Triplet Model Research Articles

Probing Inert Triplet Model at a multi-TeV muon collider via vector boson fusion with forward muon tagging

This study investigates the potential of a multi-TeV Muon Collider (MuC) for probing the Inert Triplet Model (ITM), which introduces a triplet scalar field with hypercharge Y = 0 to the Standard Model. The ITM stands out as a compelling Beyond the Standard Model scenario, featuring a neutral triplet T0 and charged triplets T±. Notably, T0 is posited as a dark matter (DM) candidate, being odd under a Z2 symmetry. Rigorous evaluations against theoretical, collider, and DM experimental constraints corner the triplet scalar mass to a narrow TeV-scale region, within which three benchmark points are identified, with T± masses of 1.21 TeV, 1.68 TeV, and 3.86 TeV, for the collider study. The ITM’s unique TTVV four-point vertex, differing from fermionic DM models, facilitates efficient pair production through Vector Boson Fusion (VBF). This characteristic positions the MuC as an ideal platform for exploring the ITM, particularly due to the enhanced VBF cross-sections at high collision energies. To address the challenge of the soft decay products of T± resulting from the narrow mass gap between T± and T0, we propose using Disappearing Charged Tracks (DCTs) from T± and Forward muons as key signatures. We provide event counts for these signatures at MuC energies of 6 TeV and 10 TeV, with respective luminosities of 4 ab−1 and 10 ab−1. Despite the challenge of beam-induced backgrounds contaminating the signal, we demonstrate that our proposed final states enable the MuC to achieve a 5σ discovery for the identified benchmark points, particularly highlighting the effectiveness of the final state with one DCT and one Forward muon.

Revolutionising sustainability using a new triplet: A system dynamic model

The theory of sustainability has been analysed and implemented in various sectors to minimise the consumption of limited resources and to consume the fullest potential of existing resources. The triple-bottom concept of sustainability covers all possible interactions within an ecosystem. However, the dynamic nature and interconnectivity of sustainability systems, such as the environmental, economic, and social systems, are quite complex to curb sustainability challenges. The modification in one system may create disturbances in other systems. Based on the existing studies on sustainability, this study explored how these systems can be optimised by analysing the relationship between each side of sustainability triangle by 3As, i.e., Adaptability, Affordability and Availability, to determine their impacting macro and micro flows. The reinforcement can be achieved between social-environment by adaptability, social-economics by affordability and economics-environment by availability. These 3As synergise sustainability systems if inflows and outflows in sustainability systems are optimised. The system dynamic approach was adopted to model and examine all possible 3As’ inflows and outflows comprehensively in urban ecosystems. Micro flows are analysed for the associated macro flows for each factor of triplet. The results suggest that although sustainability systems are complex but not wicked in nature, it is required to focus on macro and micro flows of adaptability, affordability and availability in each system. This study may serve as a source of information for improving and maintaining sustainability in industries, businesses and the policy makers to optimise their existing resources on this comprehensive triplet model.

Triplet attention-based deep learning model for hierarchical image classification of household items for robotic applications

Triplet attention-based deep learning model for hierarchical image classification of household items for robotic applications

Double Higgs boson production via photon fusion at muon colliders within the triplet Higgs model

In this paper, we present predictions for scattering cross section the of Higgs boson pair production via photon fusion at future muon colliders, focusing specifically the production processes μ+μ−→γγ→h0h0,A0A0. We investigated the impact of three choices the photon structure functions on cross-section predictions for a range model input parameters within the theoretical framework of the Higgs triplet model [; ; ; ]. Published by the American Physical Society 2024

A Triplet Multimodel Transfer Learning Network for Speech Disorder Screening of Parkinson’s Disease

Deterioration in the quality of a person’s voice and speech is an early sign of Parkinson’s disease (PD). Although a number of computer-based methods have been invested to use patients’ speech for early diagnosis of Parkinson’s disease, they only focus on a fixed pronunciation test, such as the subjects’ monosyllabic pronunciation is analyzed to determine whether they have potential possibility of PD. Moreover, only using traditional speech analysis methods to extract single-view speech features cannot provide a comprehensive feature representation. This paper is dedicated to the study of various pronunciation tests for patients with PD, including the pronunciation of five monosyllabic vowels and a spontaneous dialogue. A triplet multimodel transfer learning network is designed and proposed for identifying subjects with PD in these two groups of tests. First, multisource data extract mel frequency cepstrum coefficient (MFCC) features of speech for preprocessing. Subsequently, a pretrained triplet model represents features from three dimensions as the upstream task of the transfer learning framework. Finally, the pretrained model is reconstructed as a novel model that integrates the triplet model, temporal model, and auxiliary layer as the downstream task, and weights are updated through fine-tuning to identify abnormal speech. Experimental results show that the highest PD detection rates in the two groups of tests are 99% and 90% , respectively, which outperform a large number of internationally popular pattern recognition algorithms and serve as a baseline for other academic researchers in this field.

