Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning

TL;DR

This paper introduces a deep learning-based fast simulation method for high-energy physics that models detector effects efficiently, reducing computational costs for large datasets at the LHC.

Contribution

The paper presents a novel analysis-specific fast simulation workflow using neural networks to emulate detector resolution effects, significantly reducing resource requirements.

Findings

Achieves about tenfold reduction in simulation resources.

Successfully models detector effects using neural networks.

Enables large-scale analysis-specific data generation.

Abstract

We present a fast simulation application based on a Deep Neural Network, designed to create large analysis-specific datasets. Taking as an example the generation of W+jet events produced in sqrt(s)= 13 TeV proton-proton collisions, we train a neural network to model detector resolution effects as a transfer function acting on an analysis-specific set of relevant features, computed at generation level, i.e., in absence of detector effects. Based on this model, we propose a novel fast-simulation workflow that starts from a large amount of generator-level events to deliver large analysis-specific samples. The adoption of this approach would result in about an order-of-magnitude reduction in computing and storage requirements for the collision simulation workflow. This strategy could help the high energy physics community to face the computing challenges of the future High-Luminosity LHC.