22 citations · 88 across the 25 of their papers we have counts for
13 papers · 1 filter
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…
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…
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…
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…
Generative Unfolding with Distribution Mapping
Anja Butter, Sascha Diefenbacher, Nathan Huetsch +4
Machine learning enables unbinned, highly-differential cross section measurements. A recent idea uses generative models to morph a starting simulation into the unfolded data. We sh…
The Fundamental Limit of Jet Tagging
Joep Geuskens, Nishank Gite, Michael Krämer +4
Identifying the origin of high-energy hadronic jets ('jet tagging') has been a critical benchmark problem for machine learning in particle physics. Jets are ubiquitous at colliders…
Multidimensional Deconvolution with Profiling
Huanbiao Zhu, Krish Desai, Mikael Kuusela +3
In many experimental contexts, it is necessary to statistically remove the impact of instrumental effects in order to physically interpret measurements. This task has been extensiv…
The Landscape of Unfolding with Machine Learning
Nathan Huetsch, Javier Mariño Villadamigo, Alexander Shmakov +9
Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new…
Solving Key Challenges in Collider Physics with Foundation Models
Vinicius Mikuni, Benjamin Nachman
Foundation Models are neural networks that are capable of simultaneously solving many problems. Large Language Foundation Models like ChatGPT have revolutionized many aspects of da…