Search

The doubly charmed baryon $\Xi_{cc}^{++}$ has been observed by LHCb through the non-leptonic decay modes of $\Xi_{cc}^{++}\to\Lambda_{c}^{+}K^{-}\pi^{+}\pi^{+}$ and $\Xi_{c}^{+}\pi^{+}$ in 2017. After that, the experimentalists turn their attention to finding other doubly charmed baryons $\Xi_{cc}^{+}$ and $\Omega_{cc}^{+}$. In this work, we investigate the nonleptonic weak decays of doubly charmed baryons ${\cal B}_{cc}\to{\cal B}_{c}P$, where ${\cal B}_{cc}$ denotes the doubly charmed baryons $(\Xi_{cc}^{++},\Xi_{cc}^{+},\Omega_{cc}^{+})$, ${\cal B}_{c}$ represents the singly charmed baryons $({\cal B}_{\bar{3}},{\cal B}_{6})$ and $P$ is the light pseudoscalar. For these non-leptonic decay modes, their short-distance contributions can be accurately estimated in theoretical calculations. However, dealing with the long-distance contributions for final-state-interaction effects is challenging. To address this, we use the rescattering mechanism to calculate the long-distance contributions and first derive the whole hadronic loop contributions for these two-body nonleptonic decays of doubly charmed baryons. Then the decay widths and branching ratios of the 45 nonleptonic decays of doubly charmed baryon are predicted. Among that, the ratio of the branching ratios ${\cal RB}=\frac{{\cal B}(\Xi_{cc}^{++}\to\Xi_{c}^{\prime+}\pi^{+})}{{\cal B}(\Xi_{cc}^{++}\to\Xi_{c}^{+}\pi^{+})}=1.15\pm0.45$ is consistent with the experimental results within statistical errors., Comment: 22 pages, 4 figures

In this work we investigate the exclusive production of a pair of light neutral mesons in $e^+e^-$ annihilation, where the final state bears an even $C$-parity. The production processes can be initiated via the photon fragmentation or the non-fragmentation mechanism. While the fragmentation contribution can be rigorously accounted, the non-fragmentation contributions are calculated within the framework of collinear factorization, where only the leading-twist light-cone distribution amplitudes (LCDAs) of mesons are considered. Mediately solely by the non-fragmentation mechanism, the production rates of double light neutral pseudoscalar mesons are too small to be observed at the commissioning $e^+e^-$ facilities. In contrast, the production rates of a pair of light neutral vector mesons are greatly amplified owing to the significant kinematic enhancement brought by the fragmentation mechanism. It is found that, at $\sqrt{s}=3.77$ GeV, after including the destructive interference between the non-fragmentation and fragmentation contributions, the production rates for $e^+e^-\to \rho^{0}\rho^{0}$ and $\rho^0\omega$ can be lowered by about 10\% and 30\% relative to the fragmentation predictions. Future precise measurement of these exclusive double neutral vector meson production channels at {\tt BESIII} experiment may provide useful constraints on the LCDAs of light vector mesons., Comment: 20 pages, 4 tables, 6 figures

The conventional wisdom in dealing with electromagnetic transition between heavy quarkonia is the multipole expansion, when the emitted photon has a typical energy of order quarkonium binding energy. Nevertheless, in the case when the energy carried by the photon is of order typical heavy quark momentum, the multipole expansion doctrine is expected to break down. In this work, we apply the "hard-scattering" approach originally developed to tackle the strongly hindered magnetic dipole ($M1$) transition [Y.~Jia {\it et al.}, Phys. \ Rev. \ D. 82, 014008 (2010)] to the strongly hindered electric dipole ($E1$) transition between heavy quarkonia. We derive the factorization formula for the strongly hindered $E1$ transition rates at the lowest order in velocity and $\alpha_s$ in the context of the non-relativistic QCD (NRQCD), and conduct a detailed numerical comparison with the standard predictions for various bottomonia and charmonia $E1$ transition processes., Comment: 18 pages, 2 figures, 4 tables

High-order topological phases of matter refer to the systems of $n$-dimensional bulk with the topology of $m$-th order, exhibiting $(n-m)$-dimensional boundary modes and can be characterized by topological pumping. Here, we experimentally demonstrate two types of second-order topological pumps, forming four 0-dimensional corner localized states on a 4$\times$4 square lattice array of 16 superconducting qubits. The initial ground state of the system for half-filling, as a product of four identical entangled 4-qubit states, is prepared using an adiabatic scheme. During the pumping procedure, we adiabatically modulate the superlattice Bose-Hubbard Hamiltonian by precisely controlling both the hopping strengths and on-site potentials. At the half pumping period, the system evolves to a corner-localized state in a quadrupole configuration. The robustness of the second-order topological pump is also investigated by introducing different on-site disorder. Our work studies the topological properties of high-order topological phases from the dynamical transport picture using superconducting qubits, which would inspire further research on high-order topological phases.

The purpose of unconditional text generation is to train a model with real sentences, then generate novel sentences of the same quality and diversity as the training data. However, when different metrics are used for comparing the methods of unconditional text generation, contradictory conclusions are drawn. The difficulty is that both the diversity and quality of the sample should be considered simultaneously when the models are evaluated. To solve this problem, a novel metric of distributional discrepancy (DD) is designed to evaluate generators based on the discrepancy between the generated and real training sentences. However, it cannot compute the DD directly because the distribution of real sentences is unavailable. Thus, we propose a method for estimating the DD by training a neural-network-based text classifier. For comparison, three existing metrics, bi-lingual evaluation understudy (BLEU) versus self-BLEU, language model score versus reverse language model score, and Fr\'{e}chet embedding distance, along with the proposed DD, are used to evaluate two popular generative models of long short-term memory and generative pretrained transformer 2 on both syntactic and real data. Experimental results show that DD is significantly better than the three existing metrics for ranking these generative models.

The autoregressive language model (ALM) trained with maximum likelihood estimation (MLE) is widely used in unconditional text generation. Due to exposure bias, the generated texts still suffer from low quality and diversity. This presents statistically as a discrepancy between the real text and generated text. Some research shows a discriminator can detect this discrepancy. Because the discriminator can encode more information than the generator, discriminator has the potentiality to improve generator. To alleviate the exposure bias, generative adversarial networks (GAN) use the discriminator to update the generator's parameters directly, but they fail by being evaluated precisely. A critical reason for the failure is the difference between the discriminator input and the ALM input. We propose a novel mechanism by adding a filter which has the same input as the discriminator. First, discriminator detects the discrepancy signals and passes to filter directly (or by learning). Then, we use the filter to reject some generated samples with a sampling-based method. Thus, the original generative distribution is revised to reduce the discrepancy. Two ALMs, RNN-based and Transformer-based, are experimented. Evaluated precisely by three metrics, our mechanism consistently outperforms the ALMs and all kinds of GANs across two benchmark data sets.