Study of the dilepton electromagnetic decays of $$\chi _{cJ}(1P)$$

Wang, Xuan-He; Jiang, Yue; Wang, Tianhong; Tan, Xiao-Ze; Li, Geng; Wang, Guoli

doi:10.1140/epjc/s10052-019-7531-8

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…There have been multiple applications of machine learning in motion planning. For instance, methods learn a sampling distribution to bias sampling along a desired trajectory for a specific planning problem or environment [9]- [11]. Work has also been performed to find end-to-end solutions from expert trajectories [12], [13].…”

Section: Related Workmentioning

confidence: 99%

Data-Efficient Learning of High-Quality Controls for Kinodynamic Planning used in Vehicular Navigation

Karten,

Sivaramakrishnan,

Granados

et al. 2022

Preprint

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This paper aims to improve the path quality and computational efficiency of kinodynamic planners used for vehicular systems. It proposes a learning framework for identifying promising controls during the expansion process of sampling-based motion planners for systems with dynamics. Offline, the learning process is trained to return the highestquality control that reaches a local goal state (i.e., a waypoint) in the absence of obstacles from an input difference vector between its current state and a local goal state. The data generation scheme provides bounds on the target dispersion and uses state space pruning to ensure high-quality controls. By focusing on the system's dynamics, this process is data efficient and takes place once for a dynamical system, so that it can be used for different environments with modular expansion functions. This work integrates the proposed learning process with a) an exploratory expansion function that generates waypoints with biased coverage over the reachable space, and b) proposes an exploitative expansion function for mobile robots, which generates waypoints using medial axis information. This paper evaluates the learning process and the corresponding planners for a first and second-order differential drive systems. The results show that the proposed integration of learning and planning can produce better quality paths than kinodynamic planning with random controls in fewer iterations and computation time.

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Section: Related Workmentioning

confidence: 99%

Data-Efficient Learning of High-Quality Controls for Kinodynamic Planning used in Vehicular Navigation

Karten,

Sivaramakrishnan,

Granados

et al. 2022

Preprint

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“…[19][20][21][22][23][24] Because of the extremely complex geometries of biomolecules, it is vital to increase the accuracy and efficiency of PBE-based models for nontrivial applications in biomolecular simulations. [25][26][27][28][29][30][31][32][33] For biomolecular applications, it is impossible to solve the PBE analytically. Instead, we rely on numerical solutions.…”

Section: Introductionmentioning

confidence: 99%

Robustness and Efficiency of Poisson–Boltzmann Modeling on Graphics Processing Units

Luo

2018

J. Chem. Inf. Model.

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Poisson-Boltzmann equation (PBE)-based continuum electrostatics models have been widely used in modeling electrostatic interactions in biochemical processes, particularly in estimating proteinligand binding affinities. Fast convergence of PBE solvers is crucial in binding affinity computations as numerous snapshots need to be processed. Efforts have been reported to develop PBE solvers on graphics processing units (GPUs) for efficient modeling of biomolecules, though only relatively simple successive over-relaxation and conjugate gradient methods were implemented. However, neither convergence nor scaling properties of the two methods are optimal for large biomolecules. On the other hand, geometric multigrid (MG) has been shown to be an optimal solver on CPUs, though no MG was reported for biomolecular applications on GPUs. This is not a surprise as it is a more complex method and depends on simpler but limited iterative methods such as Gauss-Seidel in its core relaxation procedure. The robustness and efficiency of MG on GPUs are also unclear. Here we present an implementation and a thorough analysis of MG on GPUs. Our analysis shows that robustness is a more pronounced issue than efficiency for both MG and other tested solvers when the single precision is used for complex biomolecules. We further show how to balance robustness and efficiency by utilizing MG's overall efficiency and conjugate gradient's robustness, pointing to a hybrid GPU solver with a good balance of efficiency and accuracy. The new PBE solver will significantly improve the computational throughput for a range of biomolecular applications on the GPU platforms.

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Radiative transitions of χcJ → ψγ and χbj → Υγ

Pei,

Li,

Wang

et al. 2024

J. High Energ. Phys.

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In the framework of instantaneous Bethe-Salpeter equation, according to the JPC of quarkonia, we find that their wave functions all contain multiple partial waves, rather than pure waves. In the radiative electromagnetic transitions χcJ→γψ and χbJ→γΥ (J = 0, 1, 2), the main wave of quarkonium gives the non-relativistic contribution, while other waves provide the relativistic corrections. Our results indicate that the relativistic effect of charmonium, especially highly excited states, is significant. Such as the relativistic effects of χcJ(2P) → γψ(1S) (J = 0, 1, 2) are {49.7%, 30.9%, 37.5%}, much larger than the corresponding {17.8%, 7.08%, 12.9%} of χbJ(2P) → γΥ(1S). The decay of χcJ(2P) → γψ can be used to distinguish between χc0(3860) and χc0(3915), which particle is the charmonium χc0(2P). Although our result of χc1(3872)→γψ(2S) is consistent with data, but the one of χc1(3872)→γψ(1S) is much larger than data, so whether χc1(3872) is the conventional χc1(2P) remains an open question. The undiscovered Υ(1D) and Υ(2D) have large production rates in decays of χb0(2P) → γΥ(1D) and χbJ(3P) → γΥ(2D) (J = 0, 1), respectively. To search for χbJ(3P) (J = 0, 1, 2), the most competitive channels are the decays χbJ(3P) → γΥ(3S). And the best way to find χb2(1F) is to search for the decay of χb2(1F) → γΥ(1D).

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Study of the dilepton electromagnetic decays of $$\chi _{cJ}(1P)$$

Cited by 6 publications

References 22 publications

Data-Efficient Learning of High-Quality Controls for Kinodynamic Planning used in Vehicular Navigation

Data-Efficient Learning of High-Quality Controls for Kinodynamic Planning used in Vehicular Navigation

Robustness and Efficiency of Poisson–Boltzmann Modeling on Graphics Processing Units

Radiative transitions of χcJ → ψγ and χbj → Υγ

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