Methods for Multidimensional Event Classification: A Case Study
Methods for Multidimensional Event Classification: A Case Study
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多维事件分类方法：案例研究
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
W. Wittek
中科院分区：
文献类型：
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作者：
R. Bock;A. Chilingarian;M. Gaug;F. Hakl;T. Hengstebeck;M. Jǐrina;J. Klaschka;E. Kotrč;P. Savický;S. Towers;A. Vaiciulis;W. Wittek
我们给出了一个案例研究的结果，比较了不同的Classifi阳离子方法，使用一组蒙特卡罗数据作为输入，生成并近似触发和预处理成像伽马-切伦科夫望远镜。这类数据分为两类，要么来自入射伽马射线，要么来自强子阵雨。信号(伽马)和背景(强子)之间的区别很弱，因此这些数据为经典fi阳离子技术提供了一个很好的试验场。对数据进行了简要的描述，给出了方法，并对结果进行了比较。有几种方法给出的结果在小fl效果图中具有可比性，这表明它们的性能达到或接近可实现分离的贝叶斯极限。其他方法得出的结果明显较差或不确定。对本研究不能解决的一些问题也进行了讨论。我们给出了一个案例研究的结果，比较了不同的Classi-fi阳离子方法，使用了一组蒙特卡罗数据作为输入，生成并近似触发和预处理了成像伽马射线切伦科夫望远镜。这类数据分为两类，要么来自入射伽马射线，要么来自强子阵雨。信号(伽马)和背景(强子)之间的区别很弱，因此这些数据为经典fl阳离子技术提供了一个很好的试验场。对数据进行了简要描述，给出了方法，并对结果进行了比较。有几种方法给出的结果在小范围(CID：176)内具有可比性，表明它们的性能达到或接近可实现分离的贝叶斯极限。其他方法得出的结果明显较差或不确定。对本研究不能解决的一些问题也进行了讨论。
: We present results from a case study comparing different classification methods, using as input a set of Monte Carlo data, generated and approximately triggered and preprocessed for an imaging gammaray Cherenkov telescope. Such data belong to two classes, originating either from incident gamma rays or caused by hadronic showers. There is a weak discrimination between signal (gamma) and background (hadrons), and thus the data provide an excellent proving ground for classification techniques. The data are briefly described, the methods are presented, and a comparison of the results is made. Several methods give results comparable within small fluctuations, suggesting that they perform at or close to the Bayesian limit of achievable separation. Other methods give clearly inferior or inconclusive results. Some problems that this study can not address are also discussed. Abstract We present results from a case study comparing difierent classiflcation methods, using as input a set of Monte Carlo data, generated and approximately triggered and pre-processed for an imaging gamma-ray Cherenkov telescope. Such data belong to two classes, originating either from incident gamma rays or caused by hadronic showers. There is a weak discrimination between signal (gamma) and background (hadrons), and thus the data provide an excellent proving ground for classiflcation techniques. The data are brie(cid:176)y described, the methods are presented, and a comparison of the results is made. Several methods give results comparable within small (cid:176)uctuations, suggesting that they perform at or close to the Bayesian limit of achievable separation. Other methods give clearly inferior or inconclusive results. Some problems that this study can not address are also discussed.