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\rightarrow 1 jet algorithms’ , 2019. arXiv:1901.08008
Original
1901
Earlier work this paper cites.
M. Cepeda et al. (Physics of the HL-LHC Working Group): ‘Higgs Physics at the HL-LHC and HE-LHC’ , 2019. arXiv:1902.00134
Original
1902
Earlier work this paper cites.
J. Hermans, V. Begy, and G. Louppe: ‘Likelihood-free MCMC with Approximate Likelihood Ratios’ , 2019. arXiv:1903.04057
Original
1903
Earlier work this paper cites.
J. Alsing, T. Charnock, S. Feeney, and B. Wandelt: ‘Fast likelihood-free cosmology with neural density estimators and active learning’ , 2019. arXiv:1903.00007
Original
1903
Earlier work this paper cites.
J. Alsing and B. Wandelt: ‘Nuisance hardened data compression for fast likelihood-free inference’ , 2019. arXiv:1903.01473
Original
1903
Earlier work this paper cites.
D. S. Greenberg, M. Nonnenmacher, and J. H. Macke: ‘Automatic Posterior Transformation for Likelihood-Free Inference’. arXiv e-prints arXiv:1905.07488, 2019. arXiv:1905.07488
Original
1905
Earlier work this paper cites.
E. Rodrigues: ‘The Scikit-HEP Project’. In ‘23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018) Sofia, Bulgaria, July 9-13, 2018’, , 2019. arXiv:1905.00002
Original
1905
Earlier work this paper cites.
J. Brehmer, K. Cranmer, I. Espejo, F. Kling, G. Louppe, and J. Pavez: ‘Effective LHC measurements with matrix elements and machine learning’, 2019. arXiv:1906.01578
Original
1906
Earlier work this paper cites.
S. S. Wilks: ‘The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses’. Annals Math. Statist. 9 (1), p. 60, 1938
1938
Earlier work this paper cites.
A. Wald: ‘Tests of statistical hypotheses concerning several parameters when the number of observations is large’. Transactions of the American Mathematical Society 54 (3), p. 426, 1943
1943
Earlier work this paper cites.
C. Radhakrishna Rao: ‘Information and the accuracy attainable in the estimation of statistical parameters’. Bull. Calcutta Math. Soc. 37, p. 81, 1945
1945
Earlier work this paper cites.
H. Cramér: ‘Mathematical Methods of Statistics’. Princeton University Press, 1946, ISBN 0691080046
1946
Earlier work this paper cites.
B. Efron: ‘Defining the curvature of a statistical problem (with applications to second order efficiency)’. Ann. Statist. 3 (6), p. 1189, 1975
1975
Earlier work this paper cites.
S.-I. Amari: ‘Differential geometry of curved exponential families-curvatures and information loss’. Ann. Statist. 10 (2), p. 357, 1982
1982
Earlier work this paper cites.
D. B. Rubin: ‘Bayesianly justifiable and relevant frequency calculations for the applied statistician’. Ann. Statist. 12 (4), p. 1151, 1984
1984
Earlier work this paper cites.
K. Kondo: ‘Dynamical Likelihood Method for Reconstruction of Events With Missing Momentum. 1: Method and Toy Models’. J. Phys. Soc. Jap. 57, p. 4126, 1988
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} ’. Phys. Rev. D45, p. 2405, 1992
1992
Earlier work this paper cites.
M. Davier, L. Duflot, F. Le Diberder, and A. Rouge: ‘The Optimal method for the measurement of tau polarization’. Phys. Lett. B306, p. 411, 1993
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^{-} ’. Z. Phys. C62, p. 397, 1994
1994
Earlier work this paper cites.
G. Van Rossum and F. L. Drake Jr: ‘Python tutorial’. Centrum voor Wiskunde en Informatica Amsterdam, The Netherlands, 1995
1995
Earlier work this paper cites.
N. Qian: ‘On the momentum term in gradient descent learning algorithms’. Neural Netw. 12 (1), p. 145, 1999
1999
Earlier work this paper cites.
K. S. Cranmer: ‘Kernel estimation in high-energy physics’. Comput. Phys. Commun. 136, p. 198, 2001. arXiv:hep-ex/0011057
2001
Earlier work this paper cites.
M. Dobbs and J. B. Hansen: ‘The HepMC C++ Monte Carlo event record for High Energy Physics’. Comput. Phys. Commun. 134, p. 41, 2001
2001
Earlier work this paper cites.
M. A. Beaumont, W. Zhang, and D. J. Balding: ‘Approximate bayesian computation in population genetics’. Genetics 162 (4), p. 2025, 2002
2002
Earlier work this paper cites.
S. Agostinelli et al. (GEANT4): ‘GEANT4: A Simulation toolkit’. Nucl. Instrum. Meth. A506, p. 250, 2003
2003
Earlier work this paper cites.
V. M. Abazov et al. (D0): ‘A precision measurement of the mass of the top quark’. Nature 429, p. 638, 2004. arXiv:hep-ex/0406031
2004
Earlier work this paper cites.
T. Oliphant: ‘NumPy: A guide to NumPy’. USA: Trelgol Publishing, 2006–. URL http://www.numpy.org/
2006
Earlier work this paper cites.
G. F. Giudice, C. Grojean, A. Pomarol, and R. Rattazzi: ‘The Strongly-Interacting Light Higgs’. JHEP 06, p. 045, 2007. arXiv:hep-ph/0703164
2007
Earlier work this paper cites.
J. D. Hunter: ‘Matplotlib: A 2d graphics environment’. Computing in Science & Engineering 9 (3), p. 90, 2007
2007
Earlier work this paper cites.
P. Artoisenet and O. Mattelaer: ‘MadWeight: Automatic event reweighting with matrix elements’. PoS CHARGED2008, p. 025, 2008
2008
Earlier work this paper cites.
T. Sjostrand, S. Mrenna, and P. Z. Skands: ‘A Brief Introduction to PYTHIA 8.1’. Comput. Phys. Commun. 178, p. 852, 2008. arXiv:0710.3820
Original
2008
Earlier work this paper cites.
Y. Gao, A. V. Gritsan, Z. Guo, K. Melnikov, M. Schulze, and N. V. Tran: ‘Spin determination of single-produced resonances at hadron colliders’. Phys. Rev. D81, p. 075022, 2010. arXiv:1001.3396
Original
2010
Earlier work this paper cites.
The HDF Group: ‘Hierarchical data format version 5’, 2000-2010. URL http://www.hdfgroup.org/HDF5
2010
Earlier work this paper cites.
J. Alwall, A. Freitas, and O. Mattelaer: ‘The Matrix Element Method and QCD Radiation’. Phys. Rev. D83, p. 074010, 2011. arXiv:1010.2263
Original
2011
Earlier work this paper cites.
D. E. Soper and M. Spannowsky: ‘Finding physics signals with shower deconstruction’. Phys. Rev. D84, p. 074002, 2011. arXiv:1102.3480
Original
2011
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, et al.: ‘Scikit-learn: Machine learning in Python’. Journal of Machine Learning Research 12, p. 2825, 2011
2011
Earlier work this paper cites.
K. Cranmer, G. Lewis, L. Moneta, A. Shibata, and W. Verkerke (ROOT): ‘HistFactory: A tool for creating statistical models for use with RooFit and RooStats’ , 2012
2012
Earlier work this paper cites.
S. Bolognesi, Y. Gao, A. V. Gritsan, et al.: ‘On the spin and parity of a single-produced resonance at the LHC’. Phys. Rev. D86, p. 095031, 2012. arXiv:1208.4018
Original
2012
Earlier work this paper cites.

