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Bayesian optimization enhances ACTS parameter tuning for particle reconstruction

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This paper explores new methods for optimizing the ACTS parameter suite, a tool used in charged-particle reconstruction. The researchers investigate Bayesian optimization techniques, specifically Expected Improvement and Upper Confidence Bound, comparing them against the existing Optuna Tree-structured Parzen Estimator (TPE) and random search. Their findings indicate that Bayesian methods can identify strong configurations more efficiently and earlier than TPE, even when expanding the search to more parameters. Furthermore, multi-objective optimization using Expected Hypervolume Improvement revealed competitive solutions with distinct trade-offs. AI

RANK_REASON The cluster contains a research paper detailing new methods for parameter optimization in a scientific context. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

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Bayesian optimization enhances ACTS parameter tuning for particle reconstruction

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The cluster contains a research paper detailing new methods for parameter optimization in a scientific context. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Chance LaVoie, Qi Bin Lei, Rocky Bala Garg, Lauren Tompkins ·

    Exploring new directions in enhancing the ACTS parameter optimization suite

    arXiv:2608.14714v1 Announce Type: cross Abstract: 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 reduce…

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