1 citations · 1 across the 2 of their papers we have counts for
5 papers
TIGER: A Topology-Agnostic, Hierarchical Graph Network for Event Reconstruction
Nathalie Soybelman, Francesco A. Di Bello, Nilotpal Kakati +1
Event reconstruction at the LHC, the task of assigning observed physics objects to their true origins, is a central challenge for precision measurements and searches. Many existing…
GLOW: A Unified Particle Flow Transformer
Dmitrii Kobylianskii, Samuel Van Stroud, Kwok Yiu Wong +5
We present GLOW, a transformer-based particle flow model that combines incidence matrix supervision from HGPflow with a MaskFormer architecture. Evaluated on CLIC detector simulati…
Conditional Deep Generative Models for Simultaneous Simulation and Reconstruction of Entire Events
Etienne Dreyer, Eilam Gross, Dmitrii Kobylianskii +2
We extend the Particle-flow Neural Assisted Simulations (Parnassus) framework of fast simulation and reconstruction to entire collider events. In particular, we use two generative…
Self-Supervised Learning Strategies for Jet Physics
Patrick Rieck, Kyle Cranmer, Etienne Dreyer +5
We extend the re-simulation-based self-supervised learning approach to learning representations of hadronic jets in colliders by exploiting the Markov property of the standard simu…
Point Cloud Deep Learning Methods for Particle Shower Reconstruction in the DHCAL
Maryna Borysova, Shikma Bressler, Eilam Gross +2
Precision measurement of hadronic final states presents complex experimental challenges. The study explores the concept of a gaseous Digital Hadronic Calorimeter (DHCAL) and discus…