Details

Autor(en) / Beteiligte

• Soybelman, Nathalie
• Kakati, Nilotpal
• Heinrich, Lukas
• Di Bello, Francesco Armando
• Dreyer, Etienne
• Ganguly, Sanmay
• Gross, Eilam
• Kado, Marumi
• Shlomi, Jonathan

Titel

Set-conditional set generation for particle physics

Ist Teil von

• Machine learning: science and technology, 2023-12, Vol.4 (4), p.45036

Ort / Verlag

Bristol: IOP Publishing

Erscheinungsjahr

2023

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• Abstract 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 a novel generative model based on a graph neural network and slot-attention components, which exceeds the performance of pre-existing baselines.