Built on

N. Berger et al. , Simplified Template Cross Sections - Stage 1.1, (2019), arXiv:1906.02754 [hep-ph]
Original
1906
Earlier work this paper cites.
R. Balasubramanian, L. Brenner, C. Burgard, G. Cowan, V. Croft, W. Verkerke, and P. Verschuuren, Statistical method and comparison of different unfolding techniques using RooFit, (2019), arXiv:1910.14654 [physics.data-an]
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, (2019), arXiv:1912.00477 [hep-ph]
Original
1912
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.
A. Hocker and V. Kartvelishvili, SVD approach to data unfolding, Nucl. Instrum. Meth. A372
1996
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T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning , Springer Series in Statistics (Springer New York Inc., New York, NY, USA, 2001)
2001
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G. Cowan, A survey of unfolding methods for particle physics, Conf. Proc. C 0203181
2002
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S. Amoroso et al. , Les Houches 2019: Physics at TeV Colliders: Standard Model Working Group Report, in 11th Les Houches Workshop on Physics at TeV Colliders: PhysTeV Les Houches (2020) arXiv:2003.01700 [hep-ph]
Original
2003
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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, SciPost Phys. 9
Original
2006
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J. D. Hunter, Matplotlib: A 2d graphics environment, Computing in Science & Engineering 9
2007
Earlier work this paper cites.
C. Oleari, The POWHEG-BOX, Nucl. Phys. B Proc. Suppl. 205-206
Original
2010
Earlier work this paper cites.

Similar

V. Blobel, Unfolding Methods in Particle Physics, PHYSTAT2011 Proceedings , 240 (2011)
2011
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 , Proceedings of Machine Learning Research, Vol. 130, edited by A. Banerjee and K. Fukumizu (PMLR, 2021) pp. 2107–2115, arXiv:2011.05836 [stat.ML]
Original
2011
Cited alongside, same era.
G. Choudalakis, Fully bayesian unfolding (2012)
2012
Cited alongside, same era.
M. Sugiyama, T. Suzuki, and T. Kanamori, Density Ratio Estimation in Machine Learning (Cambridge University Press, 2012)
2012
Cited alongside, same era.
V. Blobel, Unfolding, Data Analysis in High Energy Physics , 187 (2013)
2013
Cited alongside, same era.

Then

D. P. Kingma and J. Ba, Adam: A method for stochastic optimization (2017), arXiv:1412.6980 [cs.LG]
Original
2017
Later among the works it cites.
K. Datta, D. Kar, and D. Roy, Unfolding with Generative Adversarial Networks, (2018), arXiv:1806.00433 [physics.data-an]
Original
2018
Later among the works it cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, Pytorch: An imperative style, high-performance deep learning library, in Advances in Neural Information Processing Systems 32 (Curran Associates, Inc., 2019) pp. 8024–8035
2019
Later among the works it cites.
C. R. Harris, K. J. Millman, S. J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith, R. Kern, M. Picus, S. Hoyer, M. H. van Kerkwijk, M. Brett, A. Haldane, J. F. del Río, M. Wiebe, P. Peterson, P. Gérard-Marchant, K. Sheppard, T. Reddy, W. Weckesser, H. Abbasi, C. Gohlke, and T. E. Oliphant, Array programming with NumPy, Nature 585

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2023

Unbinned Profiled Unfolding

Chan, Jay, Nachman, Benjamin

Understand

Unfolding is an important procedure in particle physics experiments which corrects for detector effects and provides differential cross section measurements that can be used for a number of downstream tasks, such as extracting fundamental physics parameters.

Traditionally, unfolding is done by discretizing the target phase space into a finite number of bins and is limited in the number of unfolded variables.
Recently, there have been a number of proposals to perform unbinned unfolding with machine learning.
However, none of these methods (like most unfolding methods) allow for simultaneously constraining (profiling) nuisance parameters.

