Abstract
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.
Highlights
In this article we present a neural network based model to emulate 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
Our results show that this model can reproduce the matrix elements with errors below the one-percent level on the phasespace 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
Summary
In previous works [27, 28] a combination of individual neural networks were used to approximate matrix elements. In order to deal with the complex structure of the matrix elements the authors of these studies divided the phase-space into sectors according to the infrared singularities and trained networks on these sectors, thereby limiting the complexity of the fit by isolating a single divergence per sector. The performance of the extrapolation outside of its training phase-space (i.e. more singular configurations than those considered to fit the model) is problematic.
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