Similar

Y. Fan, D. J. Nott, and S. A. Sisson: ‘Approximate Bayesian Computation via Regression Density Estimation’. ArXiv e-prints , 2012. arXiv:1212.1479
Original
2012
Cited alongside, same era.
R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, R. Pittau, and P. Torrielli: ‘Four-lepton production at hadron colliders: aMC@NLO predictions with theoretical uncertainties’. JHEP 02, p. 099, 2012. arXiv:1110.4738
Original
2012
Cited alongside, same era.
P. Avery et al.: ‘Precision studies of the Higgs boson decay channel H → Z Z → 4 l H\to ZZ\to 4l with MEKD’. Phys. Rev. D87 (5), p. 055006, 2013. arXiv:1210.0896
Original
2013
Cited alongside, same era.
J. R. Andersen, C. Englert, and M. Spannowsky: ‘Extracting precise Higgs couplings by using the matrix element method’. Phys. Rev. D87 (1), p. 015019, 2013. arXiv:1211.3011
Original
2013
Cited alongside, same era.

Then

T. Kluyver, B. Ragan-Kelley, F. Pérez, et al.: ‘Jupyter notebooks - a publishing format for reproducible computational workflows’. In ‘ELPUB’, , 2016
2016
Later among the works it cites.
M. Frate, K. Cranmer, S. Kalia, A. Vandenberg-Rodes, and D. Whiteson: ‘Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes’ , 2017. arXiv:1709.05681
Original
2017
Later among the works it cites.
J. Brehmer, K. Cranmer, F. Kling, and T. Plehn: ‘Better Higgs boson measurements through information geometry’. Phys. Rev. D95 (7), p. 073002, 2017. arXiv:1612.05261
Original
2017
Later among the works it cites.
T. Martini and P. Uwer: ‘The Matrix Element Method at next-to-leading order QCD for hadronic collisions: Single top-quark production at the LHC as an example application’ , 2017. arXiv:1712.04527
Original
2017
Later among the works it cites.

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Reading the bibliography…

2019

MadMiner: Machine learning-based inference for particle physics

Brehmer, Johann, Kling, Felix, Espejo, Irina et al.

Understand

Precision measurements at the LHC often require analyzing high-dimensional event data for subtle kinematic signatures, which is challenging for established analysis methods.

Recently, a powerful family of multivariate inference techniques that leverage both matrix element information and machine learning has been developed.
This approach neither requires the reduction of high-dimensional data to summary statistics nor any simplifications to the underlying physics or detector response.
In this paper we introduce MadMiner, a Python module that streamlines the steps involved in this procedure.

