[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 …
Fast point cloud generation with diffusion models in high energy physics
Many particle physics datasets like those generated at colliders are described by continuous
coordinates (in contrast to grid points like in an image), respect a number of symmetries (like …
coordinates (in contrast to grid points like in an image), respect a number of symmetries (like …
End-to-end latent variational diffusion models for inverse problems in high energy physics
A Shmakov, K Greif, M Fenton… - Advances in …, 2024 - proceedings.neurips.cc
High-energy collisions at the Large Hadron Collider (LHC) provide valuable insights into
open questions in particle physics. However, detector effects must be corrected before …
open questions in particle physics. However, detector effects must be corrected before …
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 …
Evaluating generative models in high energy physics
There has been a recent explosion in research into machine-learning-based generative
modeling to tackle computational challenges for simulations in high energy physics (HEP) …
modeling to tackle computational challenges for simulations in high energy physics (HEP) …
Denoising diffusion models with geometry adaptation for high fidelity calorimeter simulation
O Amram, K Pedro - Physical Review D, 2023 - APS
Simulation is crucial for all aspects of collider data analysis, but the available computing
budget in the High Luminosity LHC era will be severely constrained. Generative machine …
budget in the High Luminosity LHC era will be severely constrained. Generative machine …
The Future of US Particle Physics--The Snowmass 2021 Energy Frontier Report
M Narain, L Reina, A Tricoli, M Begel, A Belloni… - arxiv preprint arxiv …, 2022 - arxiv.org
This report, as part of the 2021 Snowmass Process, summarizes the current status of collider
physics at the Energy Frontier, the broad and exciting future prospects identified for the …
physics at the Energy Frontier, the broad and exciting future prospects identified for the …
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 …
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 …