112 citations · 1.4k across the 45 of their papers we have counts for
15 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…
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…
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).
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…
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…
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)…
Score-based Generative Models for Calorimeter Shower Simulation
Vinicius Mikuni, Benjamin Nachman
Score-based generative models are a new class of generative algorithms that have been shown to produce realistic images even in high dimensional spaces, currently surpassing other…
New directions for surrogate models and differentiable programming for High Energy Physics detector simulation
Andreas Adelmann, Walter Hopkins, Evangelos Kourlitis +8
The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources. To overcome this challenge,…
Presenting Unbinned Differential Cross Section Results
Miguel Arratia, Anja Butter, Mario Campanelli +10
Machine learning tools have empowered a qualitatively new way to perform differential cross section measurements whereby the data are unbinned, possibly in many dimensions. Unbinne…
The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
Gregor Kasieczka, Benjamin Nachman, David Shih +44
A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. I…