Built on

S. Otten, S. Caron, W. de Swart, M. van Beekveld, L. Hendriks, C. van Leeuwen, D. Podareanu, R. Ruiz de Austri, and R. Verheyen, Event Generation and Statistical Sampling for Physics with Deep Generative Models and a Density Information Buffer
Original
1901
Earlier work this paper cites.
B. Hashemi, N. Amin, K. Datta, D. Olivito, and M. Pierini, LHC analysis-specific datasets with Generative Adversarial Networks
Original
1901
Earlier work this paper cites.
J. Ren, L. Wu, and J. M. Yang, Unveiling CP property of top-Higgs coupling with graph neural networks at the LHC
Original
1901
Earlier work this paper cites.
R. Di Sipio, M. Faucci Giannelli, S. Ketabchi Haghighat, and S. Palazzo, DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC
Original
1903
Earlier work this paper cites.
S. Bollweg, M. Haußmann, G. Kasieczka, M. Luchmann, T. Plehn, and J. Thompson, Deep-Learning Jets with Uncertainties and More
Original
1904
Earlier work this paper cites.
J. Brehmer, F. Kling, I. Espejo, and K. Cranmer, MadMiner: Machine learning-based inference for particle physics
Original
1907
Earlier work this paper cites.
A. Butter, T. Plehn, and R. Winterhalder, How to GAN LHC Events
Original
1907
Earlier work this paper cites.
M. Kraus, T. Martini, S. Peitzsch, and P. Uwer, Exploring BSM Higgs couplings in single top-quark production
Original
1908
Earlier work this paper cites.
A. Andreassen, P. T. Komiske, E. M. Metodiev, B. Nachman, and J. Thaler, OmniFold: A Method to Simultaneously Unfold All Observables
Original
1911
Earlier work this paper cites.
A. Butter, T. Plehn, and R. Winterhalder, How to GAN Event Subtraction
Original
1912
Earlier work this paper cites.
D. Belayneh et al
Original
1912
Earlier work this paper cites.
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, and R. Winterhalder, How to GAN away Detector Effects
Original
1912
Earlier work this paper cites.
Curran Associates, Inc., 2019
Original
A. Paszke et al · 1912
Earlier work this paper cites.
K. Kondo, Dynamical Likelihood Method for Reconstruction of Events With Missing Momentum. 1: Method and Toy Models
1988
Earlier work this paper cites.
K. Kondo, Dynamical likelihood method for reconstruction of events with missing momentum. 2: Mass spectra for 2 → \to 2 processes
1991
Earlier work this paper cites.
D. MacKay, Probable Networks and Plausible Predictions – A Review of Practical Bayesian Methods for Supervised Neural Networks
1995
Earlier work this paper cites.
PhD thesis, Toronto, 1995
R. M. Neal, Bayesian learning for neural networks · 1995
Earlier work this paper cites.
Clarendon Press, 1998
G. Cowan, Statistical data analysis · 1998
Earlier work this paper cites.
D0 Collaboration, Measurement of the Top Quark Mass in the Dilepton Channel
1999
Earlier work this paper cites.
E. Bothmann, T. Janßen, M. Knobbe, T. Schmale, and S. Schumann, Exploring phase space with Neural Importance Sampling
Original
2001
Earlier work this paper cites.
C. Gao, J. Isaacson, and C. Krause, i-flow: High-dimensional Integration and Sampling with Normalizing Flows
Original
2001
Earlier work this paper cites.
C. Gao, S. Höche, J. Isaacson, C. Krause, and H. Schulz, Event Generation with Normalizing Flows
Original
2001
Earlier work this paper cites.
Y. Alanazi, N. Sato, T. Liu, W. Melnitchouk, M. P. Kuchera, E. Pritchard, M. Robertson, R. Strauss, L. Velasco, and Y. Li, Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)
Original
2001
Earlier work this paper cites.
B. Nachman and D. Shih, Anomaly Detection with Density Estimation
Original
2001
Earlier work this paper cites.
V. Mikuni and F. Canelli, ABCNet: An attention-based method for particle tagging
Original
2001
Earlier work this paper cites.
F. A. Di Bello, S. Ganguly, E. Gross, M. Kado, M. Pitt, L. Santi, and J. Shlomi, Towards a Computer Vision Particle Flow
Original
2003
Earlier work this paper cites.
G. Kasieczka, M. Luchmann, F. Otterpohl, and T. Plehn, Per-Object Systematics using Deep-Learned Calibration
Original
2003
Earlier work this paper cites.
D0 Collaboration, A precision measurement of the mass of the top quark
2004
Earlier work this paper cites.
E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol, and K. Krüger, Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed
Original
2005
Earlier work this paper cites.
CDF Collaboration, Top quark mass measurement from dilepton events at CDF II with the matrix-element method
2006
Earlier work this paper cites.
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, A. Rousselot, R. Winterhalder, L. Ardizzone, and U. Köthe, Invertible Networks or Partons to Detector and Back Again
Original
2006
Earlier work this paper cites.
B. Bortolato, J. F. Kamenik, N. Košnik, and A. Smolkovič, Optimized probes of C P CP -odd effects in the t t ¯ h t\bar{t}h process at hadron colliders
Original
2006
Earlier work this paper cites.
K. Cranmer and T. Plehn, Maximum significance at the LHC and Higgs decays to muons
2007
Earlier work this paper cites.

