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First-principle simulations are at the heart of the high-energy physics research program.
S. Otten, S. Caron, W. de Swart, M. van Beekveld, L. Hendriks, C. van Leeuwen, D. Podareanu, R. Ruiz de Austri and R. Verheyen, · 1901
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S. Otten, S. Caron, W. de Swart, M. van Beekveld, L. Hendriks, C. van Leeuwen, D. Podareanu, R. Ruiz de Austri and R. Verheyen, · 1901
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LHC analysis-specific datasets with Generative Adversarial Networks (2019),
B. Hashemi, N. Amin, K. Datta, D. Olivito and M. Pierini, · 1901
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R. Di Sipio, M. Faucci Giannelli, S. Ketabchi Haghighat and S. Palazzo, · 1903
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R. Di Sipio, M. Faucci Giannelli, S. Ketabchi Haghighat and S. Palazzo, · 1903
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Deep-Learning Jets with Uncertainties and More ,
S. Bollweg, M. Haußmann, G. Kasieczka, M. Luchmann, T. Plehn and J. Thompson, · 1904
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Event Generation with Sherpa 2.2 ,
E. Bothmann et al. , · 1905
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Binary JUNIPR: an interpretable probabilistic model for discrimination ,
A. Andreassen, I. Feige, C. Frye and M. D. Schwartz, · 1906
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A. Butter, T. Plehn and R. Winterhalder, · 1907
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Towards a new generation of parton densities with deep learning models ,
S. Carrazza and J. Cruz-Martinez, · 1907
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Guided image generation with conditional invertible neural networks (2019),
L. Ardizzone, C. Lüth, J. Kruse, C. Rother and U. Köthe, · 1907
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From the bottom to the top—reconstruction of t t ¯ t\bar{t} events with deep learning ,
J. Erdmann, T. Kallage, K. Kröninger and O. Nackenhorst, · 1907
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MadMiner: Machine learning-based inference for particle physics ,
J. Brehmer, F. Kling, I. Espejo and K. Cranmer, · 1907
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B. Nachman, · 1909
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OmniFold: A Method to Simultaneously Unfold All Observables ,
A. Andreassen, P. T. Komiske, E. M. Metodiev, B. Nachman and J. Thaler, · 1911
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The DNNLikelihood: enhancing likelihood distribution with Deep Learning ,
A. Coccaro, M. Pierini, L. Silvestrini and R. Torre, · 1911
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(Machine) Learning Amplitudes for Faster Event Generation (2019),
F. Bishara and M. Montull, · 1912
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How to GAN Event Subtraction ,
A. Butter, T. Plehn and R. Winterhalder, · 1912
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Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics ,
D. Belayneh et al. , · 1912
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J. Arjona Martínez, T. Q. Nguyen, M. Pierini, M. Spiropulu and J.-R. Vlimant, · 1912
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How to GAN away Detector Effects ,
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn and R. Winterhalder, · 1912
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Exploring phase space with Neural Importance Sampling ,
E. Bothmann, T. Janßen, M. Knobbe, T. Schmale and S. Schumann, · 2001
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i-flow: High-dimensional Integration and Sampling with Normalizing Flows ,
C. Gao, J. Isaacson and C. Krause, · 2001
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Event Generation with Normalizing Flows ,
C. Gao, S. Höche, J. Isaacson, C. Krause and H. Schulz, · 2001
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Y. Alanazi, N. Sato, T. Liu, W. Melnitchouk, M. P. Kuchera, E. Pritchard, M. Robertson, R. Strauss, L. Velasco and Y. Li, · 2001
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Anomaly Detection with Density Estimation ,
B. Nachman and D. Shih, · 2001
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Using neural networks for efficient evaluation of high multiplicity scattering amplitudes ,
S. Badger and J. Bullock, · 2002
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Neural network parametrization of deep inelastic structure functions ,
S. Forte, L. Garrido, J. I. Latorre and A. Piccione, · 2002
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Connecting Dualities and Machine Learning ,
P. Betzler and S. Krippendorf, · 2002
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Per-Object Systematics using Deep-Learned Calibration ,
G. Kasieczka, M. Luchmann, F. Otterpohl and T. Plehn, · 2003
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Towards a Computer Vision Particle Flow ,
F. A. Di Bello, S. Ganguly, E. Gross, M. Kado, M. Pitt, L. Santi and J. Shlomi, · 2003
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Reinterpretation of LHC Results for New Physics: Status and Recommendations after Run 2 ,
W. Abdallah et al. , · 2003
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Detecting Symmetries with Neural Networks (2020),