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\rightarrow 1 jet algorithms’ , 2019. arXiv:1901.08008
Original
1901
Earlier work this paper cites.
M. Cepeda et al. (Physics of the HL-LHC Working Group): ‘Higgs Physics at the HL-LHC and HE-LHC’ , 2019. arXiv:1902.00134
Original
1902
Earlier work this paper cites.
J. Hermans, V. Begy, and G. Louppe: ‘Likelihood-free MCMC with Approximate Likelihood Ratios’ , 2019. arXiv:1903.04057
Original
1903
Earlier work this paper cites.
J. Alsing, T. Charnock, S. Feeney, and B. Wandelt: ‘Fast likelihood-free cosmology with neural density estimators and active learning’ , 2019. arXiv:1903.00007
Original
1903
Earlier work this paper cites.
J. Alsing and B. Wandelt: ‘Nuisance hardened data compression for fast likelihood-free inference’ , 2019. arXiv:1903.01473
Original
1903
Earlier work this paper cites.
D. S. Greenberg, M. Nonnenmacher, and J. H. Macke: ‘Automatic Posterior Transformation for Likelihood-Free Inference’. arXiv e-prints arXiv:1905.07488, 2019. arXiv:1905.07488
Original
1905
Earlier work this paper cites.
E. Rodrigues: ‘The Scikit-HEP Project’. In ‘23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018) Sofia, Bulgaria, July 9-13, 2018’, , 2019. arXiv:1905.00002
Original
1905
Earlier work this paper cites.
J. Brehmer, K. Cranmer, I. Espejo, F. Kling, G. Louppe, and J. Pavez: ‘Effective LHC measurements with matrix elements and machine learning’, 2019. arXiv:1906.01578
Original
1906
Earlier work this paper cites.
S. S. Wilks: ‘The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses’. Annals Math. Statist. 9 (1), p. 60, 1938
1938
Earlier work this paper cites.
A. Wald: ‘Tests of statistical hypotheses concerning several parameters when the number of observations is large’. Transactions of the American Mathematical Society 54 (3), p. 426, 1943
1943
Earlier work this paper cites.
C. Radhakrishna Rao: ‘Information and the accuracy attainable in the estimation of statistical parameters’. Bull. Calcutta Math. Soc. 37, p. 81, 1945
1945
Earlier work this paper cites.
H. Cramér: ‘Mathematical Methods of Statistics’. Princeton University Press, 1946, ISBN 0691080046
1946
Earlier work this paper cites.
B. Efron: ‘Defining the curvature of a statistical problem (with applications to second order efficiency)’. Ann. Statist. 3 (6), p. 1189, 1975
1975
Earlier work this paper cites.
S.-I. Amari: ‘Differential geometry of curved exponential families-curvatures and information loss’. Ann. Statist. 10 (2), p. 357, 1982
1982
Earlier work this paper cites.
D. B. Rubin: ‘Bayesianly justifiable and relevant frequency calculations for the applied statistician’. Ann. Statist. 12 (4), p. 1151, 1984
1984
Earlier work this paper cites.
K. Kondo: ‘Dynamical Likelihood Method for Reconstruction of Events With Missing Momentum. 1: Method and Toy Models’. J. Phys. Soc. Jap. 57, p. 4126, 1988
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} ’. Phys. Rev. D45, p. 2405, 1992
1992
Earlier work this paper cites.
M. Davier, L. Duflot, F. Le Diberder, and A. Rouge: ‘The Optimal method for the measurement of tau polarization’. Phys. Lett. B306, p. 411, 1993
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^{-} ’. Z. Phys. C62, p. 397, 1994
1994