Similar

H. Bahl, P. Bechtle, S. Heinemeyer, J. Katzy, T. Klingl, K. Peters, M. Saimpert, T. Stefaniak, and G. Weiglein, Indirect 𝒞 𝒫 \mathcal{CP} probes of the Higgs-top-quark interaction: current LHC constraints and future opportunities
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2007
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F. Bury and C. Delaere, Matrix element regression with deep neural networks — Breaking the CPU barrier
Original
2008
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Then

A. Andreassen, I. Feige, C. Frye, and M. D. Schwartz, JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics
Original
2019
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E. Bothmann and L. Debbio, Reweighting a parton shower using a neural network: the final-state case
Original
2019
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M. Erdmann, J. Glombitza, and T. Quast, Precise simulation of electromagnetic calorimeter showers using a Wasserstein Generative Adversarial Network
Original
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E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol, and K. Krüger, Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network
Original
2021
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

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Reading the bibliography…

2023

Precision-Machine Learning for the Matrix Element Method

Heimel, Theo, Huetsch, Nathan, Winterhalder, Ramon et al.

Understand

The matrix element method is the LHC inference method of choice for limited statistics.

We present a dedicated machine learning framework, based on efficient phase-space integration, a learned acceptance and transfer function.
It is based on a choice of INN and diffusion networks, and a transformer to solve jet combinatorics.
We showcase this setup for the CP-phase of the top Yukawa coupling in associated Higgs and single-top production.

Built on

S. Otten, S. Caron, W. de Swart, M. van Beekveld, L. Hendriks, C. van Leeuwen, D. Podareanu, R. Ruiz de Austri, and R. Verheyen, Event Generation and Statistical Sampling for Physics with Deep Generative Models and a Density Information Buffer
Original
1901
Earlier work this paper cites.
B. Hashemi, N. Amin, K. Datta, D. Olivito, and M. Pierini, LHC analysis-specific datasets with Generative Adversarial Networks
Original
1901
Earlier work this paper cites.
J. Ren, L. Wu, and J. M. Yang, Unveiling CP property of top-Higgs coupling with graph neural networks at the LHC
Original
1901
Earlier work this paper cites.
R. Di Sipio, M. Faucci Giannelli, S. Ketabchi Haghighat, and S. Palazzo, DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC
Original
1903
Earlier work this paper cites.
S. Bollweg, M. Haußmann, G. Kasieczka, M. Luchmann, T. Plehn, and J. Thompson, Deep-Learning Jets with Uncertainties and More
Original
1904
Earlier work this paper cites.
J. Brehmer, F. Kling, I. Espejo, and K. Cranmer, MadMiner: Machine learning-based inference for particle physics
Original
1907
Earlier work this paper cites.
A. Butter, T. Plehn, and R. Winterhalder, How to GAN LHC Events
Original
1907
Earlier work this paper cites.
M. Kraus, T. Martini, S. Peitzsch, and P. Uwer, Exploring BSM Higgs couplings in single top-quark production
Original
1908
Earlier work this paper cites.
A. Andreassen, P. T. Komiske, E. M. Metodiev, B. Nachman, and J. Thaler, OmniFold: A Method to Simultaneously Unfold All Observables
Original
1911
Earlier work this paper cites.
A. Butter, T. Plehn, and R. Winterhalder, How to GAN Event Subtraction
Original
1912
Earlier work this paper cites.
D. Belayneh et al
Original
1912
Earlier work this paper cites.
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, and R. Winterhalder, How to GAN away Detector Effects
Original
1912
Earlier work this paper cites.
Curran Associates, Inc., 2019
Original
A. Paszke et al · 1912
Earlier work this paper cites.
K. Kondo, Dynamical Likelihood Method for Reconstruction of Events With Missing Momentum. 1: Method and Toy Models
1988
Earlier work this paper cites.
K. Kondo, Dynamical likelihood method for reconstruction of events with missing momentum. 2: Mass spectra for 2 → \to 2 processes
1991
Earlier work this paper cites.
D. MacKay, Probable Networks and Plausible Predictions – A Review of Practical Bayesian Methods for Supervised Neural Networks
1995
Earlier work this paper cites.
PhD thesis, Toronto, 1995
R. M. Neal, Bayesian learning for neural networks · 1995
Earlier work this paper cites.
Clarendon Press, 1998
G. Cowan, Statistical data analysis · 1998
Earlier work this paper cites.
D0 Collaboration, Measurement of the Top Quark Mass in the Dilepton Channel
1999
Earlier work this paper cites.
E. Bothmann, T. Janßen, M. Knobbe, T. Schmale, and S. Schumann, Exploring phase space with Neural Importance Sampling
Original
2001
Earlier work this paper cites.
C. Gao, J. Isaacson, and C. Krause, i-flow: High-dimensional Integration and Sampling with Normalizing Flows
Original
2001
Earlier work this paper cites.
C. Gao, S. Höche, J. Isaacson, C. Krause, and H. Schulz, Event Generation with Normalizing Flows
Original
2001
Earlier work this paper cites.
Y. Alanazi, N. Sato, T. Liu, W. Melnitchouk, M. P. Kuchera, E. Pritchard, M. Robertson, R. Strauss, L. Velasco, and Y. Li, Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)
Original
2001
Earlier work this paper cites.
B. Nachman and D. Shih, Anomaly Detection with Density Estimation
Original
2001
Earlier work this paper cites.
V. Mikuni and F. Canelli, ABCNet: An attention-based method for particle tagging
Original
2001
Earlier work this paper cites.
F. A. Di Bello, S. Ganguly, E. Gross, M. Kado, M. Pitt, L. Santi, and J. Shlomi, Towards a Computer Vision Particle Flow
Original
2003
Earlier work this paper cites.
G. Kasieczka, M. Luchmann, F. Otterpohl, and T. Plehn, Per-Object Systematics using Deep-Learned Calibration
Original
2003
Earlier work this paper cites.
D0 Collaboration, A precision measurement of the mass of the top quark
2004
Earlier work this paper cites.
E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol, and K. Krüger, Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed
Original
2005
Earlier work this paper cites.
CDF Collaboration, Top quark mass measurement from dilepton events at CDF II with the matrix-element method
2006
Earlier work this paper cites.
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, A. Rousselot, R. Winterhalder, L. Ardizzone, and U. Köthe, Invertible Networks or Partons to Detector and Back Again
Original
2006
Earlier work this paper cites.
B. Bortolato, J. F. Kamenik, N. Košnik, and A. Smolkovič, Optimized probes of C P CP -odd effects in the t t ¯ h t\bar{t}h process at hadron colliders
Original
2006
Earlier work this paper cites.
K. Cranmer and T. Plehn, Maximum significance at the LHC and Higgs decays to muons
2007
Earlier work this paper cites.

