5 citations · 7 across the 5 of their papers we have counts for
4 papers · 1 filter
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…
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…
Point cloud-based diffusion models for the Electron-Ion Collider
Jack Y. Araz, Vinicius Mikuni, Felix Ringer +3
At high-energy collider experiments, generative models can be used for a wide range of tasks, including fast detector simulations, unfolding, searches of physics beyond the Standar…