scholarly journals Neural network parametrization of spectral functions from hadronic tau decays and determination of QCD vacuum condensates

2004 ◽  
Vol 2004 (01) ◽  
pp. 055-055 ◽  
Author(s):  
Joan Rojo ◽  
Jose I Latorre
Keyword(s):  
Neural Network ◽  
Spectral Functions ◽  
Tau Decays ◽  
Qcd Vacuum ◽  
Hadronic Tau ◽  
Vacuum Condensates

scholarly journals αs analyses from hadronic tau decays with OPAL and ALEPH data

2016 ◽  
Vol 31 (26) ◽  
pp. 1630024 ◽  
Author(s):  
Diogo Boito ◽  
Maarten Golterman ◽  
Kim Maltman ◽  
Santiago Peris
Keyword(s):  
Strong Coupling ◽  
Data Sets ◽  
Higher Dimension ◽  
Spectral Functions ◽  
Combined Analysis ◽  
Tau Decays ◽  
Opal Data ◽  
Self Consistent ◽  
Hadronic Tau

Recently, we extracted the strong coupling, [Formula: see text], from the revised ALEPH data for non-strange hadronic tau decays. Our analysis is based on a method previously used for the determination of the strong coupling from OPAL data. In our strategy, we employ different moments of the spectral functions both with and without pinching, including duality violations, in order to obtain fully self-consistent analyses that do not rely on untested assumptions (such as the smallness of higher dimension contributions in the operation product expansion (OPE)). Here we discuss the [Formula: see text] values obtained from the ALEPH and the OPAL data, the robustness of the analysis, as well as non-perturbative contributions from DVs and the OPE. We show that, although the [Formula: see text] determination is sound, non-perturbative effects limit the accuracy with which one can extract the strong coupling from tau decay data. Finally, we discuss the compatibility of the data sets and the possibility of a combined analysis.

scholarly journals V–A hadronic tau decays: A laboratory for the QCD vacuum

2005 ◽  
Vol 624 (3-4) ◽  
pp. 223-232 ◽  
Author(s):  
Stephan Narison
Keyword(s):  
Tau Decays ◽  
Qcd Vacuum ◽  
Hadronic Tau

scholarly journals DETERMINATION OF THE STRONG COUPLING FROM HADRONIC TAU DECAYS USING RENORMALIZATION GROUP SUMMED PERTURBATION THEORY

2013 ◽  
Vol 28 (24) ◽  
pp. 1360004 ◽  
Author(s):  
GAUHAR ABBAS ◽  
B. ANANTHANARAYAN ◽  
IRINEL CAPRINI
Keyword(s):  
Renormalization Group ◽  
Strong Coupling ◽  
Coupling Constant ◽  
Convergence Properties ◽  
Tau Decays ◽  
Higher Order Terms ◽  
Borel Transformation ◽  
Adler Function ◽  
Hadronic Tau

We determine the strong coupling constant αs from the τ hadronic width using a renormalization group summed (RGS) expansion of the QCD Adler function. The main theoretical uncertainty in the extraction of αs is due to the manner in which renormalization group invariance is implemented, and the as yet uncalculated higher order terms in the QCD perturbative series. We show that new expansion exhibits good renormalization group improvement and the behavior of the series is similar to that of the standard CIPT expansion. The value of the strong coupling in [Formula: see text] scheme obtained with the RGS expansion is [Formula: see text]. The convergence properties of the new expansion can be improved by Borel transformation and analytic continuation in the Borel plane. This is discussed elsewhere in these issues.

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

Determination of Age, Gender, Dress Color and Type of a Person by Convolutional Neural Network (CNN)

2020 ◽  
Author(s):  
CSN Koushik ◽  
Shruti Bhargava Choubey ◽  
Abhishek Choubey ◽  
D. Naresh ◽  
N. Bhanu Prakash Reddy
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network
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