531 citations · 7.1k across the 80 of their papers we have counts for
49 papers · 1 filter
VBF vs. GGF Higgs with Full-Event Deep Learning: Towards a Decay-Agnostic Tagger
Cheng-Wei Chiang, David Shih, Shang-Fu Wei
We study the benefits of jet- and event-level deep learning methods in distinguishing vector boson fusion (VBF) from gluon-gluon fusion (GGF) Higgs production at the LHC. We show t…
New directions for surrogate models and differentiable programming for High Energy Physics detector simulation
Andreas Adelmann, Walter Hopkins, Evangelos Kourlitis +8
The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources. To overcome this challenge,…
Machine Learning and LHC Event Generation
Anja Butter, Tilman Plehn, Steffen Schumann +48
First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predicti…
Dark Photons and Displaced Vertices at the MUonE Experiment
Iftah Galon, David Shih, Isaac R. Wang
MUonE is a proposed experiment designed to measure the hadronic vacuum polarization contribution to muon through elastic scattering. As such it employs an extremely hig…
Resolving Combinatorial Ambiguities in Dilepton Event Topologies with Neural Networks
Haider Alhazmi, Zhongtian Dong, Li Huang +3
We study the potential of deep learning to resolve the combinatorial problem in SUSY-like events with two invisible particles at the LHC. As a concrete example, we focus on dilepto…
Machine Learning in the Search for New Fundamental Physics
Georgia Karagiorgi, Gregor Kasieczka, Scott Kravitz +2
Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics. We review the state of machine learning methods and applications for new…