An AI-based Detector Simulation and Reconstruction Model for the ALEPH Experiment at LEP

TL;DR

This paper introduces Parnassus, a neural network-based generative model for simulating and reconstructing detector responses, successfully applied to the ALEPH experiment at LEP, demonstrating its versatility beyond LHC environments.

Contribution

The work adapts a modern neural generative model for detector simulation to a historical collider experiment, showing its effectiveness outside current LHC applications.

Findings

Parnassus accurately reproduces detector responses at multiple levels.

The model generalizes well to different detector geometries and physics environments.

It offers a tool for legacy data analysis where traditional software is hard to maintain.

Abstract

We present the application of Parnassus, a generative model for full detector simulation and reconstruction, to the ALEPH detector at the Large Electron-Positron Collider (LEP). Training on simulated $e^+e^-$ to Z to qqbar events processed through the ALEPH detector simulation and reconstruction, we demonstrate that Parnassus faithfully reproduces the detector response at the event, jet, and particle levels. The clean $e^+e^-$ environment, free of pileup and characterized by simple event topologies, provides a well-controlled benchmark for evaluating the generative model's fidelity. Our results demonstrate that modern neural-network-based generative simulation approaches, developed primarily for LHC experiments, generalize naturally to historical collider experiments with distinct detector geometries and physics environments. This work shows that Parnassus can be applied beyond the LHC context and serves as an important tool for legacy data analysis where archival software tools are challenging to resurrect.