[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 …
Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
B Hashemi, C Krause - arXiv preprint arXiv:2312.09597, 2023 - arxiv.org
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 …
Modern machine learning for LHC physicists
Modern machine learning is transforming particle physics fast, bullying its way into our
numerical tool box. For young researchers it is crucial to stay on top of this development …
numerical tool box. For young researchers it is crucial to stay on top of this development …
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 …
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 …
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 …
An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative training
M Backes, A Butter, M Dunford, B Malaescu - SciPost Physics Core, 2024 - scipost.org
The unfolding of detector effects is crucial for the comparison of data to theory predictions.
While traditional methods are limited to representing the data in a low number of …
While traditional methods are limited to representing the data in a low number of …
Returning CP-observables to the frames they belong
J Ackerschott, RK Barman, D Gonçalves, T Heimel… - SciPost Physics, 2024 - scipost.org
Optimal kinematic observables are often defined in specific frames and then approximated
at the reconstruction level. We show how multi-dimensional unfolding methods allow us to …
at the reconstruction level. We show how multi-dimensional unfolding methods allow us to …
CaloClouds II: ultra-fast geometry-independent highly-granular calorimeter simulation
E Buhmann, F Gaede, G Kasieczka… - Journal of …, 2024 - iopscience.iop.org
Fast simulation of the energy depositions in high-granular detectors is needed for future
collider experiments at ever-increasing luminosities. Generative machine learning (ML) …
collider experiments at ever-increasing luminosities. Generative machine learning (ML) …
Two invertible networks for the matrix element method
The matrix element method is widely considered the ultimate LHC inference tool for small
event numbers. We show how a combination of two conditional generative neural networks …
event numbers. We show how a combination of two conditional generative neural networks …