The use of clustering techniques for the classification of high energy physics data

2006 ◽  
Vol 559 (1) ◽  
pp. 199-202 ◽  
Author(s):  
Mostafa Mjahed
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Clustering Techniques ◽  
Energy Physics

scholarly journals End-to-End Event Classification of High-Energy Physics Data

2018 ◽  
Vol 1085 ◽  
pp. 042022 ◽  
Author(s):  
M Andrews ◽  
M Paulini ◽  
S Gleyzer ◽  
B Poczos
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Event Classification ◽  
End To End ◽  
Energy Physics

Classification of the low-energy neutron spectra behind the shield of the accelerator of the institute of high-energy physics

1987 ◽  
Vol 62 (3) ◽  
pp. 213-217 ◽  
Author(s):  
E. A. Belogorlov ◽  
G. I. Britvich ◽  
G. I. Krupnyi ◽  
V. N. Lebedev ◽  
V. S. Lukanin ◽  
...  
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Low Energy ◽  
Neutron Spectra ◽  
Energy Neutron ◽  
Energy Physics

scholarly journals AN EMPIRICAL STUDY OF BOOSTED NEURAL NETWORK FOR PARTICLE CLASSIFICATION IN HIGH ENERGY COLLISIONS

2007 ◽  
Vol 22 (06) ◽  
pp. 1201-1211
Author(s):  
MEILING YU ◽  
LIANSHOU LIU
Keyword(s):  
Neural Network ◽  
High Energy Physics ◽  
High Energy ◽  
Two Samples ◽  
High Energy Collisions ◽  
Boosting Technique ◽  
Single Neural Network ◽  
Energy Physics ◽  
Particle Classification

The possible application of boosted neural network to particle classification in high energy physics is discussed. A two-dimensional toy model, where the boundary between signal and background is irregular but not overlapping, is constructed to show how boosting technique works with neural network. It is found that boosted neural network not only decreases the error rate of classification significantly but also increases the efficiency and signal–background ratio. Besides, boosted neural network can avoid the disadvantage aspects of single neural network design. The boosted neural network is also applied to the classification of quark- and gluon-jet samples from Monte Carlo e+e- collisions, where the two samples show significant overlapping. The performance of boosting technique for the two different boundary cases — with and without overlapping is discussed.

Decisions, Decisions

2017 ◽  
Author(s):  
Kent W. Staley
Keyword(s):  
Higgs Boson ◽  
Scientific Inquiry ◽  
High Energy Physics ◽  
High Energy ◽  
Decision Problems ◽  
Pragmatic Approach ◽  
Value Judgments ◽  
Inductive Risk ◽  
Energy Physics

Much of the discussion of the argument from inductive risk (AIR) centers on scientific research that has relevance to policymaking. To emphasize that inductive risk pervades science, this chapter discusses the AIR in the context of high energy physics: specifically, the discovery of the Higgs boson, a scientific finding that is irrelevant to policy. The applicability of the AIR for the case of the Higgs boson is established through a pragmatic approach to scientific inquiry, emphasizing the centrality of practical decision problems to the production of scientific knowledge. This approach, drawing on debates among pragmatists over the interpretation of statistical inference, eschews the classification of value judgments into epistemic and non-epistemic.

scholarly journals Automatic Extraction of Figures from Scientific Publications in High-Energy Physics

2013 ◽  
Vol 32 (4) ◽  
pp. 25 ◽  
Author(s):  
Piotr Adam Praczyk ◽  
Javier Nogueras-Iso
Keyword(s):  
Digital Libraries ◽  
High Energy Physics ◽  
High Energy ◽  
Automatic Extraction ◽  
Scientific Publications ◽  
Clustering Techniques ◽  
Raster Image ◽  
Scholarly Publications ◽  
Pdf Format ◽  
Energy Physics

Plots and figures play an important role in the process of understanding a scientificpublication, providing overviews of large amounts of data or ideas that are difficult to in-tuitively present using only the text. State of art in digital libraries, serving as gatewaysto knowledge encoded in scholarly writings, does not take full advantage of the graphicalcontent of documents. Enabling machines to automatically unlock the meaning of scien-tific illustrations would allow immense improvements in the way scientists work and theknowledge is being processed.    In this paper we present a novel solution for the initial problem of processing graphicalcontent, obtaining figures from scholarly publications stored in PDF format. Our methodrelies on vector properties of documents and as such, does not introduce additional errors,characteristic for methods based on raster image processing. Emphasis has been placed oncorrectly processing documents in High Energy Physics. The described approach makesdistinction between different classes of objects appearing in PDF documents and usesspatial clustering techniques to group objects into larger logical entities. A number ofheuristics allow the rejection of incorrect figure candidates and the extraction of differenttypes of metadata.<br />

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