Chrome Extension
WeChat Mini Program
Use on ChatGLM

Updated Global Fit of the Aligned Two-Higgs-doublet Model with Heavy Scalars

PHYSICAL REVIEW D(2024)

UV | INFN

Cited 0|Views12
Abstract
An updated global fit on the parameter-space of the aligned two-Higgs-doublet model is performed with the help of the open-source package HEPfit, assuming the Standard-Model Higgs to be the lightest scalar. No new sources of CP violation, other than the phase in the Cabibbo-Kobayashi-Maskawa matrix of the Standard Model, are considered. A similar global fit was previously performed by O. Eberhardt et al. [Global fits in the aligned two-Higgs-doublet model, J. High Energy Phys. 05 (2021) 005] with a slightly different set of parameters. Our updated fit incorporates improved analyses of the theoretical constraints required for the perturbative unitarity and boundedness of the scalar potential from below, additional flavor observables and updated data on direct searches for heavy scalars at the LHC, Higgs signal strengths, and electroweak precision observables. Although not included in the main fit, the implications of the CDF measurement of the W +/- mass are also discussed.
More
Translated text
Key words
Conformal Symmetry
PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Related Papers
V. Khachatryan, A. M. Sirunyan, A. Tumasyan, W. Adam, T. Bergauer, M. Dragicevic, J. Erö, C. Fabjan, M. Friedl, R. Frühwirth, V. M. Ghete, C. Hartl,
2014

被引用902 | 浏览

A. M. Sirunyan, A. Tumasyan, W. Adam, F. Ambrogi, E. Asilar, T. Bergauer, J. Brandstetter, E. Brondolin, M. Dragicevic, J. Eroe, A. Escalante Del Valle, M. Flechl,
2018

被引用28 | 浏览

A. M. Sirunyan, A. Tumasyan, W. Adam, F. Ambrogi, E. Asilar, T. Bergauer, J. Brandstetter, M. Dragicevic, J. Eroe, A. Escalante Del Valle, M. Flechl, R. Fruehwirth,
2018

被引用259 | 浏览

Aaboud M., Aad G., Abbott B., Abdinov O., Abeloos B., Abhayasinghe D. K., Abidi S. H., AbouZeid O. S., Abraham N. L., Abramowicz H., Abreu H., Abulaiti Y.,
2019

被引用42 | 浏览

A. M. Sirunyan, A. Tumasyan, W. Adam, F. Ambrogi, T. Bergauer, J. Brandstetter, M. Dragicevic, J. Erö, A. Escalante Del Valle, M. Flechl, R. Frühwirth, M. Jeitler,
2020

被引用89 | 浏览

Sirunyan A. M., Tumasyan A., Adam W., Ambrogi F., Bergauer T., Brandstetter J., Dragicevic M., Erö J., Escalante Del Valle A., Flechl M., Vienna University of Technology, Krammer N.,
2020

被引用74 | 浏览

G. Aad, B. Abbott, D. C. Abbott, A. Abed Abud, K. Abeling, D. K. Abhayasinghe, S. H. Abidi, O. S. AbouZeid, N. L. Abraham, H. Abramowicz, H. Abreu, Y. Abulaiti,
2021

被引用26 | 浏览

Tumasyan A., Adam W., Andrejkovic J. W., Bergauer T., Chatterjee S., Damanakis K., Dragicevic M., Escalante Del Valle A., Frühwirth R., Jeitler M., Krammer N., Lechner L.,
2022

被引用8 | 浏览

Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest