Abstract This paper presents an automatic method for data classification in nuclear physics experiments based on evolutionary computing and vector quantization. The major novelties of our approach are the fully automatic mechanism and the use of analytical models to provide physics constraints, yielding to a fast and physically reliable classification with nearly-zero human supervision. Our method is successfully validated using experimental data produced by stacks of semiconducting detectors. The resulting classification is highly satisfactory for all explored cases and is particularly robust to noise. The algorithm is suitable to be integrated in the online and offline analysis software of existing large complexity detection arrays for the study of nucleus–nucleus collisions at low and intermediate energies.

中文翻译：

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通过约束进化聚类方法自动分类核物理数据

摘要 本文提出了一种基于进化计算和矢量量化的核物理实验数据自动分类方法。我们方法的主要新颖之处在于全自动机制和使用分析模型来提供物理约束，从而在几乎零人工监督的情况下实现快速且物理上可靠的分类。使用由半导体探测器堆栈产生的实验数据成功验证了我们的方法。由此产生的分类对于所有探索的情况都非常令人满意，并且对噪声特别鲁棒。该算法适合集成到现有大型复杂探测阵列的在线和离线分析软件中，用于研究中低能的核-核碰撞。