Built on

P. Azzi et al., Report from Working Group 1: Standard Model Physics at the HL-LHC and HE-LHC , CERN Yellow Rep. Monogr. 7
Original
1902
Earlier work this paper cites.
M. Cepeda et al., Report from Working Group 2: Higgs Physics at the HL-LHC and HE-LHC , CERN Yellow Rep. Monogr. 7
Original
1902
Earlier work this paper cites.
B. Nachman, A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty , 1909.03081
Original
1909
Earlier work this paper cites.
S. Bailey and L. Harland-Lang, Differential Top Quark Pair Production at the LHC: Challenges for PDF Fits , Eur. Phys. J. C 80
Original
1909
Earlier work this paper cites.
I. Brivio, S. Bruggisser, F. Maltoni, R. Moutafis, T. Plehn, E. Vryonidou et al., O new physics, where art thou? A global search in the top sector , JHEP 02
Original
1910
Earlier work this paper cites.
R. Balasubramanian, L. Brenner, C. Burgard, G. Cowan, V. Croft, W. Verkerke et al., Statistical method and comparison of different unfolding techniques using RooFit , 1910.14654
Original
1910
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 , Phys. Rev. Lett. 124
Original
1911
Earlier work this paper cites.
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn and R. Winterhalder, How to GAN away Detector Effects , 1912.00477
Original
1912
Earlier work this paper cites.
R. T. D’Agnolo, G. Grosso, M. Pierini, A. Wulzer and M. Zanetti, Learning multivariate new physics , Eur. Phys. J. C 81
Original
1912
Earlier work this paper cites.
T.-J. Hou et al., New CTEQ global analysis of quantum chromodynamics with high-precision data from the LHC , Phys. Rev. D 103
Original
1912
Earlier work this paper cites.
A. Butter, T. Plehn and R. Winterhalder, How to GAN Event Subtraction , 1912.08824
Original
1912
Earlier work this paper cites.
W. H. Richardson, Bayesian-based iterative method of image restoration ∗ \ast , J. Opt. Soc. Am. 62
1972
Earlier work this paper cites.
L. B. Lucy, An iterative technique for the rectification of observed distributions , Astronomical Journal 79
1974
Earlier work this paper cites.
L. Lindemann and G. Zech, Unfolding by weighting Monte Carlo events , Nucl. Instrum. Meth. A354
1995
Earlier work this paper cites.
G. D’Agostini, A multidimensional unfolding method based on bayes’ theorem , Nucl. Instrum. Meth. A362
1995
Earlier work this paper cites.
Springer Series in Statistics. Springer New York Inc., New York, NY, USA, 2001
T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning · 2001
Earlier work this paper cites.
G. Cowan, A survey of unfolding methods for particle physics , Conf. Proc. C0203181
2002
Earlier work this paper cites.
G. Zech and B. Aslan, Binning-Free Unfolding Based on Monte Carlo Migration , eConf C030908
2003
Earlier work this paper cites.
J. R. Andersen, C. Gütschow, A. Maier and S. Prestel, A Positive Resampler for Monte Carlo events with negative weights , Eur. Phys. J. C 80
Original
2005
Earlier work this paper cites.
R. Abdul Khalek, J. J. Ethier, J. Rojo and G. van Weelden, nNNPDF2.0: quark flavor separation in nuclei from LHC data , JHEP 09
Original
2006
Earlier work this paper cites.
N. D. Gagunashvili, Machine learning approach to inverse problem and unfolding procedure , 1004.2006
Original
2006
Earlier work this paper cites.
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, A. Rousselot, R. Winterhalder et al., Invertible Networks or Partons to Detector and Back Again , SciPost Phys. 9
Original
2006
Earlier work this paper cites.
T. Sjöstrand, S. Mrenna and P. Z. Skands, PYTHIA 6.4 Physics and Manual , JHEP 05
2006
Earlier work this paper cites.
M. Cacciari and G. P. Salam, Dispelling the N 3 N^{3} myth for the k t k_{t} jet-finder , Phys. Lett. B641
2006
Earlier work this paper cites.
S. Dawson, S. Homiller and S. D. Lane, Putting standard model EFT fits to work , Phys. Rev. D 102
Original
2007
Earlier work this paper cites.
S. Chen, A. Glioti, G. Panico and A. Wulzer, Parametrized classifiers for optimal EFT sensitivity , JHEP 05
Original
2007
Earlier work this paper cites.
M. Arratia, Z.-B. Kang, A. Prokudin and F. Ringer, Jet-based measurements of Sivers and Collins asymmetries at the future electron-ion collider , Phys. Rev. D 102
Original
2007
Earlier work this paper cites.
B. Nachman and J. Thaler, A Neural Resampler for Monte Carlo Reweighting with Preserved Uncertainties , 2007.11586
Original
2007
Earlier work this paper cites.

