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

  title={Optimising simulations for diphoton production at hadron colliders using amplitude neural networks},
  author={Joseph Aylett-Bullock and Simon Badger and Ryan I. Moodie},
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… 

Reconstructing partonic kinematics at colliders with machine learning

In the context of high-energy physics, a reliable description of the parton-level kinematics plays a crucial role for understanding the internal structure of hadrons and improving the precision of

Accelerating Monte Carlo event generation -- rejection sampling using neural network event-weight estimates

The generation of unit-weight events for complex scattering processes presents a severe challenge to modern Monte Carlo event generators. Even when using sophisticated phase-space sampling techniques

Machine Learning and LHC Event Generation

First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and

A factorisation-aware Matrix element emulator

A neural network based model to emulate matrix elements improves on existing methods by taking advantage of the known factorisation properties of matrix elements and can control the behaviour of simulated matrix elements when extrapolating into more singular regions than the ones used for training the neural network.

Loop Amplitudes from Precision Networks

It is shown that simple, Bayesian networks can learn such amplitudes and model their uncertainties reliably and a boosted training of the Bayesian network further improves the uncertainty estimate and the network precision in critical phase space regions.

Les Houches 2021: Physics at TeV Colliders: Report on the Standard Model Precision Wishlist

Les Houches activities in 2021 were truncated due to the lack of an in-person component. However, given the rapid progress in the field, and the restart of the LHC, we wanted to continue the

A Living Review of Machine Learning for Particle Physics

This living review is a nearly comprehensive list of citations for those developing and applying deep learning approaches to experimental, phenomenological, or theoretical analyses, and will be updated as often as possible to incorporate the latest developments.

Comparing Machine Learning and Interpolation Methods for Loop-Level Calculations

Four interpolation and three machine learning techniques are considered and their performance on three toy functions, the four-point scalar Passarino-Veltman D 0 function, and the two-loop self-energy master integral M are compared.

Function Approximation for High-Energy Physics: Comparing Machine Learning and Interpolation Methods

The following plots show the approximant predictions versus the true values of the test functions in various dimensions on 50,000 points, providing further evidence for the superiority of MLP over the other approximants.

Targeting multi-loop integrals with neural networks

Numerical evaluations of Feynman integrals often proceed via a deformation of the integration contour into the complex plane. While valid contours are easy to construct, the numerical precision for a



AI-based Monte Carlo event generator for electron-proton scattering

We present a new strategy using artificial intelligence (AI) to build the first AI-based Monte Carlo event generator (MCEG) capable of faithfully generating final state particle phase space in

Using neural networks for efficient evaluation of high multiplicity scattering amplitudes

The possibility of using neural networks to approximate multi-variable scattering amplitudes and provide efficient inputs for Monte Carlo integration is explored and reliable interpolation is demonstrated when a series of networks are trained to amplitudes that have been divided into sectors defined by their infrared singularity structure.

Reweighting a parton shower using a neural network: the final-state case

A bstractThe use of QCD calculations that include the resummation of soft-collinear logarithms via parton-shower algorithms is currently not possible in PDF fits due to the high computational cost of

Deep learning as a parton shower

  • J. Monk
  • Physics, Computer Science
    Journal of High Energy Physics
  • 2018
A bstractWe make the connection between certain deep learning architectures and the renormalisation group explicit in the context of QCD by using a deep learning network to construct a toy parton

Improved neural network Monte Carlo simulation

It is argued that the training procedure naturally prefers bijective maps, and it is demonstrated that the trained ANN isBijective to a very good approximation.

Neural network-based approach to phase space integration

A Neural Network algorithm optimized to perform Monte Carlo methods to integrate and sample probability distributions on multi-dimensional phase spaces, including situations with non-trivial features such as sharp resonances and soft/collinear enhancements.

Event generation with normalizing flows

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 and generates the correct result even if the underlying neural networks are not optimally trained.

Ntuples for NLO events at hadron colliders

Neural resampler for Monte Carlo reweighting with preserved uncertainties

Monte Carlo event generators are an essential tool for data analysis in collider physics. To include subleading quantum corrections, these generators often need to produce negative weight events,

Herwig++ physics and manual

AbstractIn this paper we describe $\mathsf{Herwig++}$ version 2.2, a general-purpose Monte Carlo event generator for the simulation of hard lepton-lepton and hadron-hadron collisions. A number of