Timezone: »
Set-Conditional Set Generation for Particle Physics
Sanmay Ganguly · Lukas Heinrich · Nilotpal Kakati · Nathalie Soybelman
The simulation of particle physics data is a fundamental but computationally intensive ingredient for physics analysis at the Large Hadron Collider, where observational set-valued data is generated conditional on a set of incoming particles. To accelerate this task, we present an novel generative model based on graph neural network and slot-attention components, which exceeds the performance of pre-existing baselines.
Author Information
Sanmay Ganguly (University of Tokyo)
Lukas Heinrich (New York University)
Nilotpal Kakati (Weizmann Institute of Science)
Nathalie Soybelman (Weizmann Institute of Science)
More from the Same Authors
-
2022 : HGPflow: Particle reconstruction as hyperedge prediction »
Etienne Dreyer · Nilotpal Kakati · Francesco Armando Di Bello -
2019 Poster: Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model »
Atilim Gunes Baydin · Lei Shao · Wahid Bhimji · Lukas Heinrich · Saeid Naderiparizi · Andreas Munk · Jialin Liu · Bradley Gram-Hansen · Gilles Louppe · Lawrence Meadows · Philip Torr · Victor Lee · Kyle Cranmer · Mr. Prabhat · Frank Wood