23 papers
Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthrough
Gaia Grosso, Vinicius Mikuni, Lukas Heinrich
Machine learning (ML) has become integral to fundamental physics, accelerating statistical workflows from data acquisition through inference and hypothesis testing. As ML systems g…
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…
OmniMol: Transferring Particle Physics Knowledge to Molecular Dynamics with Point-Edge Transformers
Ibrahim Elsharkawy, Vinicius Mikuni, Wahid Bhimji +1
We present OmniMol, a state-of-the-art all-to-all transformer-based small molecule machine-learned interatomic potential (MLIP). OmniMol is built by adapting Omnilearned, a foundat…
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…
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…
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…