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With the vast data-collecting capabilities of current and future high-energy collider experiments, there is an increasing demand for computationally efficient simulations.
Geant4—a simulation toolkit ,
S. Agostinelli, J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce, M. Asai, D. Axen, S. Banerjee, G. Barrand, F. Behner, L. Bellagamba et al. , · 2003
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The anti- k t k_{t} jet clustering algorithm ,
M. Cacciari, G. P. Salam and G. Soyez, · 2008
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Brownian distance covariance ,
G. J. Székely and M. L. Rizzo, · 2009
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Adam: A Method for Stochastic Optimization ,
D. P. Kingma and J. Ba, · 2014
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Deep Residual Learning for Image Recognition ,
K. He, X. Zhang, S. Ren and J. Sun, · 2015
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Least Squares Generative Adversarial Networks ,
X. Mao, Q. Li, H. Xie, R. Y. K. Lau, Z. Wang and S. P. Smolley, · 2016
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Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks (2016), 1602.07868
T. Salimans and D. P. Kingma, · 2016
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Neural Message Passing for Quantum Chemistry ,
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals and G. E. Dahl, · 2017
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Deep Sets ,
M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. Salakhutdinov and A. Smola, · 2017
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CaloGAN: Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks ,
M. Paganini, L. de Oliveira and B. Nachman, · 2018
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Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks ,
J. Lee, Y. Lee, J. Kim, A. R. Kosiorek, S. Choi and Y. W. Teh, · 2018
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Point Cloud GAN ,
C.-L. Li, M. Zaheer, Y. Zhang, B. Poczos and R. Salakhutdinov, · 2018
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Relational inductive biases, deep learning, and graph networks ,
P. W. Battaglia, J. B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, C. Gulcehre, F. Song et al. , · 2018
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Energy flow polynomials: a complete linear basis for jet substructure ,
P. T. Komiske, E. M. Metodiev and J. Thaler, · 2018
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3D convolutional GAN for fast simulation ,
S. Vallecorsa, F. Carminati and G. Khattak, · 2019
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Precise Simulation of Electromagnetic Calorimeter Showers Using a Wasserstein Generative Adversarial Network ,
M. Erdmann, J. Glombitza and T. Quast, · 2019
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Energy Flow Networks: Deep Sets for Particle Jets ,
P. T. Komiske, E. M. Metodiev and J. Thaler, · 2019
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PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows ,
G. Yang, X. Huang, Z. Hao, M.-Y. Liu, S. Belongie and B. Hariharan, · 2019
Cited alongside, same era.
PyTorch: An Imperative Style, High-Performance Deep Learning Library ,
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf et al. , · 2019
Cited alongside, same era.
Learning representations of irregular particle-detector geometry with distance-weighted graph networks ,
S. R. Qasim, J. Kieseler, Y. Iiyama and M. Pierini, · 2019
Cited alongside, same era.
Conditional Set Generation with Transformers ,
A. R. Kosiorek, H. Kim and D. J. Rezende, · 2020
Cited alongside, same era.
DCTRGAN: improving the precision of generative models with reweighting ,
S. Diefenbacher, E. Eren, G. Kasieczka, A. Korol, B. Nachman and D. Shih, · 2020
Cited alongside, same era.
Calomplification — the power of generative calorimeter models ,
S. Bieringer, A. Butter, S. Diefenbacher, E. Eren, F. Gaede, D. Hundhausen, G. Kasieczka, B. Nachman, T. Plehn and M. Trabs, · 2022
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Radio Galaxy Classification with wGAN-Supported Augmentation (2022),
J. Kummer, L. Rustige, F. Griese, K. Borras, M. Brüggen, P. L. S. Connor, F. Gaede, G. Kasieczka and P. Schleper, · 2022
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Deep generative models for fast photon shower simulation in ATLAS (2022), 2210.06204
ATLAS Collaboration, · 2022
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AtlFast3: The Next Generation of Fast Simulation in ATLAS ,
ATLAS collaboration, · 2022
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CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds (2022), 2211.15380
J. C. Cresswell, B. L. Ross, G. Loaiza-Ganem, H. Reyes-Gonzalez, M. Letizia and A. L. Caterini, · 2022
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Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon and B. Poole, · 2020
Cited alongside, same era.
GANplifying event samples ,
A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman and T. Plehn, · 2021
Cited alongside, same era.
Fast Simulation of a High Granularity Calorimeter by Generative Adversarial Networks (2021), 2109.07388
G. R. Khattak, S. Vallecorsa, F. Carminati and G. M. Khan, · 2021
Cited alongside, same era.
Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed ,
E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol and K. Krüger, · 2021
Cited alongside, same era.
Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network ,
E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol and K. Krüger, · 2021
Cited alongside, same era.
Hadrons, Better, Faster, Stronger ,
E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, D. Hundhausen, G. Kasieczka, W. Korcari, K. Krüger, P. McKeown and L. Rustige, · 2021
Cited alongside, same era.
CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows ,
C. Krause and D. Shih, · 2021
Cited alongside, same era.
Score-based generative models for calorimeter shower simulation ,
V. Mikuni and B. Nachman, · 2022
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Uncertainties associated with GAN-generated datasets in high energy physics ,
K. Matchev, A. Roman and P. Shyamsundar, · 2022
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Particle-based fast jet simulation at the LHC with variational autoencoders ,
M. Touranakou, N. Chernyavskaya, J. Duarte, D. Gunopulos, R. Kansal, B. Orzari, M. Pierini, T. Tomei and J.-R. Vlimant, · 2022
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JetFlow: Generating Jets with Conditioned and Mass Constrained Normalising Flows ,
B. Käch, D. Krücker, I. Melzer-Pellmann, M. Scham, S. Schnake and A. Verney-Provatas, · 2022
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Conditional Generative Modelling of Reconstructed Particles at Collider Experiments ,
F. A. Di Bello, E. Dreyer, S. Ganguly, E. Gross, L. Heinrich, M. Kado, N. Kakati, J. Shlomi and N. Soybelman, · 2022
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On the Evaluation of Generative Models in High Energy Physics ,
R. Kansal, A. Li, J. Duarte, N. Chernyavskaya, M. Pierini, B. Orzari and T. Tomei, · 2022
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Point-E: A System for Generating 3D Point Clouds from Complex Prompts ,
A. Nichol, H. Jun, P. Dhariwal, P. Mishkin and M. Chen, · 2022
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Classifying anomalies through outer density estimation ,
A. Hallin, J. Isaacson, G. Kasieczka, C. Krause, B. Nachman, T. Quadfasel, M. Schlaffer, D. Shih and M. Sommerhalder, · 2022
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JetNet (Version 2) [Data set] ,
R. Kansal, J. Duarte, H. Su, B. Orzari, T. Tomei, M. Pierini, M. Touranakou, J.-R. Vlimant and D. Gunopulos, · 2022
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JetNet150 (2.0.0) [Data set] ,
R. Kansal, J. Duarte, H. Su, B. Orzari, T. Tomei, M. Pierini, M. Touranakou, J.-R. Vlimant and D. Gunopulos, · 2022
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A comprehensive guide to the physics and usage of PYTHIA 8.3 (2022),
C. Bierlich et al. , · 2022
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