112 citations · 1.4k across the 45 of their papers we have counts for
6 papers · 1 filter
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…