Strong decays of the XYZXYZ states

Through the spin rearrangement scheme in the heavy quark limit, we have performed a comprehensive investigation of the decay pattern and production mechanism of the hidden beauty di-meson states, which are either composed of a P-wave bottom meson and an S-wave bottom meson or two S-wave bottom mesons. We further extend the corresponding formula to discuss the decay behavior of some charmonium-like states by combining the experimental information with our numerical results. The typical ratios presented in this work can be measured by future experiments like BESIII, Belle, LHCb and the forthcoming BelleII, which shall provide important clues to the inner structures of the exotic states.Comment: 21pages, 12table

Leptonic ZZ decays in the littlest HiggsHiggs model with T-parity

The littlest $Higgs$ model with T-parity (called the $LHT$ model) predicts the existence of the T-odd leptons, which can generate contributions to some leptonic processes at the one-loop level. We calculate their contributions to the leptonic $Z$ decay processes $Z\to l\bar{l'}$, $Z\to l\bar{l}$, and Z\rightarro \nu\bar{\nu}. We find that the T-odd leptons can give significant contributions to the branching ratios of these decay processes in most of the parameter space. The experimental measurement values might generate constraints on the free parameters of the $LHT$ model.Comment: 16 pages, 8 figures, minor corrections; final version published in Phys.Rev.

Adversarial Image Generation and Training for Deep Neural Networks

Deep neural networks (DNNs) have achieved great success in image classification, but they may be very vulnerable to adversarial attacks with small perturbations to images. Moreover, the adversarial training based on adversarial image samples has been shown to improve the robustness and generalization of DNNs. The aim of this paper is to develop a novel framework based on information-geometry sensitivity analysis and the particle swarm optimization to improve two aspects of adversarial image generation and training for DNNs. The first one is customized generation of adversarial examples. It can design adversarial attacks from options of the number of perturbed pixels, the misclassification probability, and the targeted incorrect class, and hence it is more flexible and effective to locate vulnerable pixels and also enjoys certain adversarial universality. The other is targeted adversarial training. DNN models can be improved in training with the adversarial information using a manifold-based influence measure effective in vulnerable image/pixel detection as well as allowing for targeted attacks, thereby exhibiting an enhanced adversarial defense in testing

Probing the XYZXYZ states through radiative decays

In this work, we have adopted the spin rearrangement scheme in the heavy quark limit and extensively investigated three classes of the radiative decays: $\mathfrak{M}\to (b\bar{b})+\gamma$, $(b\bar{b})\to \mathfrak{M}+\gamma$, $\mathfrak{M} \to \mathfrak{M}^\prime+\gamma$, corresponding to the electromagnetic transitions between one molecular state and bottomonium, one bottomonium and molecular state, and two molecular states respectively. We also extend the same formalism to study the radiative decays of the molecular states with hidden charm. We have derived some model independent ratios when the initial or final states belong to the same spin flavor multiplet. Future experimental measurement of these ratios will test the molecular picture and explore the underlying structures of the $XYZ$ states.Comment: 21 pages, 10 tables Accepted by Phys.Rev.