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 $pp$ and peripheral $AA$ collisions where the medium effect is negligible. We demonstrate that the medium-induced broadening $\langle p_\perp^2\rangle$ and the so-called jet quenching parameter $\hat q$ can be extracted from the angular de-correlations observed in $AA$ collisions. A global $\chi^2$ analysis of dihadron and hadron-jet angular correlation data renders the best fit $\langle p_\perp^2 \rangle \sim 13~\textrm{GeV}^2$ 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