22 citations · 88 across the 25 of their papers we have counts for
13 papers · 1 filter
OmniCosmos: Transferring Particle Physics Knowledge Across the Cosmos
Vinicius Mikuni, Ibrahim Elsharkawy, Benjamin Nachman
Foundation models build an effective representations of data that can be deployed on diverse downstream tasks. Previous research developed the OmniLearned foundation model for coll…
Machine Learning-based Unfolding for Cross Section Measurements in the Presence of Nuisance Parameters
Huanbiao Zhu, Krish Desai, Mikael Kuusela +3
Statistically correcting measured cross sections for detector effects is an important step across many applications. In particle physics, this inverse problem is known as unfolding…
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…
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…
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…
Machine Learning-Assisted Unfolding for Neutrino Cross-section Measurements with the OmniFold Technique
Roger G. Huang, Andrew Cudd, Masaki Kawaue +4
The choice of unfolding method for a cross-section measurement is tightly coupled to the model dependence of the efficiency correction and the overall impact of cross-section model…
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…
Conditional Deep Generative Models for Simultaneous Simulation and Reconstruction of Entire Events
Etienne Dreyer, Eilam Gross, Dmitrii Kobylianskii +2
We extend the Particle-flow Neural Assisted Simulations (Parnassus) framework of fast simulation and reconstruction to entire collider events. In particular, we use two generative…
Tools for Unbinned Unfolding
Ryan Milton, Vinicius Mikuni, Trevin Lee +3
Machine learning has enabled differential cross section measurements that are not discretized. Going beyond the traditional histogram-based paradigm, these unbinned unfolding metho…
A Method to Simultaneously Facilitate All Jet Physics Tasks
Vinicius Mikuni, Benjamin Nachman
Machine learning has become an essential tool in jet physics. Due to their complex, high-dimensional nature, jets can be explored holistically by neural networks in ways that are n…