Two component dark matter with multi-Higgs portals

With the assistance of two extra groups, i.e., an extra hidden gauge group $SU(2)_D$ and a global $U(1)$ group, we propose a two component dark matter (DM) model. After the symmetry $SU(2)_D\times U(1)$ being broken, we obtain both the vector and scalar DM candidates. The two DM candidates communicate with the standard model (SM) via three Higgs as multi-Higgs portals. The three Higgs are mixing states of the SM Higgs, the Higgs of the hidden sector and real part of a supplement complex scalar singlet. We study relic density and direct detection of DM in three scenarios. The resonance behaviors and interplay between the two component DM candidates are represented through investigating of the relic density in the parameter spaces of the two DMs masses. The electroweak precision parameters constrains the two Higgs portals couplings ($\lambda_m$ and $\delta_2$). The relevant vacuum stability and naturalness problem in the parameter space of $\lambda_m$ and $\delta_2$ are studied as well. The model could alleviate these two problems in some parameter spaces under the constraints of electroweak precision observables and Higgs indirect search.Comment: 27 pages, 16 figures. Version accepted for publication in JHE

Single-Shot Refinement Neural Network for Object Detection

For object detection, the two-stage approach (e.g., Faster R-CNN) has been achieving the highest accuracy, whereas the one-stage approach (e.g., SSD) has the advantage of high efficiency. To inherit the merits of both while overcoming their disadvantages, in this paper, we propose a novel single-shot based detector, called RefineDet, that achieves better accuracy than two-stage methods and maintains comparable efficiency of one-stage methods. RefineDet consists of two inter-connected modules, namely, the anchor refinement module and the object detection module. Specifically, the former aims to (1) filter out negative anchors to reduce search space for the classifier, and (2) coarsely adjust the locations and sizes of anchors to provide better initialization for the subsequent regressor. The latter module takes the refined anchors as the input from the former to further improve the regression and predict multi-class label. Meanwhile, we design a transfer connection block to transfer the features in the anchor refinement module to predict locations, sizes and class labels of objects in the object detection module. The multi-task loss function enables us to train the whole network in an end-to-end way. Extensive experiments on PASCAL VOC 2007, PASCAL VOC 2012, and MS COCO demonstrate that RefineDet achieves state-of-the-art detection accuracy with high efficiency. Code is available at https://github.com/sfzhang15/RefineDetComment: 14 pages, 7 figures, 7 table