基于约束进化聚类的核物理数据自动分类
Automatic classification of nuclear physics data via a Constrained Evolutionary Clustering approach
摘要：
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。我们的方法的主要创新之处是全自动机制和使用分析模型来提供物理约束，从而在几乎零人为监督的情况下实现快速和物理可靠的分类。我们的方法是成功地验证了使用半导体探测器堆栈产生的实验数据。所得到的分类是非常令人满意的所有探索的情况下，是特别强大的噪音。该算法适合于集成到现有的大复杂度探测阵列的在线和离线分析程序中，用于研究中低能核-核碰撞。
英文摘要：
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 by 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 programs of existing large complexity detection arrays for the study of nucleus-nucleus collisions at low and intermediate energies.
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