22 citations · 89 across the 28 of their papers we have counts for
29 papers
The logical set of Gaussian states under the stabilizer subsystem decomposition
Santiago Zamora, Kaustav Chatterjee, Ulrik Lund Andersen +4
Universal quantum computation requires non-Gaussianity in continuous-variable systems and non-stabilizerness in discrete-variable systems. Yet, mapping a Gaussian state into the Go…
Future of Artificial Intelligence for Science in Japan 2024 Community Report
Yoshitaka Itow, Jia Liu, Hirokazu Maesaka +16
This white paper summarizes scientific challenges and AI/ML research opportunities identified through the FAIRS Japan 2024 unconference process. The discussion focuses on three maj…
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…
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…
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…
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…
Analysis note: measurement of thrust in collisions at = 91 GeV with archived ALEPH data
Anthony Badea, Austin Baty, Hannah Bossi +15
A measurement of the thrust distribution in collisions at GeV with archived data from the ALEPH experiment at the Large Electron-Positron Collider is…
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…
Generator Based Inference (GBI)
Chi Lung Cheng, Ranit Das, Runze Li +5
Statistical inference in physics is often based on samples from a generator (sometimes referred to as a ``forward model") that emulate experimental data and depend on parameters of…
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…
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…
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…
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
Claudius Krause, Michele Faucci Giannelli, Gregor Kasieczka +66
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of i…