Built on

M. Kraus, T. Martini, and P. Uwer, Predicting event weights at next-to-leading order QCD for jet events defined by 2 → 1 2\to 1 jet algorithms
Original
1901
Earlier work this paper cites.
A. Butter et al
Original
1902
Earlier work this paper cites.
J. Hermans, V. Begy, and G. Louppe, Likelihood-free MCMC with Approximate Likelihood Ratios
Original
1903
Earlier work this paper cites.
J. Alsing and B. Wandelt, Nuisance hardened data compression for fast likelihood-free inference
Original
1903
Earlier work this paper cites.
A. Andreassen and B. Nachman, Neural Networks for Full Phase-space Reweighting and Parameter Tuning
Original
1907
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. G. Baydin, L. Shao, W. Bhimji, L. Heinrich, L. Meadows, J. Liu, A. Munk, S. Naderiparizi, B. Gram-Hansen, G. Louppe, M. Ma, X. Zhao, P. Torr, V. Lee, K. Cranmer, Prabhat, and F. Wood, Etalumis: Bringing probabilistic programming to scientific simulators at scale
Original
1907
Earlier work this paper cites.
J. Brehmer, S. Dawson, S. Homiller, F. Kling, and T. Plehn, Benchmarking simplified template cross sections in W H WH production
Original
1908
Earlier work this paper cites.
J. Brehmer, S. Mishra-Sharma, J. Hermans, G. Louppe, and K. Cranmer, Mining for Dark Matter Substructure: Inferring Subhalo Population Properties from Strong Lenses with Machine Learning
Original
1909
Earlier work this paper cites.
B. Nachman and C. Shimmin, AI Safety for High Energy Physics
Original
1910
Earlier work this paper cites.
National Academy of Sciences, 2020
Original
K. Cranmer, J. Brehmer, and G. Louppe, The frontier of simulation-based inference · 1911
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.
G. Papamakarios, E. Nalisnick, D. Jimenez Rezende, S. Mohamed, and B. Lakshminarayanan, Normalizing Flows for Probabilistic Modeling and Inference
Original
1912
Earlier work this paper cites.
S. S. Wilks, The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses
1938
Earlier work this paper cites.
A. Wald, Tests of Statistical Hypotheses Concerning Several Parameters When the Number of Observations is Large
1943
Earlier work this paper cites.
F. James and M. Roos, Minuit: A System for Function Minimization and Analysis of the Parameter Errors and Correlations
1975
Earlier work this paper cites.
D. B. Rubin, Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician
1984
Earlier work this paper cites.
K. Kondo, Dynamical Likelihood Method for Reconstruction of Events with Missing Momentum: I. Method and Toy Models
1988
Earlier work this paper cites.
D. Atwood and A. Soni, Analysis for magnetic moment and electric dipole moment form factors of the top quark via e + e − → t t ¯ e^{+}e^{-}\to t\bar{t}
1992
Earlier work this paper cites.
M. Davier, L. Duflot, F. Le Diberder, and A. Rougé, The optimal method for the measurement of tau polarization
1993
Earlier work this paper cites.
M. Diehl and O. Nachtmann, Optimal observables for the measurement of three gauge boson couplings in e + e − → W + W − e^{+}e^{-}\to W^{+}W^{-}
1994
Earlier work this paper cites.
L. Holmstrom, S. Sain, and H. Miettinen, A New multivariate technique for top quark search
1995
Earlier work this paper cites.
G. Corcella, I. G. Knowles, G. Marchesini, S. Moretti, K. Odagiri, P. Richardson, M. H. Seymour, and B. R. Webber, HERWIG 6: An Event generator for hadron emission reactions with interfering gluons (including supersymmetric processes)
2001
Earlier work this paper cites.
K. Cranmer, Kernel estimation in high-energy physics
2001
Earlier work this paper cites.
B. Nachman and D. Shih, Anomaly Detection with Density Estimation
Original
2001
Earlier work this paper cites.
A. Andreassen, B. Nachman, and D. Shih, Simulation Assisted Likelihood-free Anomaly Detection
Original
2001
Earlier work this paper cites.
C. Durkan, I. Murray, and G. Papamakarios, On Contrastive Learning for Likelihood-free Inference
Original
2002
Earlier work this paper cites.
J. Hollingsworth and D. Whiteson, Resonance Searches with Machine Learned Likelihood Ratios
Original
2002
Earlier work this paper cites.

