2 citations · 2 across the 2 of their papers we have counts for
5 papers · 1 filter
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…
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…
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 architec…
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…