[HTML][HTML] Deep generative models for detector signature simulation: A taxonomic review
B Hashemi, C Krause - Reviews in Physics, 2024 - Elsevier
In modern collider experiments, the quest to explore fundamental interactions between
elementary particles has reached unparalleled levels of precision. Signatures from particle …
elementary particles has reached unparalleled levels of precision. Signatures from particle …
AI for nuclear physics
P Bedaque, A Boehnlein, M Cromaz… - The European Physical …, 2021 - Springer
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Menu Find a journal Publish with us Search Cart 1.Home 2.The European Physical Journal A …
Menu Find a journal Publish with us Search Cart 1.Home 2.The European Physical Journal A …
Uvcgan: Unet vision transformer cycle-consistent gan for unpaired image-to-image translation
Unpaired image-to-image translation has broad applications in art, design, and scientific
simulations. One early breakthrough was CycleGAN that emphasizes one-to-one map**s …
simulations. One early breakthrough was CycleGAN that emphasizes one-to-one map**s …
PC-JeDi: Diffusion for particle cloud generation in high energy physics
In this paper, we present a new method to efficiently generate jets in High Energy Physics
called PC-JeDi. This method utilises score-based diffusion models in conjunction with …
called PC-JeDi. This method utilises score-based diffusion models in conjunction with …
The madnis reloaded
In pursuit of precise and fast theory predictions for the LHC, we present an implementation of
the MadNIS method in the MadGraph event generator. A series of improvements in MadNIS …
the MadNIS method in the MadGraph event generator. A series of improvements in MadNIS …
Invertible networks or partons to detector and back again
For simulations where the forward and the inverse directions have a physics meaning,
invertible neural networks are especially useful. A conditional INN can invert a detector …
invertible neural networks are especially useful. A conditional INN can invert a detector …
How to understand limitations of generative networks
Well-trained classifiers and their complete weight distributions provide us with a well-
motivated and practicable method to test generative networks in particle physics. We …
motivated and practicable method to test generative networks in particle physics. We …
Precision-machine learning for the matrix element method
The matrix element method is the LHC inference method of choice for limited statistics. We
present a dedicated machine learning framework, based on efficient phase-space …
present a dedicated machine learning framework, based on efficient phase-space …
Jet Diffusion versus JetGPT--Modern Networks for the LHC
We introduce two diffusion models and an autoregressive transformer for LHC physics
simulations. Bayesian versions allow us to control the networks and capture training …
simulations. Bayesian versions allow us to control the networks and capture training …
Faster diffusion model with improved quality for particle cloud generation
Building on the success of PC-JeDi we introduce PC-Droid, a substantially improved
diffusion model for the generation of jet particle clouds. By leveraging a new diffusion …
diffusion model for the generation of jet particle clouds. By leveraging a new diffusion …