when the event samples are unweighted to constitute events with weights o
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