2.6k citations · 10.3k across the 85 of their papers we have counts for
91 papers
Uncertainty in Physics and AI: Taxonomy, Quantification, and Validation
Manuel Haußmann, Ramon Winterhalder, Maria Ubiali
Reliable uncertainty quantification is essential for the use of machine learning in physics, where scientific discoveries depend on validated probabilistic statements. We provide a…
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…
Parton distribution functions and theory parameters: an NNPDF perspective
Richard D. Ball, Tommaso Giani, Felix Hekhorn +5
Parton Distribution Functions (PDFs) are a key ingredient in theoretical predictions for Large Hadron Collider (LHC) observables and play a central role in the extraction of precis…
Modern Machine Learning and Particle Physics Phenomenology at the LHC
Maria Ubiali
Modern machine learning is driving a paradigm shift in particle physics phenomenology at the Large Hadron Collider. This short review examines the transformative role of machine le…
Tailored PDFs for New Physics searches
Ella Cole, Mark N. Costantini, Elie Hammou +4
Given the non-negligible interplay between parton distribution functions (PDFs) at large x and potential New Physics (NP) effects in the high-energy tails of hadron collider observ…
Colibri: A new tool for fast-flying PDF fits
Mark N. Costantini, Luca Mantani, James M. Moore +2
We present Colibri, an open-source Python code that provides a general and flexible tool for PDF fits. The code is built so that users can implement their own PDF model, and use th…