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