LINC00887 regulates malignant progression and T-cell chemotaxis in clear cell renal cell carcinoma by activating CD70 via recruitment of SPI1

BackgroundLINC00887 has been mentioned in several articles regarding its involvement in various cancers like nasopharyngeal carcinoma, lung cancer and glioma. However, the mechanism of LINC00887 in the malignant progression of clear cell renal cell carcinoma (ccRCC) is still unclear. The topic of our study is mainly centered on exploring how LINC00887 exactly affects ccRCC malignant progression. MethodsThe bioinformatics method predicted the downstream TF and target genes of LINC00887 by the “LncRNA-transcription factor (TF)-Gene” triplet model. RNA immunoprecipitation, chromatin immunoprecipitation analysis, and Dual-luciferase reporter assay determined the regulatory relationship between LINC00887 and its downstream genes. The LINC00887 expression and its downstream gene expression in ccRCC cells were examined by qRT-PCR and Western blot. The effect of LINC00887-SPI1-CD70 modulation axis on proliferative transfer, cell stemness and T cell chemotaxis of ccRCC cells was examined in cellular and animal experiments. ResultsOur research demonstrated an upregulation of LINC00887 in ccRCC, which facilitated tumor growth and stemness in vivo. In addition, LINC00887 could upregulate the CD70 expression by recruiting transcriptional factor SPI1. The results of in vitro experiments illustrated that the LINC00887-SPI1-CD70 regulatory axis facilitated ccRCC malignant progression by promoting cell stemness and hindering T-cell chemotaxis. ConclusionLINC00887, by recruiting SPI1, activated CD70 transcription, thereby propelling malignant progression and cell stemness and suppressing T cell chemotaxis in ccRCC. Based on our findings, we believed that the LINC00887-SPI1-CD70 regulatory axis had the potential to be a critical breakthrough for treating ccRCC.

Dimension-8 SMEFT analysis of minimal scalar field extensions of the Standard Model

We analyze the constraints obtainable from present data using the Standard Model Effective Field Theory (SMEFT) on extensions of the Standard Model with additional electroweak singlet or triplet scalar fields. We compare results obtained using only contributions that are linear in dimension-6 operator coefficients with those obtained including terms quadratic in these coefficients as well as contributions that are linear in dimension-8 operator coefficients. We also implement theoretical constraints arising from the stability of the electroweak vacuum and perturbative unitarity. Analyzing the models at the dimension-8 level constrains scalar couplings that are not bounded at the dimension-6 level. The strongest experimental constraints on the singlet model are provided by Higgs coupling measurements, whereas electroweak precision observables provide the strongest constraints on the triplet model. In the singlet model the present di-Higgs constraints already play a significant role. We find that the current constraints on model parameters are already competitive with those anticipated from future di- and tri-Higgs measurements. We compare our results with calculations in the full model, exhibiting the improvements when higher-order SMEFT terms are included. We also identify regions in parameter space where the SMEFT approximation appears to break down. We find that the combination of current constraints with the theoretical bounds still admits regions where the SMEFT approach is not valid, particularly for lower scalar boson masses.

The Political Determinants of Fiscal Governance in the EU: Towards a New Equilibrium

The article investigates the political determinants of fiscal governance in the EU. Since the outset of the Economic and Monetary Union, the EU adopted a model of fiscal regulation which attempted to keep government debt and deficit in check to avoid “fiscal dominance.” With the 2020 pandemic, the EU suspended the fiscal rules and adopted a program, Next Generation EU, having some features of a central fiscal capacity. On the bases of comparative federal analysis, the article discusses the political conditions that preside over the formation of a stable central fiscal capacity, here conceptualized as the “triple-T model.” We argue that, in unions of states, the determinants of a central fiscal capacity consist in the appearance of an existential threat, in the reciprocal trust among national governments for answering the threat with central resources, and an adequately long time planning horizon of national policymakers to apprehend the benefits of those common resources for all member states. On these bases, the article outlines the contour of a new EU fiscal set up which encompasses an EU central fiscal capacity and robust budget rules framing the fiscal choices of national authorities.

