Research
My research focuses on particle physics and quantum field theory. A full list of publications is available on INSPIRE and Google Scholar.
Machine Learning
Machine learning has been a part of particle physics for at least 40 years. Our group has contributed to interpretable and unsupervised approaches, as well as practical tools for jet classification, pileup removal, and weak supervision.
- Observable optimization for precision theory: machine learning energy correlators
- Learning the simplicity of scattering amplitudes
- Reconstructing S-matrix Phases with Machine Learning
- Neural network field theories: non-Gaussianity, actions, and locality
- Simplifying Polylogarithms with Machine Learning
- Machine learning and LHC event generation
- Modern Machine Learning and Particle Physics
- Parameter inference from event ensembles and the top-quark mass
- Automating the ABCD method with machine learning
- Binary JUNIPR: an interpretable probabilistic model for discrimination
- JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics
- Jet Charge and Machine Learning
- Learning to classify from impure samples with high-dimensional data
- Pileup Mitigation with Machine Learning (PUMML)
- Deep learning in color: towards automated quark/gluon jet discrimination
- Pure Samples of Quark and Gluon Jets at the LHC
- Should artificial intelligence be interpretable to humans?
Quantum Field Theory
My primary focus is on understanding quantum field theory, from its foundations to its applications in particle physics, particularly the behavior of quantum field theories at long distances.
S-Matrix
Factorization
Forward Scattering
Factorization-Violating Effects
- Factorization Violation and Scale Invariance
- Nonglobal logarithms at three loops, four loops, five loops, and beyond
- The two-loop hemisphere soft function
Massive Gravity
- Effective field theory for massive gravitons and gravity in theory space
- Constructing gravitational dimensions
- Tensor mesons in AdS/QCD
Precision Standard Model Calculations
Vacuum Stability
The universe, as described by the Standard Model, is unstable to the formation of bubbles of negative energy density through quantum tunneling. With Anders Andreassen, David Farhi, and Will Frost, we resolved inconsistencies in previous treatments and produced analytic formulas for the tunneling rate.
Our estimate for the lifetime of the universe: Tuniverse = 10167 years.
Jet Physics
Jets are the experimental signatures of quarks and gluons. Our group has developed several widely-used techniques for jet analysis at the LHC.
Featured Papers
- Top Tagging: A Method for Identifying Boosted Hadronically Decaying Top Quarks
- Seeing in Color: Jet Superstructure
- Jet Charge at the LHC
- Precision Jet Substructure from Boosted Event Shapes
- Jet Sampling: Improving Event Reconstruction through Multiple Interpretations
- Qjets: A Non-Deterministic Approach to Tree-Based Jet Substructure
- Resummation for W and Z production at large pT
- Quark and Gluon Tagging at the LHC
- Constraining Light Colored Particles with Event Shapes
- Improving Jet Distributions with Effective Field Theory
- Infrared Lorentz violation and Slowly Instantaneous Electricity
- Unification and the Hierarchy from AdS5