Signals of New Gauge Bosons in Gauged Two Higgs Doublet Model

Recently a gauged two Higgs doublet model, in which the two Higgs doublets are embedded into the fundamental representation of an extra local $SU(2)_H$ group, is constructed. Both the new gauge bosons $Z^\prime$ and $W^{\prime (p,m)}$ are electrically neutral. While $Z^\prime$ can be singly produced at colliders, $W^{\prime (p,m)}$, which is heavier, must be pair produced. We explore the constraints of $Z^\prime$ using the current Drell-Yan type data from the Large Hadron Collider. Anticipating optimistically that $Z^\prime$ can be discovered via the clean Drell-Yan type signals at high luminosity upgrade of the collider, we explore the detectability of extra heavy fermions in the model via the two leptons/jets plus missing transverse energy signals from the exotic decay modes of $Z^\prime$. For the $W^{\prime (p,m)}$ pair production in a future 100 TeV proton-proton collider, we demonstrate certain kinematical distributions for the two/four leptons plus missing energy signals have distinguishable features from the Standard Model background. In addition, comparisons of these kinematical distributions between the gauged two Higgs doublet model and the littlest Higgs model with T-parity, the latter of which can give rise to the same signals with competitive if not larger cross sections, are also presented.Comment: 39 pages, 23 figures, 7 tables and two new appendixes, to appear in EPJ

Weakly-supervised Caricature Face Parsing through Domain Adaptation

A caricature is an artistic form of a person's picture in which certain striking characteristics are abstracted or exaggerated in order to create a humor or sarcasm effect. For numerous caricature related applications such as attribute recognition and caricature editing, face parsing is an essential pre-processing step that provides a complete facial structure understanding. However, current state-of-the-art face parsing methods require large amounts of labeled data on the pixel-level and such process for caricature is tedious and labor-intensive. For real photos, there are numerous labeled datasets for face parsing. Thus, we formulate caricature face parsing as a domain adaptation problem, where real photos play the role of the source domain, adapting to the target caricatures. Specifically, we first leverage a spatial transformer based network to enable shape domain shifts. A feed-forward style transfer network is then utilized to capture texture-level domain gaps. With these two steps, we synthesize face caricatures from real photos, and thus we can use parsing ground truths of the original photos to learn the parsing model. Experimental results on the synthetic and real caricatures demonstrate the effectiveness of the proposed domain adaptation algorithm. Code is available at: https://github.com/ZJULearning/CariFaceParsing .Comment: Accepted in ICIP 2019, code and model are available at https://github.com/ZJULearning/CariFaceParsin

A model explaining neutrino masses and the DAMPE cosmic ray electron excess

We propose a flavored $U(1)_{e\mu}$ neutrino mass and dark matter~(DM) model to explain the recent DArk Matter Particle Explorer (DAMPE) data, which feature an excess on the cosmic ray electron plus positron flux around 1.4 TeV. Only the first two lepton generations of the Standard Model are charged under the new $U(1)_{e\mu}$ gauge symmetry. A vector-like fermion $\psi$, which is our DM candidate, annihilates into $e^{\pm}$ and $\mu^{\pm}$ via the new gauge boson $Z'$ exchange and accounts for the DAMPE excess. We have found that the data favors a $\psi$ mass around 1.5~TeV and a $Z'$ mass around 2.6~TeV, which can potentially be probed by the next generation lepton colliders and DM direct detection experiments.Comment: 7 pages, 3 figures. V2: version accepted by Physics Letters