论文标题
通过约束进化聚类方法自动对核物理数据进行自动分类
Automatic classification of nuclear physics data via a Constrained Evolutionary Clustering approach
论文作者
论文摘要
本文提出了一种基于进化计算和矢量量化的核物理实验中数据分类的自动方法。我们方法的主要新颖性是全自动机制和分析模型提供物理限制的使用，并通过近零的人类监督屈服于快速且物理上可靠的分类。通过使用由半导体检测器堆栈产生的实验数据成功验证我们的方法。对于所有探索案例，所得的分类非常令人满意，并且对噪声尤其强大。该算法适用于现有大型复杂性检测阵列的在线和离线分析程序，以研究低和中间能量的核核碰撞。
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.
