8 citations · 32 across the 16 of their papers we have counts for
5 papers · 1 filter
Scaling Collider Event Generation with Residual-Quantized Tokens
Dan Godi, Dmitrii Kobylianskii, Eilam Gross
Full detector simulation and reconstruction of collider events are projected to become major bottlenecks at the High-Luminosity Large Hadron Collider, motivating the development of…
Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Tasks
Nilotpal Kakati, Daniel Murnane, Baran Hashemi +5
Reconstruction and simulation at a collider experiment are long chains of specialised algorithms, each tuned to a single step. We explore how one model can serve many of those step…
Differentiable Parametric Simulation and Reconstruction Models in Parnassus
Abdelrahman Elabd, Eilam Gross, Dmitrii Kobylianskii +2
Parnassus is a framework for fast detector simulation and reconstruction, directly mapping truth-level particles onto reconstructed objects. Such models can be built from deep gene…
Passage of particles through matter and the effective straggling-function: High-fidelity accelerated simulation via Physics-Informed Machine Learning
Oleksandr Borysov, Rotem Dover, Eilam Gross +2
High-fidelity simulation of particle-matter interactions provides the essential theoretical reference for diverse physics disciplines, yet generating synthetic datasets at the scal…
An AI-based Detector Simulation and Reconstruction Model for the ALEPH Experiment at LEP
Ya-Feng Lo, Dmitrii Kobylianskii, Benjamin Nachman +1
We present the application of Parnassus, a generative model for full detector simulation and reconstruction, to the ALEPH detector at the Large Electron-Positron Collider (LEP). Tr…