S. Krippendorf and M. Syvaeri, · 2003
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Exhaustive neural importance sampling applied to Monte Carlo event generation ,
S. Pina-Otey, V. Gaitan, F. Sánchez and T. Lux, · 2005
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Unbiased determination of the proton structure function F(2)**p with faithful uncertainty estimation ,
L. Del Debbio, S. Forte, J. I. Latorre, A. Piccione and J. Rojo, · 2005
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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, · 2005
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nNNPDF2.0: quark flavor separation in nuclei from LHC data ,
R. Abdul Khalek, J. J. Ethier, J. Rojo and G. van Weelden, · 2006
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Invertible Networks or Partons to Detector and Back Again ,
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, A. Rousselot, R. Winterhalder, L. Ardizzone and U. Köthe, · 2006
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Generative Networks for LHC events (2020),
A. Butter and T. Plehn, · 2008
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A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman and T. Plehn, · 2008
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Improved Neural Network Monte Carlo Simulation ,
I.-K. Chen, M. D. Klimek and M. Perelstein, · 2009
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Variational Autoencoders for Jet Simulation (2020),
K. Dohi, · 2009
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DCTRGAN: Improving the Precision of Generative Models with Reweighting ,
S. Diefenbacher, E. Eren, G. Kasieczka, A. Korol, B. Nachman and D. Shih, · 2009
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MCNNTUNES: Tuning Shower Monte Carlo generators with machine learning ,
M. Lazzarin, S. Alioli and S. Carrazza, · 2010
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A first unbiased global NLO determination of parton distributions and their uncertainties ,
R. D. Ball, L. Del Debbio, S. Forte, A. Guffanti, J. I. Latorre, J. Rojo and M. Ubiali, · 2010
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WHIZARD: Simulating Multi-Particle Processes at LHC and ILC ,
W. Kilian, T. Ohl and J. Reuter, · 2011
Cited alongside, same era.
Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows ,
B. Stienen and R. Verheyen, · 2011
Cited alongside, same era.
Phase space sampling and inference from weighted events with autoregressive flows ,
B. Stienen and R. Verheyen, · 2011
Cited alongside, same era.
M. Vandegar, M. Kagan, A. Wehenkel and G. Louppe, · 2011
Cited alongside, same era.
A factorisation-aware Matrix element emulator ,
D. Maître and H. Truong, · 2021
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Sparse Data Generation for Particle-Based Simulation of Hadronic Jets in the LHC ,
B. Orzari, T. Tomei, M. Pierini, M. Touranakou, J. Duarte, R. Kansal, J.-R. Vlimant and D. Gunopulos, · 2021
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Particle Graph Autoencoders and Differentiable, Learned Energy Mover’s Distance ,
S. Tsan, R. Kansal, A. Aportela, D. Diaz, J. Duarte, S. Krishna, F. Mokhtar, J.-R. Vlimant and M. Pierini, · 2021
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An open-source machine learning framework for global analyses of parton distributions ,
R. D. Ball et al. , · 2021
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Compressing PDF sets using generative adversarial networks ,
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M. Backes, A. Butter, T. Plehn and R. Winterhalder, · 2012
Cited alongside, same era.
Explainable machine learning of the underlying physics of high-energy particle collisions ,
Y. S. Lai, D. Neill, M. Płoskoń and F. Ringer, · 2012
Cited alongside, same era.
Measuring QCD Splittings with Invertible Networks ,
S. Bieringer, A. Butter, T. Heimel, S. Höche, U. Köthe, T. Plehn and S. T. Radev, · 2012
Cited alongside, same era.
How to GAN Higher Jet Resolution ,
P. Baldi, L. Blecher, A. Butter, J. Collado, J. N. Howard, F. Keilbach, T. Plehn, G. Kasieczka and D. Whiteson, · 2012
Cited alongside, same era.
J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O. Mattelaer, H. S. Shao, T. Stelzer, P. Torrielli and M. Zaro, · 2014
Cited alongside, same era.
A first unbiased global determination of polarized PDFs and their uncertainties ,
E. R. Nocera, R. D. Ball, S. Forte, G. Ridolfi and J. Rojo, · 2014
Cited alongside, same era.
DELPHES 3, A modular framework for fast simulation of a generic collider experiment ,
J. de Favereau, C. Delaere, P. Demin, A. Giammanco, V. Lemaître, A. Mertens and M. Selvaggi, · 2014
Cited alongside, same era.
An Introduction to PYTHIA 8.2 ,
T. Sjöstrand, S. Ask, J. R. Christiansen, R. Corke, N. Desai, P. Ilten, S. Mrenna, S. Prestel, C. O. Rasmussen and P. Z. Skands, · 2015
Cited alongside, same era.
S. Carrazza, J. M. Cruz-Martinez and T. R. Rabemananjara, · 2021
Later among the works it cites.
R. A. Khalek, V. Bertone and E. R. Nocera, · 2021
Later among the works it cites.
Generative Networks for Precision Enthusiasts (2021),
A. Butter, T. Heimel, S. Hummerich, T. Krebs, T. Plehn, A. Rousselot and S. Vent, · 2021
Later among the works it cites.
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
Later among the works it cites.
