Hadronic Decays Involving Heavy Pentaquarks

Recently several experiments have reported evidences for pentaquark $\Theta^+$. H1 experiment at HERA-B has also reported evidence for $\Theta_c$. $\Theta^+$ is interpreted as a bound state of an $\bar s$ with other four light quarks $udud$ which is a member of the anti-decuplet under flavor $SU(3)_f$. While $\Theta_c$ is a state by replacing the $\bar s$ in $\Theta^+$ by a $\bar c$. One can also form $\Theta_b$ by replacing the $\bar s$ by a $\bar b$. The charmed and bottomed heavy pentaquarks form triplets and anti-sixtets under $SU(3)_f$. We study decay processes involving at least one heavy pentaquark using $SU(3)_f$ and estimate the decay widths for some decay modes. We find several relations for heavy pentaquarks decay into another heavy pentaquark and a $B (B^*)$ or a $D(D^*)$ which can be tested in the future. $B$ can decay through weak interaction to charmed heavy pentaquarks. We also study some $B$ decay modes with a heavy pebtaquark in the final states. Experiments at the current $B$ factories can provide important information about the heavy pentaquark properties.Comment: RevTex 20 pages. Revised version. Discussions on the recent H1 data and new references adde

MCTNet: A Multi-Scale CNN-Transformer Network for Change Detection in Optical Remote Sensing Images

For the task of change detection (CD) in remote sensing images, deep convolution neural networks (CNNs)-based methods have recently aggregated transformer modules to improve the capability of global feature extraction. However, they suffer degraded CD performance on small changed areas due to the simple single-scale integration of deep CNNs and transformer modules. To address this issue, we propose a hybrid network based on multi-scale CNN-transformer structure, termed MCTNet, where the multi-scale global and local information are exploited to enhance the robustness of the CD performance on changed areas with different sizes. Especially, we design the ConvTrans block to adaptively aggregate global features from transformer modules and local features from CNN layers, which provides abundant global-local features with different scales. Experimental results demonstrate that our MCTNet achieves better detection performance than existing state-of-the-art CD methods