Open Access
EPJ Web Conf.
Volume 251, 2021
25th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2021)
Article Number 03049
Number of page(s) 13
Section Offline Computing
Published online 23 August 2021
  1. R. Jansky, The ATLAS Fast Monte Carlo Production Chain Project (2015), J. Phys. Conf. Ser. 664, 072024 [Google Scholar]
  2. A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman, T. Plehn, GAN plifying Event Samples (2020). 2008.06545 [Google Scholar]
  3. I.J. Goodfellow et al., Generative Adversarial Nets, in Proceedings of the 27th Interna-tional Conference on Neural Information Processing Systems - Volume 2 (Cambridge, MA, USA, 2014), NIPS'14, p. 2672–2680. 1406.2661, [Google Scholar]
  4. D.P. Kingma, M. Welling, Auto-Encoding Variational Bayes (2014). 1312.6114 [Google Scholar]
  5. C. Huang, D. Krueger, A. Lacoste, A.C. Courville, Neural Autoregressive Flows (2018), CoRR. 1804.00779 [Google Scholar]
  6. M. Paganini, L. de Oliveira, B. Nachman, Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters (2018). 1705.02355 [Google Scholar]
  7. L. de Oliveira, M. Paganini, B. Nachman, Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis (2017). 1701.05927 [Google Scholar]
  8. M. Paganini, L. de Oliveira, B. Nachman, CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks (2018). 1712.10321 [Google Scholar]
  9. M. Erdmann, L. Geiger, J. Glombitza, D. Schmidt, Generating and refining particle detector simulations using the Wasserstein distance in adversarial networks (2018). 1802.03325 [Google Scholar]
  10. M. Erdmann, J. Glombitza, T. Quast, Precise simulation of electromagnetic calorimeter showers using a Wasserstein Generative Adversarial Network (2019). 1807.01954 [Google Scholar]
  11. ATLAS Collaboration, Tech. Rep. ATL-SOFT-PUB-2018-001, CERN, Geneva (2018), [Google Scholar]
  12. ATLAS Collaboration, Tech. Rep. ATL-SOFT-SIM-2019-007, CERN (2019), [Google Scholar]
  13. A. Ghosh (ATLAS Collaboration), Tech. Rep. ATL-SOFT-PROC-2019-007, CERN, Geneva (2019), [Google Scholar]
  14. D. Belayneh et al., Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics (2019). 1912.06794 [Google Scholar]
  15. E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol, K. Krüger, Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed (2021). 2005.05334 [Google Scholar]
  16. V. Cédric, Optimal Transport: Old and New (Springer, Berlin, 2009) [Google Scholar]
  17. S. Voloshynovskiy, M. Kondah, S. Rezaeifar, O. Taran, T. Holotyak, D.J. Rezende, Information bottleneck through variational glasses (2019). 1912.00830 [Google Scholar]
  18. E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol, K. Krüger, Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network (2021), submitted to vCHEP 2021. 2102.12491 [Google Scholar]
  19. H. Abramowicz et al. (ILD Concept Group), International Large Detector: Interim DesignReport (2020). 2003.01116 [Google Scholar]
  20. M. Frank, F. Gaede, C. Grefe, P. Mato, DD4hep: A Detector Description Toolkit for High Energy Physics Experiments (2014), J. Phys. Conf. Ser. 513, 022010 [Google Scholar]
  21. A. Paszke et al., PyTorch: An Imperative Style, High-Performance Deep Learning Library (2019), Advances in Neural Information Processing Systems 32 pp. 8024–8035, [Google Scholar]
  22. P. Baldi, K. Cranmer, T. Faucett, P. Sadowski, D. Whiteson, Parameterized Machine Learning for High-Energy Physics (2016), Eur. Phys. J. C 76, 235. 1601.07913 [CrossRef] [EDP Sciences] [Google Scholar]
  23. I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, A. Courville, Improved Training of Wasserstein GANs, in Advances in Neural Information Processing Systems 30 (2017), pp. 5767–5777. 1704.00028, [Google Scholar]
  24. A. Gretton, K.M. Borgwardt, M.J. Rasch, B. Schölkopf, A.J. Smola, A Kernel Method for the Two-Sample Problem (2008), CoRR. 0805.2368 [Google Scholar]
  25. E. Parzen, On Estimation of a Probability Density Function and Mode (1962), The Annals of Mathematical Statistics 33, pp. 1065, [Google Scholar]
  26. S. Otten, S. Caron, W. de Swart, M. van Beekveld, L. Hendriks, C. van Leeuwen, D. Podareanu, R.R. de Austri, R. Verheyen, Event Generation and Statistical Sampling for Physics with Deep Generative Models and a Density Information Buffer (2019). 1901.00875 [Google Scholar]
  27. Y. Wu, M. Rosca, T. Lillicrap, Deep Compressed Sensing (2019). 1905.06723 [Google Scholar]
  28. Y. Wu, J. Donahue, D. Balduzzi, K. Simonyan, T. Lillicrap, LOGAN: Latent Optimisation for Generative Adversarial Networks (2020). 1912.00953 [Google Scholar]
  29. S. Amari, Natural Gradient Works Efficiently in Learning (1998), Neural Computation 10, 251 [Google Scholar]
  30. T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, X. Chen, Improved Techniques for Training GANs (2016). 1909.10578 [Google Scholar]
  31. T. Karras, T. Aila, S. Laine, J. Lehtinen, Progressive Growing of GANs for Improved Quality, Stability, and Variation (2017). 1710.10196 [Google Scholar]
  32. M. Thomson, Particle flow calorimetry and the PandoraPFA algorithm (2009), Nuclear Instruments and Methods in Physics Research Section A: Accel-erators, Spectrometers, Detectors and Associated Equipment 611, 25–40,].nima.2009.09.009 [Google Scholar]
  33. R. Kansal, J. Duarte, B. Orzari, T. Tomei, M. Pierini, M. Touranakou, J.R. Vlimant, D. Gunopulos, Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics (2021). 2012.00173 [Google Scholar]
  34. J.N. Howard, S. Mandt, D. Whiteson, Y. Yang, Foundations of a Fast, Data-Driven, Machine-Learned Simulator (2021). 2101.08944 [Google Scholar]

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