Analysis-Specific Fast Simulation at the LHC with Deep Learning ,
C. Chen, O. Cerri, T. Q. Nguyen, J. R. Vlimant and M. Pierini, · 2021
Later among the works it cites.
CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows (2021),
C. Krause and D. Shih, · 2021
Later among the works it cites.
C. Krause and D. Shih, · 2021
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Classifying Anomalies THrough Outer Density Estimation (CATHODE) (2021),
A. Hallin, J. Isaacson, G. Kasieczka, C. Krause, B. Nachman, T. Quadfasel, M. Schlaffer, D. Shih and M. Sommerhalder, · 2021
Later among the works it cites.
MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks ,
J. Pata, J. Duarte, J.-R. Vlimant, M. Pierini and M. Spiropulu, · 2021
Later among the works it cites.
S. R. Qasim, K. Long, J. Kieseler, M. Pierini and R. Nawaz, · 2021
Later among the works it cites.
Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution ,
A. Andreassen, P. T. Komiske, E. M. Metodiev, B. Nachman, A. Suresh and J. Thaler, · 2021
Later among the works it cites.
Preserving new physics while simultaneously unfolding all observables ,
P. Komiske, W. P. McCormack and B. Nachman, · 2021
Later among the works it cites.
V. Andreev et al. , · 2021
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G. Aad et al. , · 2021
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“SUSY-2019-04-ONNX.tgz” of “Search for R-parity-violating supersymmetry in a final state containing leptons and many jets with the ATLAS experiment using s = 13 T e V \sqrt{s}=13{TeV} proton–proton collision data” (Version 1) ,
ATLAS Collaboration, · 2021
Later among the works it cites.
Data and Analysis Preservation, Recasting, and Reinterpretation ,
S. Bailey et al. , · 2021
Later among the works it cites.
A FAIR and AI-ready Higgs boson decay dataset (2021),
Y. Chen et al. , · 2021
Later among the works it cites.
Back to the Formula – LHC Edition (2021),
A. Butter, T. Plehn, N. Soybelman and J. Brehmer, · 2021
Later among the works it cites.
G. Barenboim, J. Hirn and V. Sanz, · 2021
Later among the works it cites.
Event Generators for High-Energy Physics Experiments ,
J. M. Campbell et al. , · 2022
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Modeling hadronization using machine learning (2022),
P. Ilten, T. Menzo, A. Youssef and J. Zupan, · 2022
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Differentiable Matrix Elements with MadJax
L. Heinrich and M. Kagan, · 2022
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K. Danziger, T. Janßen, S. Schumann and F. Siegert, · 2022
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Loop Amplitudes from a Boosted Precision Network (2022),
S. Badger, A. Butter, M. Luchmann, S. Pitz and T. Plehn, · 2022
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Targeting multi-loop integrals with neural networks ,
R. Winterhalder, V. Magerya, E. Villa, S. P. Jones, M. Kerner, A. Butter, G. Heinrich and T. Plehn, · 2022
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The path to proton structure at 1% accuracy ,
R. D. Ball et al. , · 2022
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nNNPDF3.0: evidence for a modified partonic structure in heavy nuclei ,
R. Abdul Khalek, R. Gauld, T. Giani, E. R. Nocera, T. R. Rabemananjara and J. Rojo, · 2022
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A data-based parametrization of parton distribution functions ,
S. Carrazza, J. M. Cruz-Martinez and R. Stegeman, · 2022
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M. Soleymaninia, H. Hashamipour and H. Khanpour, · 2022
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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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Learning to simulate high energy particle collisions from unlabeled data ,
J. N. Howard, S. Mandt, D. Whiteson and Y. Yang, · 2022
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Understanding Event-Generation Networks via Uncertainties ,
M. Bellagente, M. Haußmann, M. Luchmann and T. Plehn, · 2022
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S. Caron, L. Hendriks and R. Verheyen, · 2022
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What’s Anomalous in LHC Jets? (2022),
T. Buss, B. M. Dillon, T. Finke, M. Krämer, A. Morandini, A. Mück, I. Oleksiyuk and T. Plehn, · 2022
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Publishing unbinned differential cross section results ,
M. Arratia et al. , · 2022
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Machine learning the Higgs boson-top quark CP phase ,
R. K. Barman, D. Gonçalves and F. Kling, · 2022
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Publishing statistical models: Getting the most out of particle physics experiments ,
K. Cranmer et al. , · 2022
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H. Bahl and S. Brass, · 2022
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Shared Data and Algorithms for Deep Learning in Fundamental Physics ,
L. Benato et al. , · 2022
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Ephemeral Learning - Augmenting Triggers with Online-Trained Normalizing Flows ,
A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman, T. Plehn, D. Shih and R. Winterhalder, · 2022
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Symmetry discovery with deep learning ,
K. Desai, B. Nachman and J. Thaler, · 2022
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