A factorisation-aware Matrix element emulator

TL;DR

This paper introduces a neural network model that emulates matrix elements by leveraging their factorisation properties, enabling accurate and robust extrapolation into singular regions in particle physics simulations.

Contribution

The paper presents a novel neural network approach that incorporates factorisation properties to improve matrix element emulation and extrapolation capabilities.

Findings

Achieves sub-1% error in reproducing matrix elements within training phase-space.

Demonstrates robust extrapolation to more singular phase-space regions.

Applicable to leading-order jet production in electron-positron collisions.

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 so doing 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.