Built on

N. Berger et al. , Simplified Template Cross Sections - Stage 1.1, (2019), arXiv:1906.02754 [hep-ph]
Original
1906
Earlier work this paper cites.
R. Balasubramanian, L. Brenner, C. Burgard, G. Cowan, V. Croft, W. Verkerke, and P. Verschuuren, Statistical method and comparison of different unfolding techniques using RooFit, (2019), arXiv:1910.14654 [physics.data-an]
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, (2019), arXiv:1912.00477 [hep-ph]
Original
1912
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.
A. Hocker and V. Kartvelishvili, SVD approach to data unfolding, Nucl. Instrum. Meth. A372
1996
Earlier work this paper cites.
T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning , Springer Series in Statistics (Springer New York Inc., New York, NY, USA, 2001)
2001
Earlier work this paper cites.
G. Cowan, A survey of unfolding methods for particle physics, Conf. Proc. C 0203181
2002
Earlier work this paper cites.
S. Amoroso et al. , Les Houches 2019: Physics at TeV Colliders: Standard Model Working Group Report, in 11th Les Houches Workshop on Physics at TeV Colliders: PhysTeV Les Houches (2020) arXiv:2003.01700 [hep-ph]
Original
2003
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, SciPost Phys. 9
Original
2006
Earlier work this paper cites.
J. D. Hunter, Matplotlib: A 2d graphics environment, Computing in Science & Engineering 9
2007
Earlier work this paper cites.
C. Oleari, The POWHEG-BOX, Nucl. Phys. B Proc. Suppl. 205-206
Original
2010
Earlier work this paper cites.

Similar

V. Blobel, Unfolding Methods in Particle Physics, PHYSTAT2011 Proceedings , 240 (2011)
2011
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 , Proceedings of Machine Learning Research, Vol. 130, edited by A. Banerjee and K. Fukumizu (PMLR, 2021) pp. 2107–2115, arXiv:2011.05836 [stat.ML]
Original
2011
Cited alongside, same era.
G. Choudalakis, Fully bayesian unfolding (2012)
2012
Cited alongside, same era.
M. Sugiyama, T. Suzuki, and T. Kanamori, Density Ratio Estimation in Machine Learning (Cambridge University Press, 2012)
2012
Cited alongside, same era.
V. Blobel, Unfolding, Data Analysis in High Energy Physics , 187 (2013)
2013
Cited alongside, same era.

Then

D. P. Kingma and J. Ba, Adam: A method for stochastic optimization (2017), arXiv:1412.6980 [cs.LG]
Original
2017
Later among the works it cites.
K. Datta, D. Kar, and D. Roy, Unfolding with Generative Adversarial Networks, (2018), arXiv:1806.00433 [physics.data-an]
Original
2018
Later among the works it cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, Pytorch: An imperative style, high-performance deep learning library, in Advances in Neural Information Processing Systems 32 (Curran Associates, Inc., 2019) pp. 8024–8035
2019
Later among the works it cites.
C. R. Harris, K. J. Millman, S. J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith, R. Kern, M. Picus, S. Hoyer, M. H. van Kerkwijk, M. Brett, A. Haldane, J. F. del Río, M. Wiebe, P. Peterson, P. Gérard-Marchant, K. Sheppard, T. Reddy, W. Weckesser, H. Abbasi, C. Gohlke, and T. E. Oliphant, Array programming with NumPy, Nature 585

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Open on alphaXiv

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I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, Generative adversarial nets, in Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2 , NIPS’14 (MIT Press, Cambridge, MA, USA, 2014) pp. 2672–2680

2014

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D. P. Kingma and M. Welling, Auto-encoding variational bayes, (2014), arXiv:1312.6114 [stat.ML]

Original

2014

Cited alongside, same era.

D. J. Rezende and S. Mohamed, Variational inference with normalizing flows, International Conference on Machine Learning 37

2015

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T. Sjöstrand, S. Ask, J. R. Christiansen, R. Corke, N. Desai, P. Ilten, S. Mrenna, S. Prestel, C. O. Rasmussen, and P. Z. Skands, An Introduction to PYTHIA 8.2, Comput. Phys. Commun. 191

Original

2015

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D. de Florian et al. (LHC Higgs Cross Section Working Group), Handbook of LHC Higgs Cross Sections: 4. Deciphering the Nature of the Higgs Sector 2/2017

Original

2016

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J. R. Andersen et al. , Les Houches 2015: Physics at TeV Colliders Standard Model Working Group Report, in 9th Les Houches Workshop on Physics at TeV Colliders (2016) arXiv:1605.04692 [hep-ph]

Original

2016

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T. Kluyver, B. Ragan-Kelley, F. Pérez, B. Granger, M. Bussonnier, J. Frederic, K. Kelley, J. Hamrick, J. Grout, S. Corlay, P. Ivanov, D. Avila, S. Abdalla, and C. Willing, Jupyter notebooks – a publishing format for reproducible computational workflows, in Positioning and Power in Academic Publishing: Players, Agents and Agendas , edited by F. Loizides and B. Schmidt (IOS Press, 2016) pp. 87 – 90

2016

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2020