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T. Sjöstrand, S. Mrenna and P. Z. Skands, A Brief Introduction to PYTHIA 8.1 , Comput. Phys. Commun. 178
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2008
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2008
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M. Cacciari, G. P. Salam and G. Soyez, The anti- k t k_{t} jet clustering algorithm , JHEP 04
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Then

D. Maitre, G. Heinrich and M. Johnson, N(N)LO event files: applications and prospects , PoS LL2016
Original
2016
Later among the works it cites.
S. Mrenna and P. Skands, Automated Parton-Shower Variations in Pythia 8 , Phys. Rev. D 94
Original
2016
Later among the works it cites.
J. Bellm, S. Plätzer, P. Richardson, A. Siódmok and S. Webster, Reweighting Parton Showers , Phys. Rev. D 94
Original
2016
Later among the works it cites.
E. Bothmann, M. Schönherr and S. Schumann, Reweighting QCD matrix-element and parton-shower calculations , Eur. Phys. J. C 76
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2016
Later among the works it cites.
J. Bellm et al., Herwig 7.0/Herwig++ 3.0 release note , Eur. Phys. J. C76

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2021

Presenting Unbinned Differential Cross Section Results

Arratia, Miguel, Butter, Anja, Campanelli, Mario et al.

Understand

Machine learning tools have empowered a qualitatively new way to perform differential cross section measurements whereby the data are unbinned, possibly in many dimensions.

Unbinned measurements can enable, improve, or at least simplify comparisons between experiments and with theoretical predictions.
Furthermore, many-dimensional measurements can be used to define observables after the measurement instead of before.
There is currently no community standard for publishing unbinned data.

Built on

P. Azzi et al., Report from Working Group 1: Standard Model Physics at the HL-LHC and HE-LHC , CERN Yellow Rep. Monogr. 7
Original
1902
Earlier work this paper cites.
M. Cepeda et al., Report from Working Group 2: Higgs Physics at the HL-LHC and HE-LHC , CERN Yellow Rep. Monogr. 7
Original
1902
Earlier work this paper cites.
B. Nachman, A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty , 1909.03081
Original
1909
Earlier work this paper cites.
S. Bailey and L. Harland-Lang, Differential Top Quark Pair Production at the LHC: Challenges for PDF Fits , Eur. Phys. J. C 80
Original
1909
Earlier work this paper cites.
I. Brivio, S. Bruggisser, F. Maltoni, R. Moutafis, T. Plehn, E. Vryonidou et al., O new physics, where art thou? A global search in the top sector , JHEP 02
Original
1910
Earlier work this paper cites.
R. Balasubramanian, L. Brenner, C. Burgard, G. Cowan, V. Croft, W. Verkerke et al., Statistical method and comparison of different unfolding techniques using RooFit , 1910.14654
Original
1910
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 , Phys. Rev. Lett. 124
Original
1911
Earlier work this paper cites.
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn and R. Winterhalder, How to GAN away Detector Effects , 1912.00477
Original
1912
Earlier work this paper cites.
R. T. D’Agnolo, G. Grosso, M. Pierini, A. Wulzer and M. Zanetti, Learning multivariate new physics , Eur. Phys. J. C 81
Original
1912
Earlier work this paper cites.
T.-J. Hou et al., New CTEQ global analysis of quantum chromodynamics with high-precision data from the LHC , Phys. Rev. D 103
Original
1912
Earlier work this paper cites.
A. Butter, T. Plehn and R. Winterhalder, How to GAN Event Subtraction , 1912.08824
Original
1912
Earlier work this paper cites.
W. H. Richardson, Bayesian-based iterative method of image restoration ∗ \ast , J. Opt. Soc. Am. 62
1972
Earlier work this paper cites.
L. B. Lucy, An iterative technique for the rectification of observed distributions , Astronomical Journal 79
1974
Earlier work this paper cites.
L. Lindemann and G. Zech, Unfolding by weighting Monte Carlo events , Nucl. Instrum. Meth. A354
1995
Earlier work this paper cites.
G. D’Agostini, A multidimensional unfolding method based on bayes’ theorem , Nucl. Instrum. Meth. A362
1995
Earlier work this paper cites.
Springer Series in Statistics. Springer New York Inc., New York, NY, USA, 2001
T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning · 2001
Earlier work this paper cites.
G. Cowan, A survey of unfolding methods for particle physics , Conf. Proc. C0203181
2002
Earlier work this paper cites.
G. Zech and B. Aslan, Binning-Free Unfolding Based on Monte Carlo Migration , eConf C030908
2003
Earlier work this paper cites.
J. R. Andersen, C. Gütschow, A. Maier and S. Prestel, A Positive Resampler for Monte Carlo events with negative weights , Eur. Phys. J. C 80
Original
2005
Earlier work this paper cites.
R. Abdul Khalek, J. J. Ethier, J. Rojo and G. van Weelden, nNNPDF2.0: quark flavor separation in nuclei from LHC data , JHEP 09
Original
2006
Earlier work this paper cites.
N. D. Gagunashvili, Machine learning approach to inverse problem and unfolding procedure , 1004.2006
Original
2006
Earlier work this paper cites.
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, A. Rousselot, R. Winterhalder et al., Invertible Networks or Partons to Detector and Back Again , SciPost Phys. 9
Original
2006
Earlier work this paper cites.
T. Sjöstrand, S. Mrenna and P. Z. Skands, PYTHIA 6.4 Physics and Manual , JHEP 05
2006
Earlier work this paper cites.
M. Cacciari and G. P. Salam, Dispelling the N 3 N^{3} myth for the k t k_{t} jet-finder , Phys. Lett. B641
2006
Earlier work this paper cites.
S. Dawson, S. Homiller and S. D. Lane, Putting standard model EFT fits to work , Phys. Rev. D 102
Original
2007
Earlier work this paper cites.
S. Chen, A. Glioti, G. Panico and A. Wulzer, Parametrized classifiers for optimal EFT sensitivity , JHEP 05
Original
2007
Earlier work this paper cites.
M. Arratia, Z.-B. Kang, A. Prokudin and F. Ringer, Jet-based measurements of Sivers and Collins asymmetries at the future electron-ion collider , Phys. Rev. D 102
Original
2007
Earlier work this paper cites.
B. Nachman and J. Thaler, A Neural Resampler for Monte Carlo Reweighting with Preserved Uncertainties , 2007.11586
Original
2007
Earlier work this paper cites.

