Exploring the Higgs Sector of a Most Natural NMSSM and its Prediction on Higgs Pair Production at the LHC

As a most natural realization of the Next-to Minimal Supersymmetry Standard Model (NMSSM), {\lambda}-SUSY is parameterized by a large {\lambda} around one and a low tan$\beta$ below 10. In this work, we first scan the parameter space of {\lambda}-SUSY by considering various experimental constraints, including the limitation from the Higgs data updated by the ATLAS and CMS collaborations in the summer of 2014, then we study the properties of the Higgs bosons. We get two characteristic features of {\lambda}-SUSY in experimentally allowed parameter space. One is the triple self coupling of the SM-like Higgs boson may get enhanced by a factor over 10 in comparison with its SM prediction. The other is the pair production of the SM-like Higgs boson at the LHC may be two orders larger than its SM prediction. All these features seems to be unachievable in the Minimal Supersymmetric Standard Model and in the NMSSM with a low {\lambda}. Moreover, we also find that naturalness plays an important role in selecting the parameter space of {\lambda}-SUSY, and that the Higgs $\chi^2$ obtained with the latest data is usually significantly smaller than before due to the more consistency of the two collaboration measurements

Time-delayed impulsive control for discrete-time nonlinear systems with actuator saturation

This paper focuses on the problem of time-delayed impulsive control with actuator saturation for discrete-time dynamical systems. By establishing a delayed impulsive difference inequality, combining with convex analysis and inequality techniques, some sufficient conditions are obtained to ensure exponential stability for discrete-time dynamical systems via time-delayed impulsive controller with actuator saturation. The designed controller admits the existence of some transmission delays in impulsive feedback law, and the control input variables are required to stay within an availability zone. Several numerical simulations are also given to demonstrate the effectiveness of the proposed results.&nbsp

Properties of Heavy Higgs Bosons and Dark Matter under Current Experimental Limits in the μ\muNMSSM

Searches for new particles beyond the Standard Model (SM) are an important task for the Large Hadron Collider (LHC). In this paper, we investigate the properties of the heavy non-SM Higgs bosons in the $\mu$-term extended Next-to-Minimal Supersymmetric Standard Model ($\mu$NMSSM). We scan the parameter space of the $\mu$NMSSM considering the basic constraints from Higgs data, dark matter (DM) relic density, and LHC searches for sparticles. And we also consider the constraints from the LZ2022 experiment and the muon anomaly constraint at 2$\sigma$ level. We find that the LZ2022 experiment has a strict constraint on the parameter space of the $\mu$NMSSM, and the limits from the DM-nucleon spin-independent (SI) and spin-dependent (SD) cross-sections are complementary. Then we discuss the exotic decay modes of heavy Higgs bosons decaying into SM-like Higgs boson. We find that for doublet-dominated Higgs $h_3$ and $A_2$, the main exotic decay channels are $h_3\rightarrow Z A_1$, $h_3\rightarrow h_1 h_2$, $A_2\rightarrow A_1 h_1$ and $A_2\rightarrow Z h_2$, and the branching ratio can reach to about 23$\%$, 10$\%$, 35$\%$ and 10$\%$ respectively. At the 13 TeV LHC, the production cross-section of $ggF\rightarrow h_3\rightarrow h_1 h_2$ and $ggF\rightarrow A_2\rightarrow A_1 h_1$ can reach to about $10^{-11}$pb and $10^{-10}$pb, respectively

IID-GAN: an IID Sampling Perspective for Regularizing Mode Collapse

Despite its success, generative adversarial networks (GANs) still suffer from mode collapse, i.e., the generator can only map latent variables to a partial set of modes in the target distribution. In this paper, we analyze and seek to regularize this issue with an independent and identically distributed (IID) sampling perspective and emphasize that holding the IID property referring to the target distribution for generation can naturally avoid mode collapse. This is based on the basic IID assumption for real data in machine learning. However, though the source samples {z} obey IID, the generations {G(z)} may not necessarily be IID sampling from the target distribution. Based on this observation, considering a necessary condition of IID generation that the inverse samples from target data should also be IID in the source distribution, we propose a new loss to encourage the closeness between inverse samples of real data and the Gaussian source in latent space to regularize the generation to be IID from the target distribution. Experiments on both synthetic and real-world data show the effectiveness of our model.Comment: Accepted in IJCAI 202