Pair production of 125 GeV Higgs boson in the SM extension with color-octet scalars at the LHC

Although the Higgs boson mass and single production rate have been determined more or less precisely, its other properties may deviate significantly from its predictions in the standard model (SM) due to the uncertainty of Higgs data. In this work we study the Higgs pair production at the LHC in the Manohar-Wise model, which extends the SM by one family of color-octet and isospin-doublet scalars. We first scanned over the parameter space of the Manohar-Wise model considering exprimental constraints and performed fits in the model to the latest Higgs data by using the ATLAS and CMS data separately. Then we calculated the Higgs pair production rate and investigated the potential of its discovery at the LHC14. We conclude that: (i) Under current constrains including Higgs data after Run I of the LHC, the cross section of Higgs pair production in the Manohar-Wise model can be enhanced up to even $10^3$ times prediction in the SM. (ii) Moreover, the sizable enhancement comes from the contributions of the CP-odd color-octet scalar $S^A_I$. For lighter scalar $S^A_I$ and larger values of $|\lambda_I|$, the cross section of Higgs pair production can be much larger. (iii) After running again of LHC at 14 TeV, most of the parameter spaces in the Manohar-Wise model can be test. For an integrated luminosity of 100 fb$^{-1}$ at the LHC14, when the normalized ratio $R=10$, the process of Higgs pair production can be detected.Comment: 13 pages, 4 figure

A light Higgs scalar in the NMSSM confronted with the latest LHC Higgs data

In the Next-to-Minimal Supersymemtric Standard Model (NMSSM), one of the neutral Higgs scalars (CP-even or CP-odd) may be lighter than half of the SM-like Higgs boson. In this case, the SM-like Higgs boson h can decay into such a light scalar pair and consequently the diphoton and ZZ signal rates at the LHC will be suppressed. In this work, we examine the constraints of the latest LHC Higgs data on such a possibility. We perform a comprehensive scan over the parameter space of the NMSSM by considering various experimental constraints and find that the LHC Higgs data can readily constrain the parameter space and the properties of the light scalar, e.g., at 3 $\sigma$ level this light scalar should be highly singlet dominant and the branching ratio of the SM-like Higgs boson decay into the scalar pair should be less than about 30%. Also we investigate the detection of this scalar at various colliders. Through a detailed Monte Carlo simulation we find that under the constraints of the current Higgs data this light scalar can be accessible at the LHC-14 with an integrated luminosity over 300 fb$^{-1}$.Comment: Accepted by JHE

Bayesian imaging inverse problem with SA-Roundtrip prior via HMC-pCN sampler

Bayesian inference with deep generative prior has received considerable interest for solving imaging inverse problems in many scientific and engineering fields. The selection of the prior distribution is learned from, and therefore an important representation learning of, available prior measurements. The SA-Roundtrip, a novel deep generative prior, is introduced to enable controlled sampling generation and identify the data's intrinsic dimension. This prior incorporates a self-attention structure within a bidirectional generative adversarial network. Subsequently, Bayesian inference is applied to the posterior distribution in the low-dimensional latent space using the Hamiltonian Monte Carlo with preconditioned Crank-Nicolson (HMC-pCN) algorithm, which is proven to be ergodic under specific conditions. Experiments conducted on computed tomography (CT) reconstruction with the MNIST and TomoPhantom datasets reveal that the proposed method outperforms state-of-the-art comparisons, consistently yielding a robust and superior point estimator along with precise uncertainty quantification

Ultra-reliable communications for industrial internet of things : design considerations and channel modeling

Factory automation is the next industrial revolution. 5G and IIoT are enabling smart factories to seamlessly create a network of wirelessly connected machines and people that can instantaneously collect, analyze, and distribute real-time data. A 5G-enabled communication network for IIOT will boost overall efficiency, launching a new era of market opportunities and economic growth. This article presents the 5G-enabled system architecture and ultra-reliable use cases in smart factories associated with automated warehouses. In particular, for URLLC-based cases, key techniques and their corresponding solutions, including diversity for high reliability, short packets for low latency, and on-the-fly channel estimation and decoding for fast receiver processing, are discussed. Then the channel modeling requirements concerning technologies and systems are also identified in industrial scenarios. Ray tracing channel simulation can meet such requirements well, and based on that, the channel characteristic analysis is presented at 28 and 60 GHz for licensed and unlicensed band frequencies to exploit the available degrees of freedom in the channels. © 2012 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran” is provided in this record*

GEE-Based Ecological Environment Variation Analysis under Human Projects in Typical China Loess Plateau Region

The China Loess Plateau (CLP) is a unique geomorphological unit with abundant coal resources but a fragile ecological environment. Since the implementation of the Western Development plan in 2000, the Grain for Green Project (GGP), coal mining, and urbanization have been extensively promoted by the government in the CLP. However, research on the influence of these human projects on the ecological environment (EE) is still lacking. In this study, we investigated the spatial–temporal variation of EE in a typical CLP region using a Remote Sensing Ecological Index (RSEI) based on the Google Earth Engine (GEE). We obtained a long RSEI time series from 2002–2022, and used trend analysis and rescaled range analysis to predict changing trends in EE. Finally, we used Geodetector to verify the influence of three human projects (GGP, coal mining, and urbanization). Our results show that GGP was the major driving factor of ecological changes in the typical CLP region, while coal mining and urbanization had significant local effects on EE. Our research provides valuable support for ecological protection and sustainable social development in the relatively underdeveloped region of northwest China