Cross-Detector Transfer Learning with Parnassus: From ALEPH to SLD
Abstract
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.
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BibTeX
@misc{lo2026crossdetector,
title = {{Cross-Detector Transfer Learning with Parnassus: From ALEPH to SLD}},
author = {Ya-Feng Lo and Chi Lung Cheng and Benjamin Nachman},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.25061},
eprint = {2609.25061},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.25061},
}
RIS
TY - UNPB
TI - Cross-Detector Transfer Learning with Parnassus: From ALEPH to SLD
AU - Ya-Feng Lo
AU - Chi Lung Cheng
AU - Benjamin Nachman
PY - 2026
DA - 2026/09/13
PB - arXiv
DO - 10.48550/arxiv.2609.25061
UR - https://arxiv.org/abs/2609.25061
AB - 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.
ER -