1 citations · 1 across the 8 of their papers we have counts for
13 papers
EveNet: A Foundation Model for Particle Collision Data Analysis
Ting-Hsiang Hsu, Bai-Hong Zhou, Qibin Liu +8
While deep learning is transforming data analysis in high-energy physics, computational challenges limit its potential. We address these challenges in the context of collider physi…
SEAL - A Symmetry EncourAging Loss for High Energy Physics
Pradyun Hebbar, Thandikire Madula, Vinicius Mikuni +3
Physical symmetries provide a strong inductive bias for constructing functions to analyze data. In particular, this bias may improve robustness, data efficiency, and interpretabili…
Unbinned measurement of thrust in collisions at = 91.2 GeV with ALEPH archived data
The Electron-Positron Alliance, :, Anthony Badea +17
The strong coupling constant () is a fundamental parameter of quantum chromodynamics (QCD), the theory of the strong force. Some of the earliest precise constraints on $α_{S…
Neural Posterior Unfolding
Fernando Torales Acosta, Jay Chan, Krish Desai +4
Differential cross section measurements are the currency of scientific exchange in particle and nuclear physics. A key challenge for these analyses is the correction for detector d…
Analysis-ready Generative Unfolding
Anja Butter, Nathan Huetsch, Vinicius Mikuni +2
Machine Learning (ML)-based unfolding methods have enabled high-dimensional and unbinned differential cross section measurements. While a suite of such methods has been proposed, m…
Analysis note: measurement of thrust in collisions at = 91 GeV with archived ALEPH data
Anthony Badea, Austin Baty, Hannah Bossi +15
A measurement of the thrust distribution in collisions at GeV with archived data from the ALEPH experiment at the Large Electron-Positron Collider is…