元学习加速探测器设计优化
Meta-learning accelerates detector design optimization
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中文总结 AI 辅助
针对探测器设计优化中推断模型需反复重训的问题，提出元学习目标估计（MLOE），通过单一元推断模型共享不同设计间的结构，在模拟调用预算匹配下以更少调用评估候选设计并取得更优收敛排名。
中文摘要 AI 辅助
探测器设计的质量最终取决于其所支持的推断质量，即从原始探测器响应中重建感兴趣量的准确度。对于复杂探测器，推断由机器学习模型执行，而设计与其可达到的推断性能之间的关系通常是非平凡的。在本工作中，我们考虑针对探测器设计优化推断性能。传统方法要求在每一个候选设计处重新训练推断模型，从而将评估视为独立任务，并丢弃不同设计处最优推断算法的共享结构。我们提出元学习目标估计（MLOE）：不是在每个候选设计处重新解决推断问题，而是训练一个单一的元推断模型，该模型以设计为条件，并沿优化路径持续训练，在所有设计中共享。我们在三类优化问题上测试了MLOE，其中最后一类包括搜索隐藏粒子（SHiP）实验的光谱仪条跟踪器的两个设计空间；在匹配的模拟调用预算下，元推断模型评估候选设计所需的模拟调用次数少于基线策略，并在所有考察案例中在收敛曲线上保持更优的排名。
英文摘要
The quality of a detector design is ultimately determined by the quality of the inference it enables, that is, by the accuracy with which the quantities of interest are reconstructed from the raw detector response. For complex detectors, the inference is performed by machine learning models, and the relation between the design and the attainable inference performance is, in general, non-trivial. In this work, we consider the optimization of the inference performance with respect to the detector design. The conventional approach prescribes retraining the inference model at every candidate design, thus, treating the evaluations as independent tasks and discarding the shared structure of the optimal inference algorithms at different designs. We propose the meta-learned objective estimate (MLOE): instead of solving the inference problem anew at every candidate design, a single meta-inference model, conditioned on the design and trained continually along the optimization path, is shared across all of them. We test MLOE on three families of optimization problems, the last of which comprises two design spaces of the Spectrometer Straw Tracker of the Search for Hidden Particles (SHiP) experiment; under matched budgets of simulation calls, the meta-inference model evaluates a candidate design using fewer simulation calls than the baseline strategies and holds the better rank over the convergence curve in all examined cases.
