4 citations · 6 across the 6 of their papers we have counts for
6 papers
Efficient Event Generation for High-Multiplicity LHC Processes: An End-to-End GPU Workflow with Normalizing Flows
Enrico Bothmann, Joshua Isaacson, Claudius Krause +3
Producing very large unweighted event samples for high-multiplicity processes is limited by expensive matrix-element evaluations and low unweighting efficiencies. We present the fi…
Proton Structure from Neural Simulation-Based Inference at the LHC
Ricardo Barrué, Lisa Benato, Ali Kaan Güven +10
The precise determination of the parton distribution functions (PDFs) of the proton is an essential ingredient for LHC analyses, including for those at the upcoming High-Luminosity…
Higgs Signal Strength Estimation with Machine Learning under Systematic Uncertainties
Minxuan He, Claudius Krause, Daohan Wang
We present a dedicated graph neural network (GNN)-based methodology for the extraction of the Higgs boson signal strength , incorporating systematic uncertainties. The architect…
Unbinned inclusive cross-section measurements with machine-learned systematic uncertainties
Lisa Benato, Cristina Giordano, Claudius Krause +5
We introduce a novel methodology for addressing systematic uncertainties in unbinned inclusive cross-section measurements and related collider-based inference problems. Our approac…
BitHEP -- The Limits of Low-Precision ML in HEP
Claudius Krause, Daohan Wang, Ramon Winterhalder
The increasing complexity of modern neural network architectures demands fast and memory-efficient implementations to mitigate computational bottlenecks. In this work, we evaluate…
FAIR Universe HiggsML Uncertainty Dataset and Competition
Lisa Benato, Wahid Bhimji, Paolo Calafiura +26
The FAIR Universe HiggsML Uncertainty Challenge focused on measuring the physical properties of elementary particles with imperfect simulators. Participants were required to comput…