Sphaleron in the Higgs Triplet Model

The Higgs triplet model (HTM) extends the Standard Model (SM) by one complex triplet scalar (also known as the type-II seesaw model), offering a simple and viable way to account for nonzero neutrino masses. On the other hand, the nontrivial couplings of the triplet to the gauge fields and to the SM Higgs field are expected to influence the topological vacuum structure of the SM, and consequently, the energy and the field configuration of the electroweak sphaleron. The sphaleron process plays a crucial role in dynamically generating the baryon asymmetry of the Universe. In this work, we study the vacuum structure of the gauge and Higgs fields and calculate the saddle-point sphaleron configuration in the HTM. The coupled nonlinear equations of motion of the sphaleron are solved using the spectral method. We find the inclusion of the triplet scalar could in principle significantly change the sphaleron energy compared with the SM. Nevertheless, at zero temperature, the current stringent experimental constraint on the vacuum expectation value of the triplet suppresses the difference. Interestingly, we find that there still exists some narrow parameter space where the sphaleron energy can be enhanced up to 30% compared with the SM case.

Search for new heavy resonances decaying to WW, WZ, ZZ, WH, or ZH boson pairs in the all-jets final state in proton-proton collisions at [formula omitted

A search for new heavy resonances decaying to WW, WZ, ZZ, WH, or ZH boson pairs in the all-jets final state is presented. The analysis is based on proton-proton collision data recorded by the CMS detector in 2016–2018 at a centre-of-mass energy of 13 TeV at the CERN LHC, corresponding to an integrated luminosity of 138fb−1. The search is sensitive to resonances with masses between 1.3 and 6TeV, decaying to bosons that are highly Lorentz-boosted such that each of the bosons forms a single large-radius jet. Machine learning techniques are employed to identify such jets. No significant excess over the estimated standard model background is observed. A maximum local significance of 3.6 standard deviations, corresponding to a global significance of 2.3 standard deviations, is observed at masses of 2.1 and 2.9 TeV. In a heavy vector triplet model, spin-1 Z′ and W′ resonances with masses below 4.8TeV are excluded at the 95% confidence level (CL). These limits are the most stringent to date. In a bulk graviton model, spin-2 gravitons and spin-0 radions with masses below 1.4 and 2.7TeV, respectively, are excluded at 95% CL. Production of heavy resonances through vector boson fusion is constrained with upper cross section limits at 95% CL as low as 0.1fb.

Co-inversion of island leaf area index combination morphological and spectral parameters based on UAV multi-source remote sensing data

The precise and rapid estimation of leaf area index (LAI) is crucial for quantitative ecological monitoring and assessment, as it serves as an important ecological indicator of plant growth state and canopy structure. The acquisition, integration, and application of multiple remote sensing data from Unmanned Aerial Vehicle (UAV) platforms in quantitative ecological research is a current research hotspot. This study aims to explore the potential of combining morphological and spectral parameters using the symbolic regression (SR) algorithm with allometric models to improve the estimation of Leaf Area Index (LAI) at a regional scale of island ecosystem using UAV multi-source remote sensing data. The Canopy Height Model (CHM), Vegetation Density (VD), and Vegetation Indices (VIs), derived from UAV multi-platforms, were used as independent variables in the development of the retrieval models. Subsequently, regression equations were used to construct the Singlet, Duplet, and Triplet models. The precise assessments indicated that the triplet model demonstrates the highest accuracy, with a mean absolute error (MAE) of 9.48% and root mean square error (RMSE) of 0.2345. This improvement was observed compared to the singlet model (MAE = 19.55%, RMSE = 0.4091) and duplet model (MAE = 15.88%, RMSE = 0.3450), which can be attributed to incorporating morphological parameters to reduce the impact of tree height and leaf density. The analysis also showed that the estimated LAI accuracy is generally higher for shrubs and herbs compared to trees, and that lower plants with high vegetation coverage are better retrieved. The methodology advaneced in this study demonstrates significant potential and implications for ecological monitoring and assessment of island ecosystems, thereby expanding the research scope in the field of island ecology. Furthermore, it provides scientific evidence and technical support for decision-makers and scholars to propose and implement various measures for the conservation and restoration of island ecosystems.

