Journal ArticleDOI
Artificial intelligence—applications in high energy and nuclear physics
TLDR
In the parallel sessions at ACAT2002 different artificial intelligence applications in high energy and nuclear physics were presented as discussed by the authors, and a summary of these presentations can be found in the relevant section of these proceedings.Abstract:
In the parallel sessions at ACAT2002 different artificial intelligence applications in high energy and nuclear physics were presented. I will briefly summarize these presentations. Further details can be found in the relevant section of these proceedings.read more
Citations
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Journal ArticleDOI
Dynamic Fuzzy c-Means (dFCM) Clustering and its Application to Calorimetric Data Reconstruction in High Energy Physics
TL;DR: It is seen that the FCM technique works reasonably well, and at the same time, the use of the dFCM technique improves the performance.
Journal ArticleDOI
Total cross section prediction of the collisions of positrons and electrons with alkali atoms using Gradient Tree Boosting
TL;DR: In this paper, a gradient tree boosting (GTB) technique was used for modeling the total cross sections of the scattering of positrons and electrons by alkali atoms in the low and intermediate energy regions.
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Application of neural networks to digital pulse shape analysis for an array of silicon strip detectors
TL;DR: In this paper, the feasibility of using artificial neural networks (ANNs) for particle identification with silicon detectors was studied. But the authors only used the multilayer perceptron networks (MLP) for detection of 12C up to 84Kr.
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Artificial neural networks applied to quantitative elemental analysis of organic material using PIXE
TL;DR: In this article, an artificial neural network (ANN) was trained with real-sample PIXE (particle X-ray induced emission) spectra of organic substances and applied to a subset of similar samples thus obtaining the elemental concentrations in muscle, liver and gills of Cyprinus carpio.
References
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Journal ArticleDOI
Support vector machines in analysis of top quark production
TL;DR: In this article, a comparison of a conventional method and an SVM algorithm is presented for the case of identifying top quark events in Run II physics at the CDF experiment.
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Optimized Neural Networks to Search for Higgs Boson Production at the Tevatron
TL;DR: In this paper, an optimal choice of proper kinematical variables is one of the main steps in using neural networks (NN) in high energy physics, based on the analysis of a structure of Feynman diagrams contributing to the signal and background processes.
Journal ArticleDOI
Optimized neural network search of Higgs boson production with the Tevatron
E. E. Boos,Lev Dudko,D. Smirnov +2 more
TL;DR: In this article, the authors apply the analysis of the Feynman diagram structure (singularities and spin effects) for the signal and background processes to improve the efficiency of the Higgs search.
Journal ArticleDOI
Application of wavelet analysis to data treatment for small-angle neutron scattering
TL;DR: This work presents an improvement in the resulting scattering spectra quality due to the use of spectrometer resolution during both wavelet filtering and traditional smoothing the small-angle neutron scattering (SANS) data.
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Moving NN Triggers to Level-1 at LHC Rates
TL;DR: In this paper, a new FPGA-based method for implementing multilayer perceptrons with hundreds of neurons in a 25 ns pipeline structure having only 600 ns latency is discussed.