Similar

T. Sjöstrand, S. Mrenna and P. Z. Skands, A Brief Introduction to PYTHIA 8.1 , Comput. Phys. Commun. 178
Original
2008
Cited alongside, same era.
M. Bahr et al., Herwig++ Physics and Manual , Eur. Phys. J. C58
Original
2008
Cited alongside, same era.
M. Cacciari, G. P. Salam and G. Soyez, The anti- k t k_{t} jet clustering algorithm , JHEP 04
Original
2008
Cited alongside, same era.
A. Buckley, H. Hoeth, H. Lacker, H. Schulz and J. E. von Seggern, Systematic event generator tuning for the LHC , Eur. Phys. J. C 65
Original
2010
Cited alongside, same era.
V. Blobel, Unfolding Methods in Particle Physics , PHYSTAT2011 Proceedings (2011) 240
2011

Then

D. Maitre, G. Heinrich and M. Johnson, N(N)LO event files: applications and prospects , PoS LL2016
Original
2016
Later among the works it cites.
S. Mrenna and P. Skands, Automated Parton-Shower Variations in Pythia 8 , Phys. Rev. D 94
Original
2016
Later among the works it cites.
J. Bellm, S. Plätzer, P. Richardson, A. Siódmok and S. Webster, Reweighting Parton Showers , Phys. Rev. D 94
Original
2016
Later among the works it cites.
E. Bothmann, M. Schönherr and S. Schumann, Reweighting QCD matrix-element and parton-shower calculations , Eur. Phys. J. C 76
Original
2016
Later among the works it cites.
J. Bellm et al., Herwig 7.0/Herwig++ 3.0 release note , Eur. Phys. J. C76

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

Cited alongside, same era.

M. Vandegar, M. Kagan, A. Wehenkel and G. Louppe, Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference , in Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (A. Banerjee and K. Fukumizu, eds.), vol. 130 of Proceedings of Machine Learning Research , pp. 2107–2115, PMLR, 11, 2021, 2011.05836 , https://proceedings.mlr.press/v130/vandegar21a.html

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2011

Cited alongside, same era.

B. Stienen and R. Verheyen, Phase space sampling and inference from weighted events with autoregressive flows , SciPost Phys. 10

Original

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Cited alongside, same era.

T. Adye, Unfolding algorithms and tests using roounfold , 2011

2011

Cited alongside, same era.

J. Thaler and K. Van Tilburg, Identifying Boosted Objects with N-subjettiness , JHEP 03

Original

2011

Cited alongside, same era.

J. Ellis, M. Madigan, K. Mimasu, V. Sanz and T. You, Top, Higgs, Diboson and Electroweak Fit to the Standard Model Effective Field Theory , JHEP 04

Original

2012

Cited alongside, same era.

R. D. Ball, V. Bertone, F. Cerutti, L. Del Debbio, S. Forte, A. Guffanti et al., Reweighting and Unweighting of Parton Distributions and the LHC W lepton asymmetry data , Nucl. Phys. B 855

Original

2012

Cited alongside, same era.

Cambridge University Press, 2012, 10.1017/CBO9781139035613

M. Sugiyama, T. Suzuki and T. Kanamori, Density Ratio Estimation in Machine Learning · 2012

Cited alongside, same era.

Original

2016