◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

Kun Wang

21 papers hereh-index 9248 citations28 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author8
  • middle author12

Across the 20 of 21 papers where every author was matched, so the position is known.

fields
  • hep-ph20
  • cond-mat.str-el1
same name
  • Kun Wang — 19 papers, h 3
  • Kun Wang — 18 papers, h 11
  • Kun Wang — 17 papers, h 7
  • Kun Wang — 15 papers, h 5
  • Kun Wang — 15 papers, h 5
  • Kun Wang — 14 papers, h 12

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20182026
most citedSolving the muon g-2 anomaly in CMSSM extension with non-universal gaugino masses

31 citations · 160 across the 21 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

hep-ph2020★ 8 cited

Higgsino Asymmetry and Direct-Detection Constraints of Light Dark Matter in the NMSSM with Non-Universal Higgs Masses

Kun Wang, Jingya Zhu, Quanlin Jie

In this work, we study the direct-detection constraints of light dark matter in the next-to minimal supersymmetric standard model (NMSSM) with non-universal Higgs masses (NUHM), es…

hep-ph2020★ 2 cited

Higgs decay to light scalars in the semi-constrained NMSSM

Shiquan Ma, Kun Wang, Jingya Zhu

The next-to minimal supersymmetric standard model (NMSSM) with non-universal Higgs masses, or the semi-constrained NMSSM (scNMSSM), extend the minimal supersymmetric standard model…

hep-ph2020★ 21 cited

Funnel annihilations of light dark matter and the invisible decay of the Higgs boson

Kun Wang, Jingya Zhu

The semi-constrained NMSSM (scNMSSM), or NMSSM with non-universal Higgs masses, can naturally predict a light dark matter under current constraints including Higgs data, sparticle-…

hep-ph2020★ 15 cited

A Novel Scenario in the Semi-constrained NMSSM

Kun Wang, Jingya Zhu

In this work, we develop a novel efficient scan method, combining the Heuristically Search (HS) and the Generative Adversarial Network (GAN), where the HS can shift marginal sample…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.