What is he best known for?
The fields of study he is best known for:
- Particle physics
- Electron
- Standard Model
His primary areas of study are Particle physics, Dark matter, Astrophysics, Standard Model and Light dark matter.
His study in Higgs boson, Elementary particle, Supersymmetry, Superpartner and Large Hadron Collider falls under the purview of Particle physics.
His Higgs boson research incorporates elements of Top quark, Quark and Particle Data Group.
His Quark study integrates concerns from other disciplines, such as Minimal Supersymmetric Standard Model and Big Bang.
His Dark matter study combines topics in areas such as COSMIC cancer database, Weakly interacting massive particles and Neutrino, Nuclear physics.
As part of one scientific family, Jonathan L. Feng deals mainly with the area of Standard Model, narrowing it down to issues related to the Quantum chromodynamics, and often Collider and Electroweak interaction.
His most cited work include:
- Review of particle properties. (4466 citations)
- Review of Particle Physics: Particle data group (4198 citations)
- Review of Particle Physics (3135 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Particle physics, Supersymmetry, Dark matter, Large Hadron Collider and Astrophysics.
His studies in Superpartner, Supersymmetry breaking, Gravitino, Higgs boson and Physics beyond the Standard Model are all subfields of Particle physics research.
His Higgs boson research focuses on Naturalness and how it connects with Renormalization group.
The Supersymmetry study combines topics in areas such as Symmetry breaking and Nuclear physics.
The study incorporates disciplines such as Cosmology, Weakly interacting massive particles and Light dark matter in addition to Dark matter.
Jonathan L. Feng combines subjects such as Quantum chromodynamics, Quark and Elementary particle with his study of Standard Model.
He most often published in these fields:
- Particle physics (85.61%)
- Supersymmetry (39.11%)
- Dark matter (34.69%)
What were the highlights of his more recent work (between 2015-2021)?
- Particle physics (85.61%)
- Large Hadron Collider (21.40%)
- Physics beyond the Standard Model (14.02%)
In recent papers he was focusing on the following fields of study:
Jonathan L. Feng focuses on Particle physics, Large Hadron Collider, Physics beyond the Standard Model, Dark matter and Nuclear physics.
In most of his Particle physics studies, his work intersects topics such as Warm dark matter.
Jonathan L. Feng works mostly in the field of Large Hadron Collider, limiting it down to topics relating to Neutrino and, in certain cases, Electron.
The concepts of his Physics beyond the Standard Model study are interwoven with issues in Standard Model, Supersymmetry and Collider.
His Higgs boson course of study focuses on Lepton and Fermion and Gluon.
His Minimal Supersymmetric Standard Model research is multidisciplinary, incorporating elements of Gravitino and Elementary particle.
Between 2015 and 2021, his most popular works were:
- ForwArd Search ExpeRiment at the LHC (204 citations)
- US Cosmic Visions: New Ideas in Dark Matter 2017: Community Report (183 citations)
- Physics beyond colliders at CERN : beyond the Standard Model working group report (156 citations)
In his most recent research, the most cited papers focused on:
- Electron
- Particle physics
- Standard Model
Jonathan L. Feng mainly investigates Particle physics, Large Hadron Collider, Physics beyond the Standard Model, Boson and Nuclear physics.
Jonathan L. Feng studies Dark matter, a branch of Particle physics.
His Dark matter research incorporates themes from COSMIC cancer database and Neutrino.
His Standard Model research extends to the thematically linked field of Large Hadron Collider.
His research in Physics beyond the Standard Model intersects with topics in Astroparticle physics, Collider and Two-photon physics.
His Boson research focuses on Higgs boson and how it relates to Lepton, Meson and Pion.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
