112 citations · 1.4k across the 45 of their papers we have counts for
48 papers
Pre-Training for Simulation-Based Science: A Study on Jet Foundation Model Training Objectives
Ibrahim Elsharkawy, Joschka Birk, Vinicius Mikuni +3
Foundation models (FMs) trained on large datasets and fine-tuned on downstream tasks have emerged as a powerful paradigm in AI for science. Industrial FMs are typically trained usi…
Diffusion-Based Point-Cloud Generation of Heavy-Ion Events
Rita Sadek, Vinicius Mikuni, Mateusz Ploskon
Heavy-ion collisions produce final states with thousands to tens of thousands of particles, making their simulation among the most computationally intensive tasks in high-energy nu…
Cross-Domain Transfer with Particle Physics Foundation Models: From Jets to Neutrino Interactions
Gregor Krzmanc, Vinicius Mikuni, Benjamin Nachman +1
Future AI-based studies in particle physics will likely start from a foundation model to accelerate training and enhance sensitivity. As a step toward a general-purpose foundation…
Searching for Anomalies with Foundation Models
Vinicius Mikuni, Benjamin Nachman
Foundation models have the potential to extend the discovery reach for anomaly detection searches. When studying the large OmniLearned foundation model on data from the CMS experim…
Explicit or Implicit? Encoding Physics at the Precision Frontier
Victor Breso-Pla, Kevin Greif, Vinicius Mikuni +4
High-performance machine learning tools in particle physics rest on two complementary directions: encoding symmetries explicitly in the architecture, and implicitly learning the st…
Generation of Imaging Air Cherenkov Telescope images using Diffusion Models
Christian Elflein, Stefan Funk, Jonas Glombitza +3
Substantial amounts of air-shower simulations are needed to derive the instrument response for analyzing Imaging Air Cherenkov Telescope (IACT) data. This process is both computati…
FLEX: A Backbone for Diffusion-Based Modeling of Spatio-temporal Physical Systems
N. Benjamin Erichson, Vinicius Mikuni, Dongwei Lyu +4
We introduce FLEX (FLow EXpert), a backbone architecture for generative modeling of spatio-temporal physical systems using diffusion models. FLEX operates in the residual space rat…
Machine Learning-Assisted Measurement of Lepton-Jet Azimuthal Angular Asymmetries in Deep-Inelastic Scattering at HERA
The H1 collaboration, V. Andreev, M. Arratia +141
In deep-inelastic positron-proton scattering, the lepton-jet azimuthal angular asymmetry is measured using data collected with the H1 detector at HERA. When the average transverse…
Les Houches 2023: Physics at TeV Colliders: Standard Model Working Group Report
J. Andersen, B. Assi, K. Asteriadis +81
This report presents a short summary of the activities of the "Standard Model" working group for the "Physics at TeV Colliders" workshop (Les Houches, France, 12-30 June, 2023).
Measurement of the 1-jettiness event shape observable in deep-inelastic electron-proton scattering at HERA
The H1 collaboration, V. Andreev, M. Arratia +146
The H1 Collaboration reports the first measurement of the 1-jettiness event shape observable in neutral-current deep-inelastic electron-proton scattering (DIS). The observa…
Measurement of groomed event shape observables in deep-inelastic electron-proton scattering at HERA
The H1 collaboration, V. Andreev, M. Arratia +145
The H1 Collaboration at HERA reports the first measurement of groomed event shape observables in deep inelastic electron-proton scattering (DIS) at GeV, using data r…
Observation and differential cross section measurement of neutral current DIS events with an empty hemisphere in the Breit frame
The H1 collaboration, V. Andreev, M. Arratia +146
The Breit frame provides a natural frame to analyze lepton-proton scattering events. In this reference frame, the parton model hard interactions between a quark and an exchanged bo…
Distilling particle knowledge for fast reconstruction at high-energy physics experiments
Aritra Bal, Tristan Brandes, Fabio Iemmi +4
Knowledge distillation is a form of model compression that allows artificial neural networks of different sizes to learn from one another. Its main application is the compactificat…
Full Phase Space Resonant Anomaly Detection
Erik Buhmann, Cedric Ewen, Gregor Kasieczka +3
Physics beyond the Standard Model that is resonant in one or more dimensions has been a longstanding focus of countless searches at colliders and beyond. Recently, many new strateg…
Improving Generative Model-based Unfolding with Schrödinger Bridges
Sascha Diefenbacher, Guan-Horng Liu, Vinicius Mikuni +2
Machine learning-based unfolding has enabled unbinned and high-dimensional differential cross section measurements. Two main approaches have emerged in this research area: one base…
CaloScore v2: Single-shot Calorimeter Shower Simulation with Diffusion Models
Vinicius Mikuni, Benjamin Nachman
Diffusion generative models are promising alternatives for fast surrogate models, producing high-fidelity physics simulations. However, the generation time often requires an expens…
Refining Fast Calorimeter Simulations with a Schrödinger Bridge
Sascha Diefenbacher, Vinicius Mikuni, Benjamin Nachman
Machine learning-based simulations, especially calorimeter simulations, are promising tools for approximating the precision of classical high energy physics simulations with a frac…
Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation
Fernando Torales Acosta, Vinicius Mikuni, Benjamin Nachman +5
Score based generative models are a new class of generative models that have been shown to accurately generate high dimensional calorimeter datasets. Recent advances in generative…
Artificial Intelligence for the Electron Ion Collider (AI4EIC)
C. Allaire, R. Ammendola, E. -C. Aschenauer +92
The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune t…
High-dimensional and Permutation Invariant Anomaly Detection
Vinicius Mikuni, Benjamin Nachman
Methods for anomaly detection of new physics processes are often limited to low-dimensional spaces due to the difficulty of learning high-dimensional probability densities. Particu…
Fast Point Cloud Generation with Diffusion Models in High Energy Physics
Vinicius Mikuni, Benjamin Nachman, Mariel Pettee
Many particle physics datasets like those generated at colliders are described by continuous coordinates (in contrast to grid points like in an image), respect a number of symmetri…
Unbinned Deep Learning Jet Substructure Measurement in High ep collisions at HERA
The H1 collaboration, V. Andreev, M. Arratia +142
The radiation pattern within high energy quark- and gluon-initiated jets (jet substructure) is used extensively as a precision probe of the strong force as well as an environment f…
Optimal transport for a novel event description at hadron colliders
Loukas Gouskos, Fabio Iemmi, Sascha Liechti +3
We propose a novel strategy for disentangling proton collisions at hadron colliders such as the LHC that considerably improves over the current state of the art. Employing a metric…
Anomaly Detection under Coordinate Transformations
Gregor Kasieczka, Radha Mastandrea, Vinicius Mikuni +3
There is a growing need for machine learning-based anomaly detection strategies to broaden the search for Beyond-the-Standard-Model (BSM) physics at the Large Hadron Collider (LHC)…