13 papers · 1 filter
OmniLearned: A Foundation Model Framework for All Tasks Involving Jet Physics
Wahid Bhimji, Chris Harris, Vinicius Mikuni +1
Foundation models use large datasets to build an effective representation of data that can be deployed on diverse downstream tasks. Previous research developed the OmniLearn founda…
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…
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…
A Practical Guide to Unbinned Unfolding
Florencia Canelli, Kyle Cormier, Andrew Cudd +12
Unfolding, in the context of high-energy particle physics, refers to the process of removing detector distortions in experimental data. The resulting unfolded measurements are stra…
Stabilizing Neural Likelihood Ratio Estimation
Fernando Torales Acosta, Tanvi Wamorkar, Vinicius Mikuni +1
Likelihood ratios are used for a variety of applications in particle physics data analysis, including parameter estimation, unfolding, and anomaly detection. When the data are high…