arXiv subjects

Lu Meng

Publications and source records attributed to Lu Meng.

75 records · Page 5Linked to original sources

arXiv2017

Deuteron-like states composed of two doubly charmed baryons

We present a systematic investigation of the possible molecular states composed of a pair of doubly charmed baryons ($Ξ_{cc}Ξ_{cc}$) or one doubly charmed baryon and one doubly charmed antibaryon $(Ξ_{cc}\barΞ_{cc})$ within the framework of the one-boson-exchange-potential model. For the spin-triplet systems, we take into account the mixing between the ${}^3S_1$ and ${}^3D_1$ channels. For the baryon-baryon system $Ξ_{cc}Ξ_{cc}$ with $(R,I) = (\bar{3}, 1/2)$ and $(\bar{3}, 0)$, where $R$ and $I$ represent the group representation and the isospin of the system, respectively, there exist loosely bound molecular states. For the baryon-antibaryon system $Ξ_{cc}\barΞ_{cc}$ with $(R,I) = (8, 1)$, $(8, 1/2)$ and $(8,0)$, there also exist deuteron-like molecules. The $B_{cc}\bar{B}_{cc}$ molecular states may be produced at LHC. The proximity of their masses to the threshold of two doubly charmed baryons provides a clean clue to identify them.

hep-ph↗

arXiv2017

Exotic Hadron Bound State Production at Hadronic Colliders

The non-relativistic wave function framework is applied to study the production and decay of the exotic hadrons which can be effectively described as bound states of other hadrons. Employing the factorized formulation, we investigate the production of exotic hadrons in the multiproduction processes at high energy hadronic colliders with the help of event generators. This study provides crucial information for the measurements of the relevant exotic hadrons.

hep-ph↗

arXiv2016

Phase-Aligned Spectral Filtering for Decomposing Spatiotemporal Dynamics

Spatiotemporal dynamics is central to a wide range of applications from climatology, computer vision to neural sciences. From temporal observations taken on a high-dimensional vector of spatial locations, we seek to derive knowledge about such dynamics via data assimilation and modeling. It is assumed that the observed spatiotemporal data represent superimposed lower-rank smooth oscillations and movements from a generative dynamic system, mixed with higher-rank random noises. Separating the signals from noises is essential for us to visualize, model and understand these lower-rank dynamic systems. It is also often the case that such a lower-rank dynamic system have multiple independent components, corresponding to different trends or functionalities of the system under study. In this paper, we present a novel filtering framework for identifying lower-rank dynamics and its components embedded in a high dimensional spatiotemporal system. It is based on an approach of structural decomposition and phase-aligned construction in the frequency domain. In both our simulated examples and real data applications, we illustrate that the proposed method is able to separate and identify meaningful lower-rank movements, while existing methods fail.

stat.ME↗