Earlier work this paper cites.
G. Van Rossum and F. L. Drake Jr: ‘Python tutorial’. Centrum voor Wiskunde en Informatica Amsterdam, The Netherlands, 1995
1995
Earlier work this paper cites.
N. Qian: ‘On the momentum term in gradient descent learning algorithms’. Neural Netw. 12 (1), p. 145, 1999
1999
Earlier work this paper cites.
K. S. Cranmer: ‘Kernel estimation in high-energy physics’. Comput. Phys. Commun. 136, p. 198, 2001. arXiv:hep-ex/0011057
2001
Earlier work this paper cites.
M. Dobbs and J. B. Hansen: ‘The HepMC C++ Monte Carlo event record for High Energy Physics’. Comput. Phys. Commun. 134, p. 41, 2001
2001
Earlier work this paper cites.
M. A. Beaumont, W. Zhang, and D. J. Balding: ‘Approximate bayesian computation in population genetics’. Genetics 162 (4), p. 2025, 2002
2002
Earlier work this paper cites.
S. Agostinelli et al. (GEANT4): ‘GEANT4: A Simulation toolkit’. Nucl. Instrum. Meth. A506, p. 250, 2003
2003
Earlier work this paper cites.
V. M. Abazov et al. (D0): ‘A precision measurement of the mass of the top quark’. Nature 429, p. 638, 2004. arXiv:hep-ex/0406031
2004
Earlier work this paper cites.
T. Oliphant: ‘NumPy: A guide to NumPy’. USA: Trelgol Publishing, 2006–. URL http://www.numpy.org/
2006
Earlier work this paper cites.
G. F. Giudice, C. Grojean, A. Pomarol, and R. Rattazzi: ‘The Strongly-Interacting Light Higgs’. JHEP 06, p. 045, 2007. arXiv:hep-ph/0703164
2007
Earlier work this paper cites.
J. D. Hunter: ‘Matplotlib: A 2d graphics environment’. Computing in Science & Engineering 9 (3), p. 90, 2007
2007
Earlier work this paper cites.
P. Artoisenet and O. Mattelaer: ‘MadWeight: Automatic event reweighting with matrix elements’. PoS CHARGED2008, p. 025, 2008
2008
Earlier work this paper cites.
T. Sjostrand, S. Mrenna, and P. Z. Skands: ‘A Brief Introduction to PYTHIA 8.1’. Comput. Phys. Commun. 178, p. 852, 2008. arXiv:0710.3820
Original
2008
Earlier work this paper cites.
Y. Gao, A. V. Gritsan, Z. Guo, K. Melnikov, M. Schulze, and N. V. Tran: ‘Spin determination of single-produced resonances at hadron colliders’. Phys. Rev. D81, p. 075022, 2010. arXiv:1001.3396
Original
2010
Earlier work this paper cites.
The HDF Group: ‘Hierarchical data format version 5’, 2000-2010. URL http://www.hdfgroup.org/HDF5
2010
Earlier work this paper cites.
J. Alwall, A. Freitas, and O. Mattelaer: ‘The Matrix Element Method and QCD Radiation’. Phys. Rev. D83, p. 074010, 2011. arXiv:1010.2263
Original
2011
Earlier work this paper cites.
D. E. Soper and M. Spannowsky: ‘Finding physics signals with shower deconstruction’. Phys. Rev. D84, p. 074002, 2011. arXiv:1102.3480
Original
2011
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, et al.: ‘Scikit-learn: Machine learning in Python’. Journal of Machine Learning Research 12, p. 2825, 2011
2011
Earlier work this paper cites.
K. Cranmer, G. Lewis, L. Moneta, A. Shibata, and W. Verkerke (ROOT): ‘HistFactory: A tool for creating statistical models for use with RooFit and RooStats’ , 2012
2012
Earlier work this paper cites.
S. Bolognesi, Y. Gao, A. V. Gritsan, et al.: ‘On the spin and parity of a single-produced resonance at the LHC’. Phys. Rev. D86, p. 095031, 2012. arXiv:1208.4018
Original
2012
Earlier work this paper cites.