Similar

H. Bahl, P. Bechtle, S. Heinemeyer, J. Katzy, T. Klingl, K. Peters, M. Saimpert, T. Stefaniak, and G. Weiglein, Indirect 𝒞 𝒫 \mathcal{CP} probes of the Higgs-top-quark interaction: current LHC constraints and future opportunities
Original
2007
Cited alongside, same era.
F. Bury and C. Delaere, Matrix element regression with deep neural networks — Breaking the CPU barrier
Original
2008
Cited alongside, same era.
A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman, and T. Plehn, GANplifying event samples
Original
2008
Cited alongside, same era.
I.-K. Chen, M. D. Klimek, and M. Perelstein, Improved Neural Network Monte Carlo Simulation
Original
2009
Cited alongside, same era.
K. Dohi, Variational Autoencoders for Jet Simulation

Then

A. Andreassen, I. Feige, C. Frye, and M. D. Schwartz, JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics
Original
2019
Later among the works it cites.
E. Bothmann and L. Debbio, Reweighting a parton shower using a neural network: the final-state case
Original
2019
Later among the works it cites.
M. Erdmann, J. Glombitza, and T. Quast, Precise simulation of electromagnetic calorimeter showers using a Wasserstein Generative Adversarial Network
Original
2019
Later among the works it cites.
E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol, and K. Krüger, Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network
Original
2021
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Original

2009

Cited alongside, same era.

F. Fiedler, A. Grohsjean, P. Haefner, and P. Schieferdecker, The Matrix Element Method and its Application in Measurements of the Top Quark Mass

Original

2010

Cited alongside, same era.

P. Artoisenet, V. Lemaitre, F. Maltoni, and O. Mattelaer, Automation of the matrix element reweighting method

Original

2010

Cited alongside, same era.

J. Alwall, A. Freitas, and O. Mattelaer, The Matrix Element Method and QCD Radiation

Original

2011

Cited alongside, same era.

B. Stienen and R. Verheyen, Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows

Original

2011

Cited alongside, same era.

M. Backes, A. Butter, T. Plehn, and R. Winterhalder, How to GAN Event Unweighting

Original

2012

Cited alongside, same era.

P. Baldi, L. Blecher, A. Butter, J. Collado, J. N. Howard, F. Keilbach, T. Plehn, G. Kasieczka, and D. Whiteson, How to GAN Higher Jet Resolution

Original

2012

Cited alongside, same era.

S. Bieringer, A. Butter, T. Heimel, S. Höche, U. Köthe, T. Plehn, and S. T. Radev, Measuring QCD Splittings with Invertible Networks

Original

2012

Cited alongside, same era.

C. Chen, O. Cerri, T. Q. Nguyen, J. R. Vlimant, and M. Pierini, Analysis-Specific Fast Simulation at the LHC with Deep Learning

2021