Similar

T. G. Collaboration, Geant4 – a simulation toolkit
2003
Cited alongside, same era.
V. M. Abazov and Others, A precision measurement of the mass of the top quark
2004
Cited alongside, same era.
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
Cited alongside, same era.
S. Chen, A. Glioti, G. Panico, and A. Wulzer, Parametrized classifiers for optimal EFT sensitivity
Original
2007
Cited alongside, same era.
P. Artoisenet and O. Mattelaer, MadWeight: Automatic event reweighting with matrix elements
2008
Cited alongside, same era.

Then

G. Aad and Others, “A morphing technique for signal modelling in a multidimensional space of coupling parameters.” 2015
2015
Later among the works it cites.
A. V. Gritsan, R. Röntsch, M. Schulze, and M. Xiao, Constraining anomalous Higgs boson couplings to the heavy-flavor fermions using matrix element techniques
Original
2016
Later among the works it cites.
C. Englert, O. Mattelaer, and M. Spannowsky, Measuring the Higgs-bottom coupling in weak boson fusion
Original
2016
Later among the works it cites.
K. Cranmer and G. Louppe, Unifying generative models and exact likelihood-free inference with conditional bijections
2016
Later among the works it cites.
P. Baldi, K. Cranmer, T. Faucett, P. Sadowski, and D. Whiteson, Parameterized neural networks for high-energy physics
Original

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2020

Simulation-based inference methods for particle physics

Brehmer, Johann, Cranmer, Kyle

Understand

Our predictions for particle physics processes are realized in a chain of complex simulators.

They allow us to generate high-fidelity simulated data, but they are not well-suited for inference on the theory parameters with observed data.
We explain why the likelihood function of high-dimensional LHC data cannot be explicitly evaluated, why this matters for data analysis, and reframe what the field has traditionally done to circumvent this problem.
We then review new simulation-based inference methods that let us directly analyze high-dimensional data by combining machine learning techniques and information from the simulator.

Built on

M. Kraus, T. Martini, and P. Uwer, Predicting event weights at next-to-leading order QCD for jet events defined by 2 → 1 2\to 1 jet algorithms
Original
1901
Earlier work this paper cites.
A. Butter et al
Original
1902
Earlier work this paper cites.
J. Hermans, V. Begy, and G. Louppe, Likelihood-free MCMC with Approximate Likelihood Ratios
Original
1903
Earlier work this paper cites.
J. Alsing and B. Wandelt, Nuisance hardened data compression for fast likelihood-free inference
Original
1903
Earlier work this paper cites.
A. Andreassen and B. Nachman, Neural Networks for Full Phase-space Reweighting and Parameter Tuning
Original
1907
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. G. Baydin, L. Shao, W. Bhimji, L. Heinrich, L. Meadows, J. Liu, A. Munk, S. Naderiparizi, B. Gram-Hansen, G. Louppe, M. Ma, X. Zhao, P. Torr, V. Lee, K. Cranmer, Prabhat, and F. Wood, Etalumis: Bringing probabilistic programming to scientific simulators at scale
Original
1907
Earlier work this paper cites.
J. Brehmer, S. Dawson, S. Homiller, F. Kling, and T. Plehn, Benchmarking simplified template cross sections in W H WH production
Original
1908
Earlier work this paper cites.
J. Brehmer, S. Mishra-Sharma, J. Hermans, G. Louppe, and K. Cranmer, Mining for Dark Matter Substructure: Inferring Subhalo Population Properties from Strong Lenses with Machine Learning
Original
1909
Earlier work this paper cites.
B. Nachman and C. Shimmin, AI Safety for High Energy Physics
Original
1910
Earlier work this paper cites.
National Academy of Sciences, 2020
Original
K. Cranmer, J. Brehmer, and G. Louppe, The frontier of simulation-based inference · 1911
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.
G. Papamakarios, E. Nalisnick, D. Jimenez Rezende, S. Mohamed, and B. Lakshminarayanan, Normalizing Flows for Probabilistic Modeling and Inference
Original
1912
Earlier work this paper cites.
S. S. Wilks, The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses
1938
Earlier work this paper cites.
A. Wald, Tests of Statistical Hypotheses Concerning Several Parameters When the Number of Observations is Large
1943
Earlier work this paper cites.
F. James and M. Roos, Minuit: A System for Function Minimization and Analysis of the Parameter Errors and Correlations
1975
Earlier work this paper cites.
D. B. Rubin, Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician
1984
Earlier work this paper cites.
K. Kondo, Dynamical Likelihood Method for Reconstruction of Events with Missing Momentum: I. Method and Toy Models
1988
Earlier work this paper cites.
D. Atwood and A. Soni, Analysis for magnetic moment and electric dipole moment form factors of the top quark via e + e − → t t ¯ e^{+}e^{-}\to t\bar{t}
1992
Earlier work this paper cites.
M. Davier, L. Duflot, F. Le Diberder, and A. Rougé, The optimal method for the measurement of tau polarization
1993
Earlier work this paper cites.
M. Diehl and O. Nachtmann, Optimal observables for the measurement of three gauge boson couplings in e + e − → W + W − e^{+}e^{-}\to W^{+}W^{-}
1994
Earlier work this paper cites.
L. Holmstrom, S. Sain, and H. Miettinen, A New multivariate technique for top quark search
1995
Earlier work this paper cites.
G. Corcella, I. G. Knowles, G. Marchesini, S. Moretti, K. Odagiri, P. Richardson, M. H. Seymour, and B. R. Webber, HERWIG 6: An Event generator for hadron emission reactions with interfering gluons (including supersymmetric processes)
2001
Earlier work this paper cites.
K. Cranmer, Kernel estimation in high-energy physics
2001
Earlier work this paper cites.
B. Nachman and D. Shih, Anomaly Detection with Density Estimation
Original
2001
Earlier work this paper cites.
A. Andreassen, B. Nachman, and D. Shih, Simulation Assisted Likelihood-free Anomaly Detection
Original
2001
Earlier work this paper cites.
C. Durkan, I. Murray, and G. Papamakarios, On Contrastive Learning for Likelihood-free Inference
Original
2002
Earlier work this paper cites.
J. Hollingsworth and D. Whiteson, Resonance Searches with Machine Learned Likelihood Ratios
Original
2002
Earlier work this paper cites.

