2,756 research outputs found
Probing Transverse Momentum Broadening via Dihadron and Hadron-jet Angular Correlations in Relativistic Heavy-ion Collisions
Dijet, dihadron, hadron-jet angular correlations have been reckoned as
important probes of the transverse momentum broadening effects in relativistic
nuclear collisions. When a pair of high-energy jets created in hard collisions
traverse the quark-gluon plasma produced in heavy-ion collisions, they become
de-correlated due to the vacuum soft gluon radiation associated with the
Sudakov logarithms and the medium-induced transverse momentum broadening. For
the first time, we employ the systematical resummation formalism and establish
a baseline calculation to describe the dihadron and hadron-jet angular
correlation data in and peripheral collisions where the medium effect
is negligible. We demonstrate that the medium-induced broadening and the so-called jet quenching parameter can be
extracted from the angular de-correlations observed in collisions. A
global analysis of dihadron and hadron-jet angular correlation data
renders the best fit for a
quark jet at RHIC top energy. Further experimental and theoretical efforts
along the direction of this work shall significantly advance the quantitative
understanding of transverse momentum broadening and help us acquire
unprecedented knowledge of jet quenching parameter in relativistic heavy-ion
collisions.Comment: 6 pages, 3 figure
Research of dimensionless index for fault diagnosis positioning based on EMD
Dimensionless index as a new theory tool has been applied in fault diagnosis study, which has shown some progress, however, it will cause some interference to the diagnosis results since no considering the influence of other noise jamming signal is given. Empirical Mode Decomposition (EMD) technique could extract effectively the fault characteristic signal of vibration data. In view of the noise jamming of dimensionless index in analyzing data, dimensionless index processing algorithms based on EMD is proposed. Firstly, EMD method is used to decompose the collected vibration signals, then the first few Intrinsic Mode Functions (IMF) components are obtained which contains the fault characteristic of vibration data, and the effects of other noise signal are removed at the same time. Secondly, fault diagnosis can be achieved by calculating dimensionless parameter values to the IMF components with characteristic signal of vibration data, and obtaining range of characteristic value of their dimensionless index, then diagnosing and analyzing fault characteristics of the equipment. The proposed method is applied to fault diagnosis test analysis of rotating machinery, and the experiment has shown that the proposed method is efficient and effective
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