arXiv: Data Analysis, Statistics and Probability最新文献

Using Deep Learning Techniques to Search for the MiniBooNE Low Energy Excess in MicroBooNE with > 3$sigma$ Sensitivity 利用深度学习技术搜索灵敏度> 3$sigma$的MiniBooNE低能量过剩

arXiv: Data Analysis, Statistics and Probability Pub Date : 2020-10-26 DOI: 10.2172/1767032

J. Moon

引用次数: 0

Towards an Improved Eigensystem Realization Algorithm for Low-Error Guarantees 一种改进的低误差保证特征系统实现算法

arXiv: Data Analysis, Statistics and Probability Pub Date : 2020-05-27 DOI: 10.1007/978-981-16-0586-4_4

Mohammad N. Murshed, M. H. Chowdhury, Md Nazmul Islam Shuzan, Mohammad Monir Uddin

引用次数: 2

Measures of Spike Train Synchrony and Directionality 尖峰列车同步和方向性的措施

arXiv: Data Analysis, Statistics and Probability Pub Date : 2020-01-13 DOI: 10.1007/978-3-319-68297-6_13

Eero Satuvuori, Irene Malvestio, T. Kreuz

引用次数: 0

Practical Statistics for Particle Physics 粒子物理实用统计

arXiv: Data Analysis, Statistics and Probability Pub Date : 2019-05-29 DOI: 10.23730/CYRSP-2020-005.149

R. Barlow

引用次数: 6

Data-Driven Computational Methods 数据驱动的计算方法

arXiv: Data Analysis, Statistics and Probability Pub Date : 2018-03-21 DOI: 10.1017/9781108562461

J. Harlim

引用次数: 18

An approximation theoretic perspective of the Sobol' indices with dependent variables. 带因变量的Sobol指数的近似理论透视。

arXiv: Data Analysis, Statistics and Probability Pub Date : 2017-12-22 DOI: 10.1615/int.j.uncertaintyquantification.2018026498

Joseph L. Hart, P. Gremaud

引用次数: 19

The Role of Data Analysis in Uncertainty Quantification: Case Studies for Materials Modeling 数据分析在不确定性量化中的作用:材料建模的案例研究

arXiv: Data Analysis, Statistics and Probability Pub Date : 2017-12-05 DOI: 10.2514/6.2018-0927

P. Patrone, Anthony J. Kearsley, A. Dienstfrey

引用次数: 7

The case for preserving our knowledge and data in physics experiments 在物理实验中保存我们的知识和数据

arXiv: Data Analysis, Statistics and Probability Pub Date : 2017-12-04 DOI: 10.3204/DESY-PROC-2017-02/BERGHAUS_FRANK

F. Berghaus

引用次数: 0

Machine and deep learning techniques in heavy-ion collisions with ALICE 与ALICE重离子碰撞中的机器和深度学习技术

arXiv: Data Analysis, Statistics and Probability Pub Date : 2017-09-22 DOI: 10.22323/1.314.0498

R. Haake

{"title":"Machine and deep learning techniques in heavy-ion collisions with ALICE","authors":"R. Haake","doi":"10.22323/1.314.0498","DOIUrl":"https://doi.org/10.22323/1.314.0498","url":null,"abstract":"Over the last years, machine learning tools have been successfully applied to a wealth of problems in high-energy physics. A typical example is the classification of physics objects. Supervised machine learning methods allow for significant improvements in classification problems by taking into account observable correlations and by learning the optimal selection from examples, e.g. from Monte Carlo simulations. Even more promising is the usage of deep learning techniques. Methods like deep convolutional networks might be able to catch features from low-level parameters that are not exploited by default cut-based methods. \u0000These ideas could be particularly beneficial for measurements in heavy-ion collisions, because of the very large multiplicities. Indeed, machine learning methods potentially perform much better in systems with a large number of degrees of freedom compared to cut-based methods. Moreover, many key heavy-ion observables are most interesting at low transverse momentum where the underlying event is dominant and the signal-to-noise ratio is quite low. \u0000In this work, recent developments of machine- and deep learning applications in heavy-ion collisions with ALICE will be presented, with focus on a deep learning-based b-jet tagging approach and the measurement of low-mass dielectrons. While the b-jet tagger is based on a mixture of shallow fully-connected and deep convolutional networks, the low-mass dielectron measurement uses gradient boosting and shallow neural networks. Both methods are very promising compared to default cut-based methods.","PeriodicalId":308994,"journal":{"name":"arXiv: Data Analysis, Statistics and Probability","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126691569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Combination of various data analysis techniques for efficient track reconstruction in very high multiplicity events 结合各种数据分析技术，在非常高的多重事件中进行有效的航迹重建

arXiv: Data Analysis, Statistics and Probability Pub Date : 2017-08-08 DOI: 10.1051/EPJCONF/201715000011

F. Siklér

{"title":"Combination of various data analysis techniques for efficient track reconstruction in very high multiplicity events","authors":"F. Siklér","doi":"10.1051/EPJCONF/201715000011","DOIUrl":"https://doi.org/10.1051/EPJCONF/201715000011","url":null,"abstract":"A novel combination of established data analysis techniques for reconstructing charged-particles in high energy collisions is proposed. It uses all information available in a collision event while keeping competing choices open as long as possible. Suitable track candidates are selected by transforming measured hits to a binned, three- or four-dimensional, track parameter space. It is accomplished by the use of templates taking advantage of the translational and rotational symmetries of the detectors. Track candidates and their corresponding hits, the nodes, form a usually highly connected network, a bipartite graph, where we allow for multiple hit to track assignments, edges. In order to get a manageable problem, the graph is cut into very many minigraphs by removing a few of its vulnerable components, edges and nodes. Finally the hits are distributed among the track candidates by exploring a deterministic decision tree. A depth-limited search is performed maximizing the number of hits on tracks, and minimizing the sum of track-fit χ 2 . Simplified but realistic models of LHC silicon trackers including the relevant physics processes are used to test and study the performance (efficiency, purity, timing) of the proposed method in the case of single or many simultaneous proton-proton collisions (high pileup), and for single heavy-ion collisions at the highest available energies.","PeriodicalId":308994,"journal":{"name":"arXiv: Data Analysis, Statistics and Probability","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133646462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5