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The use of Generative Adversarial Networks to characterise new physics
in multi-lepton final states at the LHC

v1v2v3v4 (latest)

The use of Generative Adversarial Networks to characterise new physics in multi-lepton final states at the LHC

31 May 2021

ArXiv (abs)PDF HTML

Papers citing "The use of Generative Adversarial Networks to characterise new physics in multi-lepton final states at the LHC"

3 / 3 papers shown

ForceGen: End-to-end de novo protein generation based on nonlinear
mechanical unfolding responses using a protein language diffusion model

ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a protein language diffusion model

David L. Kaplan

Markus J. Buehler

362

5

0

16 Oct 2023

Optimising simulations for diphoton production at hadron colliders using
amplitude neural networks

Optimising simulations for diphoton production at hadron colliders using amplitude neural networksJournal of High Energy Physics (JHEP), 2021

Joseph Aylett-Bullock

290

34

0

17 Jun 2021

CaloFlow: Fast and Accurate Generation of Calorimeter Showers with
Normalizing Flows

CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows

Claudius Krause

357

90

0

09 Jun 2021

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