STUDYING THE RESONANCE PRODUCTION CROSS-SECTION OF THE HEAVY VECTORS WITHIN HEAVY VECTOR TRIPLET MODEL

In the context of TeV-scale extensions of the Standard Model both the experimental data and the construction of phenomenological models for the new heavy bosons searches are used by us. Heavy particles are predicted by the Simplified Model to describe only the on-shell resonance, have to be compared with LHC data. Bosons V’ created and decay according to the process pp->V’->Vh (V=W, Z) have certain properties that can be modeled within the Heavy Vector Triplet model using the Madgraph computer program. We have calculated the production cross sections of heavy particles using the experimental constraints in the parameter space (cH, cF) imposed on the benchmark scenario. The nature of the functional dependence of the cross section on the mass of the new boson, as well as the mechanism for the heavy particle production is studied with variation of the model parameters.

A search for new resonances in multiple final states with a high transverse momentum Z boson in sqrt{s} = 13 TeV pp collisions with the ATLAS detector

A generic search for resonances is performed with events containing a Z boson with transverse momentum greater than 100 GeV, decaying into e+e− or μ+μ−. The analysed data collected with the ATLAS detector in proton-proton collisions at a centre-of-mass energy of 13 TeV at the Large Hadron Collider correspond to an integrated luminosity of 139 fb−1. Two invariant mass distributions are examined for a localised excess relative to the expected Standard Model background in six independent event categories (and their inclusive sum) to increase the sensitivity. No significant excess is observed. Exclusion limits at 95% confidence level are derived for two cases: a model-independent interpretation of Gaussian-shaped resonances with the mass width between 3% and 10% of the resonance mass, and a specific heavy vector triplet model with the decay mode W′ → ZW →ℓℓqq.

Search for heavy resonances decaying into a Z or W boson and a Higgs boson in final states with leptons and b-jets in 139 fb−1 of pp collisions at sqrt{s} = 13 TeV with the ATLAS detector

This article presents a search for new resonances decaying into a Z or W boson and a 125 GeV Higgs boson h, and it targets the nu overline{nu}boverline{b} , {ell}^{+}{ell}^{-}boverline{b} , or {ell}^{pm}nu boverline{b} final states, where ℓ = e or μ, in proton-proton collisions at sqrt{s} = 13 TeV. The data used correspond to a total integrated luminosity of 139 fb−1 collected by the ATLAS detector during Run 2 of the LHC at CERN. The search is conducted by examining the reconstructed invariant or transverse mass distributions of Zh or Wh candidates for evidence of a localised excess in the mass range from 220 GeV to 5 TeV. No significant excess is observed and 95% confidence-level upper limits between 1.3 pb and 0.3 fb are placed on the production cross section times branching fraction of neutral and charged spin-1 resonances and CP-odd scalar bosons. These limits are converted into constraints on the parameter space of the Heavy Vector Triplet model and the two-Higgs-doublet model.

Higgs-like particle decays into γZ and γγ: Fingerprints of some non-supersymmetric models

Recently, ATLAS and CMS experiments at the LHC put on light the relevant results in the measurement precision of the Higgs and BSM. In such a report, where the resonance direct search was made in the γZ channel, a mass adjustment distribution for the reconstructed Z boson and photon was established. Thus, simultaneously with the signal-from-background separation, the number of events has been perfectly described, and an excess signal approximately twice that expected by the Standard Model (SM) has been noticed, which is equivalent to a significance of 2.2 standard deviations. In this study, we examine how any possible new physics models can explain this excess, such as the CP-conserving Two-Higgs doublet model (2HDMs), the Inert doublet model (IDM), and the Higgs triplet model (HTM). While considering the available theoretical and most recent experimental constraints, the phenomenological implications of existing extensions beyond the SM are discussed, and prospects for precision studies of these processes are described. We have found that the excess could be explained in the BSM frameworks studied, depending on the charged and double-charged Higgs boson masses.

