GANplifying event samples

Butter, Anja; Diefenbacher, Sascha; Kasieczka, G.; Nachman, B. P.; Plehn, Tilman

doi:10.21468/scipostphys.10.6.139

Cited by 78 publications

(80 citation statements)

References 35 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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“…The statistical benefits of using generative models are discussed in Ref. [44], for a discussion of trainingrelated uncertainties using Bayesian normalizing flows see Refs. [45,46].…”

Section: Online Trainingmentioning

confidence: 99%

Ephemeral Learning -- Augmenting Triggers with Online-Trained Normalizing Flows

Butter,

Diefenbacher,

Kasieczka

et al. 2022

Preprint

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The large data rates at the LHC require an online trigger system to select relevant collisions. Rather than compressing individual events, we propose to compress an entire data set at once. We use a normalizing flow as a deep generative model to learn the probability density of the data online. The events are then represented by the generative neural network and can be inspected offline for anomalies or used for other analysis purposes. We demonstrate our new approach for a toy model and a correlation-enhanced bump hunt.

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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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GANplifying event samples

Cited by 78 publications

References 35 publications

A factorisation-aware Matrix element emulator

A factorisation-aware Matrix element emulator

Ephemeral Learning -- Augmenting Triggers with Online-Trained Normalizing Flows

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

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