How to GAN Event Unweighting

Backes, Mathias; Butter, Anja; Plehn, Tilman; Winterhalder, Ramon

doi:10.21468/scipostphys.10.4.089

Cited by 39 publications

(39 citation statements)

References 65 publications

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“…Since then neural networks and other machine learning techniques have proved useful in many other areas of the field. On the theory prediction side they have been used to improve the efficiency of Monte Carlo sampling [1][2][3][4][5], to accelerate the simulation of radiation within a jet [6][7][8], to streamline the processes of generation and unweighting of simulated event samples [9][10][11][12][13][14][15][16][17][18]. Closer to the experimental measurements they have also been used to emulate detector simulation [19][20][21][22], they can be used to perform unfolding [23] or correcting for detector effects [24], and perform pileup subtraction [23].…”

Section: Introductionmentioning

confidence: 99%

A factorisation-aware Matrix element emulator

Maître

Truong

2021

J. High Energ. Phys.

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In this article we present a neural network based model to emulate matrix elements. This model improves on existing methods by taking advantage of the known factorisation properties of matrix elements. In doing so we can control the behaviour of simulated matrix elements when extrapolating into more singular regions than the ones used for training the neural network. We apply our model to the case of leading-order jet production in e+e− collisions with up to five jets. Our results show that this model can reproduce the matrix elements with errors below the one-percent level on the phase-space covered during fitting and testing, and a robust extrapolation to the parts of the phase-space where the matrix elements are more singular than seen at the fitting stage.

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Section: Introductionmentioning

confidence: 99%

A factorisation-aware Matrix element emulator

Maître

Truong

2021

J. High Energ. Phys.

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“…There has also been a large focus on using ML for other components of MC event generator simulations. Specifically, Generative Adversarial Networks (GANs) [35] are being applied to event generation [36][37][38][39][40][41][42][43][44][45][46][47], event unweighting [48,49] and subtraction [50], with recent works incorporating Bayesian methods for uncertainty estimation into these generative methods [51]. NN-based approaches (some of which also use GAN technology) applied to parton showering [52][53][54][55] and event reweighting [56] have also been developed.…”

Section: Jhep08(2021)066mentioning

confidence: 99%

Optimising simulations for diphoton production at hadron colliders using amplitude neural networks

Aylett-Bullock

Badger

Moodie

2021

J. High Energ. Phys.

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Machine learning technology has the potential to dramatically optimise event generation and simulations. We continue to investigate the use of neural networks to approximate matrix elements for high-multiplicity scattering processes. We focus on the case of loop-induced diphoton production through gluon fusion, and develop a realistic simulation method that can be applied to hadron collider observables. Neural networks are trained using the one-loop amplitudes implemented in the NJet C++ library, and interfaced to the Sherpa Monte Carlo event generator, where we perform a detailed study for 2 → 3 and 2 → 4 scattering problems. We also consider how the trained networks perform when varying the kinematic cuts effecting the phase space and the reliability of the neural network simulations.

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“…Underlying techniques include GANs [2][3][4], VAEs [5,6], normalizing flows [7][8][9][10][11], and their invertible network (INN) variant [12][13][14]. As part of the standard LHC simulation chain, modern neural networks can be applied to the full range of phase space integration [15,16], phase space sampling [17][18][19][20], amplitude computations [21,22], event subtraction [23], event unweighting [24,25], parton showering [26][27][28][29][30], or super-resolution enhancement [31,32]. In essence, there is no aspect of the standard event generation chain that cannot be improved through modern machine learning.…”

Section: Introductionmentioning

confidence: 99%

Generative Networks for Precision Enthusiasts

Butter¹,

Heimel²,

Hummerich³

et al. 2021

Preprint

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Generative networks are opening new avenues in fast event generation for the LHC. We show how generative flow networks can reach percent-level precision for kinematic distributions, how they can be trained jointly with a discriminator, and how this discriminator improves the generation. Our joint training relies on a novel coupling of the two networks which does not require a Nash equilibrium. We then estimate the generation uncertainties through a Bayesian network setup and through conditional data augmentation, while the discriminator ensures that there are no systematic inconsistencies compared to the training data.

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How to GAN Event Unweighting

Cited by 39 publications

References 65 publications

A factorisation-aware Matrix element emulator

A factorisation-aware Matrix element emulator

Optimising simulations for diphoton production at hadron colliders using amplitude neural networks

Generative Networks for Precision Enthusiasts

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