Xsec: the cross-section evaluation code

Buckley, A. G.; Kvellestad, Anders; Raklev, Are; Scott, Pat; Sparre, Jon Vegard; Abeele, Jeriek Van den; Vazquez-Holm, Ingrid A.

doi:10.1140/epjc/s10052-020-08635-y

Cited by 8 publications

(7 citation statements)

References 39 publications

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“…The capacity of neural networks to approximate intricate functions have already been used to provide fast calculations of production cross-sections [25,26]. In this article we investigate whether NNs can approximate production cross-sections more differentially by replacing computationally expensive matrix element calculations.…”

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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“…To constrain the parameters of a BSM theory, this process can be carried out for various values of couplings and masses and a further ML is used to interpolate over possible values. In [10], the authors trained a distributed Gaussian processes (DGP) regression algorithm to predict the Minimal Supersymmetric SM (MSSM) cross-sections at NLO.…”

Section: Scattering Amplitudes and Cross-sectionsmentioning

confidence: 99%

AI and Theoretical Particle Physics

Gupta¹,

Bhattacharya²,

Yoon³

2022

Preprint

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Theoretical particle physicists continue to push the envelope in both high performance computing and in managing and analyzing large data sets. For example, the goals of sub-percent accuracy in predictions of quantum chromodynamics (QCD) using large scale simulations of lattice QCD and in finding signals of rare events and new physics in exabytes of data produced by experiments at the high luminosity large hadron collider (LHC) require new tools beyond just developments in hardware. Machine learning and artificial intelligence offer the promise of dramatically reducing the computational cost and time. This chapter reviews selected areas where AI/ML tools could have a major impact, provides an overview of the challenges, and discusses how new ideas such as normalizing flows can speed up the generation of gauge configurations needed in lattice QCD calculations; the growth of ML in surrogate models and pattern matching to reduce the cost of event generators and in the analysis of experimental data; and in the search for viable vacua in the landscape of string theories. While such approaches transform aspects of particle theory into computational problems, and thus black boxes, we argue that physics-aware development of these tools combined with algorithms that ensure that the results are bias free will continue to require a deep understanding of the physics. We see this broader transformation as akin to formulating and extracting observables from simulations of lattice QCD, a numerical integration of the path integral formulation of QCD that nevertheless requires a deep understanding of the underlying quantum field theory, the standard model of particle physics and effective field theory methods.

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“…Several works have focused on developing NN techniques for explicitly learning the cross section of a given process [57,58]; however, little research has been done on learning the matrix element itself for a given phase-space point and process. Ref.…”

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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Xsec: the cross-section evaluation code

Cited by 8 publications

References 39 publications

A factorisation-aware Matrix element emulator

A factorisation-aware Matrix element emulator

AI and Theoretical Particle Physics

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

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