Parnassus: An Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction

TL;DR

Parnassus introduces a deep learning-based surrogate model that efficiently simulates and reconstructs particle detector data, significantly reducing computational costs while maintaining accuracy and generalization capabilities.

Contribution

This work presents a novel integrated neural network approach for detector simulation and reconstruction, improving speed and resource efficiency in particle physics analyses.

Findings

Accurately mimics CMS particle flow algorithm on training data

Generalizes to different jet momenta and types outside training set

Reduces computational resource requirements for detector simulation

Abstract

Detector simulation and reconstruction are a significant computational bottleneck in particle physics. We develop Particle-flow Neural Assisted Simulations (Parnassus) to address this challenge. Our deep learning model takes as input a point cloud (particles impinging on a detector) and produces a point cloud (reconstructed particles). By combining detector simulations and reconstruction into one step, we aim to minimize resource utilization and enable fast surrogate models suitable for application both inside and outside large collaborations. We demonstrate this approach using a publicly available dataset of jets passed through the full simulation and reconstruction pipeline of the CMS experiment. We show that Parnassus accurately mimics the CMS particle flow algorithm on the (statistically) same events it was trained on and can generalize to jet momentum and type outside of the training distribution.