Based on the jet image approach, which treats the energy deposition in each calorimeter cell as the pixel intensity, the Convolutional neural network (CNN) method has been found to achieve a sizable improvement in jet tagging compared to the traditional jet substructure analysis. In this work, the Mask R-CNN framework is adopted to reconstruct Higgs jets in collider-like events, with the effects of pileup contamination taken into account. This automatic jet reconstruction method achieves higher efficiency of Higgs jet detection and higher accuracy of Higgs boson four-momentum reconstruction than traditional jet clustering and jet substructure tagging methods. Moreover, the Mask R-CNN trained on events containing a single Higgs jet is capable of detecting one or more Higgs jets in events of several different processes, without apparent degradation in reconstruction efficiency and accuracy. The outputs of the network also serve as new handles for the \( t\overline{t} \) background suppression, complementing to traditional jet substructure variables. A preprint version of the article is available at ArXiv.

We for the first time obtain the analytical solution for the quirk equation of motion in an approximate way. Based on it, we study several features of quirk trajectory in a more precise way, including quirk oscillation amplitude, number of periods, as well as the thickness of quirk pair plane. Moreover, we find an exceptional case where the quirk crosses at least one of the tracking layers repeatedly. Finally, we consider the effects of ionization energy loss and fixed direction of infracolor string for a few existing searches.

A quirk propagating through a detector is subject to the Lorentz force, a new confining gauge force, and the frictional force from ionization energy loss. At the LHC, it was found that the monojet search and the coplanar search were able to constrain such a quirk signal. Inspired by the coplanar search proposed by Knapen et al. [ Phys. Rev. D 96, 115015 (2017)], we develop a new search that also utilizes the information of the relatively large ionization energy loss inside the tracker. Our algorithm has improved efficiency in finding quirk signals with a wide oscillation amplitude. Because of our trigger strategy, the $Z(\to \nu \nu )+\text{jets}$ process overlaid by pileup events is the dominant background. We find that the $\sim 100{\mathrm{fb}}^{-1}$ dataset at the LHC will be able to probe the colored fermion (scalar) quirks with masses up to 2.1 (1.1) TeV and the color neutral fermion (scalar) quirks with masses up to 450 (150) GeV, respectively.

We consider a $$SU(3)_c \times SU(3)_L \times U(1)_X$$S U ( 3 ) c × S U ( 3 ) L × U ( 1 ) X model from a SU(6) Grand Unified Theory (GUT). In order to explain the anomalous magnetic moments of muon and electron, we introduce two new scalar triplets without vacuum expectation values (VEVs) so that the leading contributions to $$\Delta a_{\mu }$$Δ a μ and $$\Delta a_{e}$$Δ a e can avoid the suppression from small muon mass. In addition, the Higgs potential stability of this 331 model is studied by giving a set of sufficient conditions to ensure the boundedness from below of the potential.

We propose the $SU(3{)}_C\times SU(3{)}_L\times U(1{)}_X$ model arising from $SU(6)$ breaking. One family of the Standard Model (SM) fermions arises from two $\overline{6}$ representations and one 15 representation of $SU(6)$ gauge symmetry. To break the $SU(3{)}_C\times SU(3{)}_L\times U(1{)}_X$ gauge symmetry down to the SM, we introduce three $SU(3{)}_L$ triplet Higgs fields, where two of them come from the $\overline{6}$ representation while the other one from the 15 representation. We study the gauge boson masses and Higgs boson mass in detail, and find that the vacuum expectation value (VEV) of the Higgs field for $SU(3{)}_L\times U(1{)}_X$ gauge symmetry breaking is around 10 TeV. The neutrino masses and mixing can be generated via the littlest inverse seesaw mechanism. In particular, we have normal hierarchy for neutrino masses and the lightest active neutrino is massless. Also, we consider constraints from the charged lepton flavor changing decays as well. Furthermore, introducing two $SU(3{)}_L$ adjoint fermions, one $SU(3{)}_C$ adjoint scalar, and one $SU(3{)}_L$ triplet scalar, we can achieve gauge coupling unification within 1%. These extra particles can provide a dark matter candidate as well.

The quirk particle carries Lorentz force and long-range infracolor force, while suffers relatively large ionization energy loss inside the detector. It can be indirectly constrained by mono-jet search or directly search through co-planar hits if the confinement scale is not too low ($\Lambda \gtrsim 100$ eV). Considering the ionization energy loss inside tracker, we improve the co-planar search. We also will solve the equation of motion for quirks numerically by including all of the important contributions. Based on our selection strategy, the $\sim 100$ fb$^{-1}$ dataset at the LHC will be able to probe the colored fermion/scalar quirks with masses up to {2.1/1.1 TeV}, and the color neutral fermion/scalar quirks with masses up to {450/150 GeV}, respectively.