Fetching the paper…
Reading the bibliography…
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.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting
1929
Earlier work this paper cites.
2005
Earlier work this paper cites.
J. Allison et al., Geant4 developments and applications
2006
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, Denoising diffusion probabilistic models · 2006
Earlier work this paper cites.
2006
Earlier work this paper cites.
2009
Earlier work this paper cites.
http://cds.cern.ch/record/1300517
ATLAS · 2010
Earlier work this paper cites.
S. Abdullin, P. Azzi, F. Beaudette, P. Janot, and A. Perrotta, The fast simulation of the CMS detector at LHC
2011
Earlier work this paper cites.
W. Lukas, Fast Simulation for ATLAS: Atlfast-II and ISF
2012
Earlier work this paper cites.
A. Giammanco, The Fast Simulation of the CMS Experiment
2014
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, U-net: Convolutional networks for biomedical image segmentation · 2015
Earlier work this paper cites.
J. Allison et al., Recent developments in Geant4
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition · 2016
Earlier work this paper cites.
https://cds.cern.ch/record/2293646
CMS · 2017
Cited alongside, same era.
2018
Cited alongside, same era.
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, The unreasonable effectiveness of deep features as a perceptual metric · 2018
Cited alongside, same era.
2019
Cited alongside, same era.
https://cds.cern.ch/record/2746032
ATLAS · 2020
Cited alongside, same era.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, High-resolution image synthesis with latent diffusion models · 2022
Later among the works it cites.
https://calochallenge.github.io/homepage/
M. F. Giannelli, G. Kasieczka, C. Krause, B. Nachman, D. Salamani, D. Shih, and A. Zaborowska, Fast Calorimeter Simulation Challenge · 2022
Later among the works it cites.
T. Salimans and J. Ho, Progressive distillation for fast sampling of diffusion models · 2022
Later among the works it cites.
C. Krause and D. Shih, Fast and accurate simulations of calorimeter showers with normalizing flows
2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
2021
Cited alongside, same era.
https://doi.org/10.7483/OPENDATA.ATLAS.UXKX.TXBN
ATLAS · 2021
Cited alongside, same era.
A. Q. Nichol and P. Dhariwal, Improved denoising diffusion probabilistic models · 2021
Cited alongside, same era.
2022
Cited alongside, same era.
V. Mikuni and B. Nachman, Score-based generative models for calorimeter shower simulation
2022
Cited alongside, same era.
A. Adelmann et al., New directions for surrogate models and differentiable programming for High Energy Physics detector simulation · 2022
Cited alongside, same era.
2023
Closest in time.
S. Badger et al., Machine learning and LHC event generation
2023
Closest in time.
M. Barbetti, Lamarr: LHCb ultra-fast simulation based on machine learning models deployed within Gauss · 2023
Closest in time.
2023
Closest in time.
https://doi.org/10.5281/zenodo.7778868
R. Kansal, J. Duarte, C. Pareja, L. Action, Z. Hao, and mova, jet-net/JetNet: v0.2.3.post3 · 2023
Closest in time.
CaloChallenge Workshop, https://agenda.infn.it/event/34036/contributions/200888/attachments/106010/149192/CaloChallenge.Summary.C.Krause.pdf
C. Krause, The Fast Calorimeter Challenge 2022: Results and The Road Ahead · 2023
Closest in time.
2023
Closest in time.
S. Bein, P. Connor, K. Pedro, P. Schleper, and M. Wolf, Refining fast simulation using machine learning · 2023
Closest in time.