Ya-Feng Lo | Search arXiv

arXiv2026

Cross-Detector Transfer Learning with Parnassus: From ALEPH to SLD

Parnassus is a fast detector-simulation and reconstruction framework that maps truth-level particles directly to reconstructed particles. In this work, we investigate cross-detector transfer learning by adapting a Parnassus model for the ALEPH detector to the SLD detector. ALEPH and SLD have similar detector responses, as they were both targeting hadronic $Z$-pole $e^+e^-$ events, while differing substantially in the underlying detector technology, reconstruction, and archived data representation. We initialize the SLD particle model with weights learned on ALEPH, fine-tune it on SLD, and compare it with the same architecture trained directly on SLD. The ALEPH-initialized model gives substantially improved particle- and jet-level agreement with the SLD reference, including a factor of 5.2 improvement in the charged-particle angular response and an improvement in the jet angular resolution from $1.73$ to $1.15$ times the SLD reference. The results show that detector-response information learned on ALEPH can be reused when modeling SLD and motivate reusable pretrained Parnassus models for legacy $e^+e^-$ detectors, which is especially critical for experiments without access to the original software pipeline. With this paper, we also release an AI-ready version of simulated SLD $e^+e^-$ events.

physics.ins-det↗

arXiv2026

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

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, with substantially better agreement than the Delphes fast simulation. 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.

physics.ins-det↗