发表机构
Carnegie Mellon University; University of California, Santa Cruz; Santa Cruz Institute for Particle Physics; Stanford University（卡内基梅隆大学; 加州大学圣克鲁兹分校; 圣克鲁兹粒子物理研究所; 斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对ACTS参数优化的局限，结合ODD应用贝叶斯优化，在8参数问题上验证其优于TPE，扩展至15参数并实现多目标优化，可增强ACTS自动调谐能力。
AI 中文摘要
径迹种子的生成强烈影响带电粒子重建的质量和计算成本，但其众多配置参数通常通过专家直觉和反复试错进行调整。ACTS借助Optuna树状Parzen估计器自动调谐器降低了这一负担，但高昂的评估成本、受限的搜索空间以及标量化目标会限制评估效率、排除有前景的配置并模糊性能权衡。我们研究贝叶斯优化是否能通过结合ACTS与开放数据探测器（ODD）来解决这些局限。在相同搜索范围和100次试验的统一预算下，我们在现有8参数问题上比较期望改进（Expected Improvement）、上置信界（Upper Confidence Bound）与树状Parzen估计器（TPE）、随机搜索的表现，将性能最佳的贝叶斯方法扩展至15参数，并应用期望超体积改进（Expected Hypervolume Improvement）在无固定标量权重的情况下优化效率、假阳性率、重复率和运行时间。候选配置通过完整ACTS重建链评估，并在独立保留事件上验证。贝叶斯获取方法比TPE更早识别出强配置，且其优势在保留验证中持续存在。进一步扩展搜索空间可提升性能，而多目标优化揭示了覆盖不同权衡的竞争性非支配解。这些结果表明，贝叶斯优化可通过高效评估、更广泛的参数搜索以及专家后续在非支配备选方案中选择，增强ACTS自动调谐能力。
英文摘要
Track seeding strongly affects both the quality and computational cost of charged-particle reconstruction, yet its many configuration parameters are commonly tuned through expert intuition and repeated trial and error. ACTS reduces this burden with an Optuna Tree-structured Parzen Estimator auto-tuner, but expensive evaluations, a restricted search space, and a scalarized objective can limit evaluation efficiency, exclude promising configurations, and obscure performance trade-offs. We investigate whether Bayesian optimization can address these limitations using ACTS with the Open Data Detector (ODD). Under identical search ranges and a common 100-trial budget, we compare Expected Improvement and Upper Confidence Bound with TPE and random search on the existing eight-parameter problem, extend the best-performing Bayesian method to fifteen parameters, and apply Expected Hypervolume Improvement to optimize efficiency, fake rate, duplicate rate, and runtime without fixed scalar weights. Candidate configurations are evaluated through the full ACTS reconstruction chain and validated on disjoint held-out events. The Bayesian acquisition methods identify strong configurations earlier than TPE, and their advantage persists in held-out validation. Expanding the search further improves performance, while multi-objective optimization reveals competitive non-dominated solutions spanning distinct trade-offs. These results indicate that Bayesian optimization can strengthen ACTS auto-tuning through efficient evaluation, broader parameter searches, and post-hoc expert selection among non-dominated alternatives.
CommentsProceedings for ACAT 2025, 5 pages, 2 figures