Similar

T. G. Collaboration, Geant4 – a simulation toolkit
2003
Cited alongside, same era.
V. M. Abazov and Others, A precision measurement of the mass of the top quark
2004
Cited alongside, same era.
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
Cited alongside, same era.
S. Chen, A. Glioti, G. Panico, and A. Wulzer, Parametrized classifiers for optimal EFT sensitivity
Original
2007
Cited alongside, same era.
P. Artoisenet and O. Mattelaer, MadWeight: Automatic event reweighting with matrix elements
2008
Cited alongside, same era.

Then

G. Aad and Others, “A morphing technique for signal modelling in a multidimensional space of coupling parameters.” 2015
2015
Later among the works it cites.
A. V. Gritsan, R. Röntsch, M. Schulze, and M. Xiao, Constraining anomalous Higgs boson couplings to the heavy-flavor fermions using matrix element techniques
Original
2016
Later among the works it cites.
C. Englert, O. Mattelaer, and M. Spannowsky, Measuring the Higgs-bottom coupling in weak boson fusion
Original
2016
Later among the works it cites.
K. Cranmer and G. Louppe, Unifying generative models and exact likelihood-free inference with conditional bijections
2016
Later among the works it cites.
P. Baldi, K. Cranmer, T. Faucett, P. Sadowski, and D. Whiteson, Parameterized neural networks for high-energy physics
Original

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

P. Ranjan, D. Bingham, and G. Michailidis, Sequential experiment design for contour estimation from complex computer codes

2008

Cited alongside, same era.

T. Gleisberg, S. Hoeche, F. Krauss, M. Schonherr, S. Schumann, F. Siegert, and J. Winter, Event generation with SHERPA 1.1

Original

2009

Cited alongside, same era.

A. J. Barr and C. G. Lester, A Review of the Mass Measurement Techniques proposed for the Large Hadron Collider

Original

2010

Cited alongside, same era.

Y. Gao, A. V. Gritsan, Z. Guo, K. Melnikov, M. Schulze, and N. V. Tran, Spin determination of single-produced resonances at hadron colliders

Original

2010

Cited alongside, same era.

L. Moneta, K. Belasco, K. S. Cranmer, S. Kreiss, A. Lazzaro, D. Piparo, G. Schott, W. Verkerke, and M. Wolf, The RooStats Project

Original

2010

Cited alongside, same era.

J. Alwall, A. Freitas, and O. Mattelaer, Matrix element method and QCD radiation

Original

2011

Cited alongside, same era.

D. E. Soper and M. Spannowsky, Finding physics signals with shower deconstruction

Original

2011

Cited alongside, same era.

2016