LHC Search of New Higgs Boson via Resonant Di-Higgs Production with Decays into 4W

Searching for new Higgs particle beyond the observed light Higgs boson h(125GeV) will unambiguously point to new physics beyond the standard model. We study the resonant production of a CP-even heavy Higgs state $H^0$ in the di-Higgs channel via, $gg\to H^0\to h^0h^0\to WW^*WW^*$, at the LHC Run-2 and the high luminosity LHC (HL-LHC). We analyze two types of the $4W$ decay modes, one with the same-sign di-leptons ($4W\to\ell^\pm\nu\ell^\pm\nu 4q$) and the other with tri-leptons ($4W\to\ell^\pm\nu\ell^\mp\nu\ell^\pm\nu 2q$). We perform a full simulation for the signals and backgrounds, and estimate the discovery potential of the heavy Higgs state at the LHC Run-2 and the HL-LHC, in the context of generical two-Higgs-doublet models (2HDM). We determine the viable parameter space of the 2HDM as allowed by the theoretical constraints and the current experimental limits. We systematically analyze the allowed parameter space of the 2HDM which can be effectively probed by the heavy Higgs searches of the LHC, and further compare this with the viable parameter region under the current theoretical and experimental bounds.Comment: v3: JHEP published version, 34pp, 10 Figs(36 plots) and 9 Tables. Only minor typos fixed, references added. v2: JHEP version. All results and conclusions un-changed, discussions and references added. (This update is much delayed due to author's traveling and flu.

Magic-angle Twisted Bilayer Systems with Quadratic-Band-Touching: Exactly Flat Bands with High-Chern Number

Studies of twisted morie systems have been mainly focused on 2D materials like graphene with Dirac points and transition-metal-dichalcogenide so far. Here we propose a new twisted bilayer of 2D systems which feature quadratic-band-touching points and find exotic physics different from previously studied twisted morie systems. Specifically, we show that exactly flat bands can emerge at magic angles and, more interestingly, each flat band exhibits a high Chern number ($C=\pm 2$) which was not realized in bilayer morie systems before. We further consider the effect of Coulomb interactions in such magic-angle twisted system and find that the ground state supports the quantum anomalous Hall effect with quantized Hall conductivity $2\frac{e^2}{hc}$ at certain filling. Furthermore, possible physical realization of such twisted bilayer systems will be briefly discussed.Comment: 4.6 pages + references + supplemental, 4 figure

Hidden itinerant-spin phase in heavily-overdoped La2-xSrxCuO4 revealed by dilute Fe doping: A combined neutron scattering and angle-resolved photoemission study

We demonstrated experimentally a direct way to probe a hidden propensity to the formation of spin density wave (SDW) in a non-magnetic metal with strong Fermi surface nesting. Substituting Fe for a tiny amount of Cu (1%) induced an incommensurate magnetic order below 20 K in heavily-overdoped La2-xSrxCuO4 (LSCO). Elastic neutron scattering suggested that this order cannot be ascribed to the localized spins on Cu or doped Fe. Angle-resolved photoemission spectroscopy (ARPES), combined with numerical calculations, revealed a strong Fermi surface nesting inherent in the pristine LSCO that likely drives this order. The heavily-overdoped Fe-doped LSCO thus represents the first plausible example of the long-sought "itinerant-spin extreme" of cuprates, where the spins of itinerant doped holes define the magnetic ordering ground state. This finding complements the current picture of cuprate spin physics that highlights the predominant role of localized spins at lower dopings. The demonstrated set of methods could potentially apply to studying hidden density-wave instabilities of other "nested" materials on the verge of density wave ordering.Comment: Abstract and discussion revised; to appear in Phys. Rev. Let

MiChao-HuaFen 1.0: A Specialized Pre-trained Corpus Dataset for Domain-specific Large Models

With the advancement of deep learning technologies, general-purpose large models such as GPT-4 have demonstrated exceptional capabilities across various domains. Nevertheless, there remains a demand for high-quality, domain-specific outputs in areas like healthcare, law, and finance. This paper first evaluates the existing large models for specialized domains and discusses their limitations. To cater to the specific needs of certain domains, we introduce the ``MiChao-HuaFen 1.0'' pre-trained corpus dataset, tailored for the news and governmental sectors. The dataset, sourced from publicly available internet data from 2022, underwent multiple rounds of cleansing and processing to ensure high quality and reliable origins, with provisions for consistent and stable updates. This dataset not only supports the pre-training of large models for Chinese vertical domains but also aids in propelling deep learning research and applications in related fields.Comment: 4 pages,2 figure

Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents

Large language models (LLMs) have dramatically enhanced the field of language intelligence, as demonstrably evidenced by their formidable empirical performance across a spectrum of complex reasoning tasks. Additionally, theoretical proofs have illuminated their emergent reasoning capabilities, providing a compelling showcase of their advanced cognitive abilities in linguistic contexts. Critical to their remarkable efficacy in handling complex reasoning tasks, LLMs leverage the intriguing chain-of-thought (CoT) reasoning techniques, obliging them to formulate intermediate steps en route to deriving an answer. The CoT reasoning approach has not only exhibited proficiency in amplifying reasoning performance but also in enhancing interpretability, controllability, and flexibility. In light of these merits, recent research endeavors have extended CoT reasoning methodologies to nurture the development of autonomous language agents, which adeptly adhere to language instructions and execute actions within varied environments. This survey paper orchestrates a thorough discourse, penetrating vital research dimensions, encompassing: (i) the foundational mechanics of CoT techniques, with a focus on elucidating the circumstances and justification behind its efficacy; (ii) the paradigm shift in CoT; and (iii) the burgeoning of language agents fortified by CoT approaches. Prospective research avenues envelop explorations into generalization, efficiency, customization, scaling, and safety. This paper caters to a wide audience, including beginners seeking comprehensive knowledge of CoT reasoning and language agents, as well as experienced researchers interested in foundational mechanics and engaging in cutting-edge discussions on these topics. A repository for the related papers is available at https://github.com/Zoeyyao27/CoT-Igniting-Agent

Electronic Structures of Graphene Layers on Metal Foil: Effect of Point Defects

Here we report a facile method to generate a high density of point defects in graphene on metal foil and show how the point defects affect the electronic structures of graphene layers. Our scanning tunneling microscopy (STM) measurements, complemented by first principle calculations, reveal that the point defects result in both the intervalley and intravalley scattering of graphene. The Fermi velocity is reduced in the vicinity area of the defect due to the enhanced scattering. Additionally, our analysis further points out that periodic point defects can tailor the electronic properties of graphene by introducing a significant bandgap, which opens an avenue towards all-graphene electronics.Comment: 4 figure

The Glauber model and flow analysis with Pb-Pb collisions at sNN \sqrt{s_{\rm NN}}=2.76 TeV

This work presents data analysis on Pb-Pb collisions at $\sqrt{s_{\rm NN}}$=2.76 TeV with centrality $40\%-50\%$. We present introduction and Monte-Carlo simulation results of the Glauber model, which shed light on the initial geometric configuration of heavy ion collisions. Three-dimensional correlation function is plotted, and Fourier decomposition is carried out in order to obtain elliptic flow. Based on the assumption that non-flow effect is less prominent in long-range area, we separate it from the second Fourier decomposition of two-particle correlation function by making polynomial curve fitting.Comment: 10 pages,8 figures, revisions are made, accepted by ICAPM 2022 Conference Proceeding

Investigating Zcs(3985)Z_{cs}(3985) and Zcs(4000)Z_{cs}(4000) exotic states in Λb→Zcs−p\Lambda_b\to Z^-_{cs}p decays

We study the $Z_{cs}(3985)$ and $Z_{cs}(4000)$ exotic states in the decays of $\Lambda_b$ baryons through a molecular scenario. In the final state interaction, the $\Lambda_b\to \Lambda_c D_s^{(*)-}$ decays are followed by the $\Lambda_c D_s^{(*)-}$ to $Z^-_{cs}p$ rescatterings via exchange of a $D^{(*)}$ meson. We predict a branching fraction of $(3.1^{+1.4}_{-2.6})\times 10^{-4}$ for $\Lambda_b\to Z^-_{cs}p$, which can be measured in the $\Lambda_b\to J/\psi K^{(*)-}p$ decay. This study provides insights into the nature of exotic hadrons and their production mechanisms, and guides future experimental searches for the $Z_{cs}(3985)$ and $Z_{cs}(4000)$.Comment: 11 pages, 3 figure

Learning Enhanced Resolution-wise features for Human Pose Estimation

Recently, multi-resolution networks (such as Hourglass, CPN, HRNet, etc.) have achieved significant performance on pose estimation by combining feature maps of various resolutions. In this paper, we propose a Resolution-wise Attention Module (RAM) and Gradual Pyramid Refinement (GPR), to learn enhanced resolution-wise feature maps for precise pose estimation. Specifically, RAM learns a group of weights to represent the different importance of feature maps across resolutions, and the GPR gradually merges every two feature maps from low to high resolutions to regress final human keypoint heatmaps. With the enhanced resolution-wise features learnt by CNN, we obtain more accurate human keypoint locations. The efficacies of our proposed methods are demonstrated on MS-COCO dataset, achieving state-of-the-art performance with average precision of 77.7 on COCO val2017 set and 77.0 on test-dev2017 set without using extra human keypoint training dataset.Comment: Published on ICIP 202