Similar

Y. Fan, D. J. Nott, and S. A. Sisson: ‘Approximate Bayesian Computation via Regression Density Estimation’. ArXiv e-prints , 2012. arXiv:1212.1479
Original
2012
Cited alongside, same era.
R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, R. Pittau, and P. Torrielli: ‘Four-lepton production at hadron colliders: aMC@NLO predictions with theoretical uncertainties’. JHEP 02, p. 099, 2012. arXiv:1110.4738
Original
2012
Cited alongside, same era.
P. Avery et al.: ‘Precision studies of the Higgs boson decay channel H → Z Z → 4 l H\to ZZ\to 4l with MEKD’. Phys. Rev. D87 (5), p. 055006, 2013. arXiv:1210.0896
Original
2013
Cited alongside, same era.
J. R. Andersen, C. Englert, and M. Spannowsky: ‘Extracting precise Higgs couplings by using the matrix element method’. Phys. Rev. D87 (1), p. 015019, 2013. arXiv:1211.3011
Original
2013
Cited alongside, same era.

Then

T. Kluyver, B. Ragan-Kelley, F. Pérez, et al.: ‘Jupyter notebooks - a publishing format for reproducible computational workflows’. In ‘ELPUB’, , 2016
2016
Later among the works it cites.
M. Frate, K. Cranmer, S. Kalia, A. Vandenberg-Rodes, and D. Whiteson: ‘Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes’ , 2017. arXiv:1709.05681
Original
2017
Later among the works it cites.
J. Brehmer, K. Cranmer, F. Kling, and T. Plehn: ‘Better Higgs boson measurements through information geometry’. Phys. Rev. D95 (7), p. 073002, 2017. arXiv:1612.05261
Original
2017
Later among the works it cites.
T. Martini and P. Uwer: ‘The Matrix Element Method at next-to-leading order QCD for hadronic collisions: Single top-quark production at the LHC as an example application’ , 2017. arXiv:1712.04527
Original
2017
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…

J. M. Campbell, R. K. Ellis, W. T. Giele, and C. Williams: ‘Finding the Higgs boson in decays to Z γ Z\gamma using the matrix element method at Next-to-Leading Order’. Phys. Rev. D87 (7), p. 073005, 2013. arXiv:1301.7086

Original

2013

Cited alongside, same era.

P. Artoisenet, P. de Aquino, F. Maltoni, and O. Mattelaer: ‘Unravelling t t ¯ h t\overline{t}h via the Matrix Element Method’. Phys. Rev. Lett. 111 (9), p. 091802, 2013. arXiv:1304.6414

Original

2013

Cited alongside, same era.

J. S. Gainer, J. Lykken, K. T. Matchev, S. Mrenna, and M. Park: ‘The Matrix Element Method: Past, Present, and Future’. In ‘Proceedings, 2013 Community Summer Study on the Future of U.S. Particle Physics: Snowmass on the Mississippi (CSS2013): Minneapolis, MN, USA, July 29-August 6, 2013’, , 2013. arXiv:1307.3546

Original

2013

Cited alongside, same era.

D. E. Soper and M. Spannowsky: ‘Finding top quarks with shower deconstruction’. Phys. Rev. D87, p. 054012, 2013. arXiv:1211.3140

Original

2013

Cited alongside, same era.

G. Cowan, K. Cranmer, E. Gross, and O. Vitells: ‘Asymptotic formulae for likelihood-based tests of new physics’. Eur. Phys. J. C71, p. 1554, 2011. [Erratum: Eur. Phys. J. C73, p. 2501, 2013]. arXiv:1007.1727

Original

2013

Cited alongside, same era.

D. E. Soper and M. Spannowsky: ‘Finding physics signals with event deconstruction’. Phys. Rev. D89 (9), p. 094005, 2014. arXiv:1402.1189

Original

2014

Cited alongside, same era.

L. Dinh, D. Krueger, and Y. Bengio: ‘NICE: Non-linear Independent Components Estimation’. ArXiv e-prints , 2014. arXiv:1410.8516

Original

2014

Cited alongside, same era.

J. Alwall, R. Frederix, S. Frixione, et al.: ‘The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations’. JHEP 07, p. 079, 2014. arXiv:1405.0301

Original

2014

Cited alongside, same era.

M. U. Gutmann, R. Dutta, S. Kaski, and J. Corander: ‘Likelihood-free inference via classification’. Statistics and Computing p. 1–15, 2017

2017