Bearing Fault Diagnosis Based on Small Sample Learning of Maml–Triplet

Since the emergence of artificial intelligence and deep learning methods, the fault diagnosis of bearings in rotating machinery has gradually been realized, reducing the high costs of bearing faults. However, in the actual work of the equipment, faults rarely occur, resulting in less fault data. Therefore, it is necessary to study small sample fault data. For the case of less fault data, the Maml–Triplet fault classification learning framework based on the combination of maml and the triplet neural network is proposed. In the framework of Maml-Triplet fault classification, firstly, an initial signal feature extractor is obtained using the Maml training method. Secondly, the feature vectors corresponding to signal data are obtained using depth distance measurement learning in the triplet neural network, and the fault type is judged based on the feature vectors of unknown signal. The results show that the accuracy of the Maml–Triplet model is 2% higher than that of the triplet model alone and 5% higher than that of the Maml–CNN meta learning method. When there are fewer data samples, the accuracy gap is more obvious. Therefore, in the case of less data, the Maml–Triplet model has an excellent fault identification ability.

Triplet Loss-Based Models for COVID-19 Detection from Vocal Sounds.

This work focuses on the automatic detection of COVID-19 from the analysis of vocal sounds, including sustained vowels, coughs, and speech while reading a short text. Specifically, we use the Mel-spectrogram representations of these acoustic signals to train neural network-based models for the task at hand. The extraction of deep learnt representations from the Mel-spectrograms is performed with Convolutional Neural Networks (CNNs). In an attempt to guide the training of the embedded representations towards more separable and robust inter-class representations, we explore the use of a triplet loss function. The experiments performed are conducted using the Your Voice Counts dataset, a new dataset containing German speakers collected using smartphones. The results obtained support the suitability of using triplet loss-based models to detect COVID-19 from vocal sounds. The best Unweighted Average Recall (UAR) of 66.5 % is obtained using a triplet loss-based model exploiting vocal sounds recorded while reading.

Implication of the W boson mass anomaly at CDF II in the Higgs triplet model with a mass difference

A new report for the measurement of the W boson mass has been provided by the CDF II experiment. The measured value of the W boson mass is given to be mWCDF II=80.4335±0.0094 GeV which shows 7σ deviation from the standard model prediction, while the other groups of high energy experiments, e.g., ATLAS and D0 II, show consistent results with the latter. Assuming that the measurement at CDF II is correct, we discuss the possibility to explain the anomaly in the Higgs triplet model. We find that the anomaly can be explained under the constraints from the electroweak rho parameter, the effective weak mixing angle, the partial width of the leptonic Z decay and current direct searches at LHC when non-zero mass differences among the triplet-like Higgs bosons and the vacuum expectation value of the triplet Higgs field are taken to be O(100) GeV and O(1) GeV, respectively.

Laser-assisted doubly charged Higgs pair production in Higgs triplet model (HTM)

In the framework of Higgs triplet model (HTM), we study the pair production process of doubly charged Higgs bosons via $e^{+}e^{-}$ annihilation in the presence of a laser field with circular polarization . We begin our work by presenting the theoretical calculation of the differential cross section in the centre of mass frame including both $Z$ and $\gamma$ diagrams. Then, from the numerical analysis of the production cross section's dependence on the laser field parameters, we have shown that the laser-assisted total cross section decreases as far as the electromagnetic field intensity enhances or by decreasing its frequency. Finally, we analyze the variation of the total cross section versus the mass of the doubly charged Higgs boson by fixing the laser field parameters and the centre of mass energy, and we have found that the order of magnitude of the cross section decreases as long as $M_{H^{\pm\pm}}$ increases.

Characterizing Heterogeneous Internet of Things Devices at Internet Scale Using Semantic Extraction

Along with the rapid-growth number of Internet of Things (IoT) devices, significant security concerns are raised due to the hidden vulnerabilities among them. Illuminating the characteristics of online devices would shed a light on protecting these potential vulnerable devices. State-of-arts methodologies enumerate devices characteristics as keywords and rules and match them with IoT network data. However, the heterogeneous implementations of IoT devices introduce intricate characteristics features, which impede the large-scale identification. In this work, we close this gap and present a semantic extraction-based approach that can automatically and effectively characterize online devices. We leverage the observation that IoT devices can be identified by analyzing the semantic information of the network packets. Specifically, we first collect the network data of IoT devices and utilize a co-training algorithm to annotate the data. We propose a residual dilate gated convolutional neural network (RDGCNN)-based encoder to extract semantic features from the annotated data. Then, we put forward an entity relationship-based decoder to generate the characteristic triplet (type, brand, and model) of IoT devices by decoding extracted features. We have implemented the prototype of the system and conducted real-world experiments to evaluate the performance. Results show that our approach achieves 92.16% precision and 86.79% recall. In addition, we apply our proposed method to characterize 15 millions IoT devices on the Internet.