Built on

P. T. Komiske, E. M. Metodiev, and J. Thaler, Metric Space of Collider Events, Phys. Rev. Lett. 123
Original
1902
Earlier work this paper cites.
E. Bothmann et al. (Sherpa), Event Generation with Sherpa 2.2, SciPost Phys. 7
Original
1905
Earlier work this paper cites.
J. Brehmer, F. Kling, I. Espejo, and K. Cranmer, MadMiner: Machine learning-based inference for particle physics, Comput. Softw. Big Sci. 4
Original
1907
Earlier work this paper cites.
A. Andreassen and B. Nachman, Neural Networks for Full Phase-space Reweighting and Parameter Tuning, Phys. Rev. D 101
Original
1907
Earlier work this paper cites.
P. T. Komiske, R. Mastandrea, E. M. Metodiev, P. Naik, and J. Thaler, Exploring the Space of Jets with CMS Open Data, Phys. Rev. D 101
Original
1908
Earlier work this paper cites.
K. Cranmer, J. Brehmer, and G. Louppe, The frontier of simulation-based inference, Proc. Nat. Acad. Sci. 117
Original
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, Phys. Rev. Lett. 124
Original
1911
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.
A. Andreassen, B. Nachman, and D. Shih, Simulation Assisted Likelihood-free Anomaly Detection, Phys. Rev. D 101
Original
2001
Earlier work this paper cites.
J. Hollingsworth and D. Whiteson, Resonance Searches with Machine Learned Likelihood Ratios, (2020), arXiv:2002.04699 [hep-ph]
Original
2002
Earlier work this paper cites.
S. Agostinelli et al. (GEANT4), GEANT4–a simulation toolkit, Nucl. Instrum. Meth. A 506
2003
Earlier work this paper cites.
C. Badiali, F. Di Bello, G. Frattari, E. Gross, V. Ippolito, M. Kado, and J. Shlomi, Efficiency Parameterization with Neural Networks, Comput. Softw. Big Sci. 5
Original
2004
Earlier work this paper cites.
M. Crispim Romão, N. F. Castro, J. G. Milhano, R. Pedro, and T. Vale, Use of a generalized energy Mover’s distance in the search for rare phenomena at colliders, Eur. Phys. J. C 81
Original
2004
Earlier work this paper cites.
C. Cesarotti and J. Thaler, A Robust Measure of Event Isotropy at Colliders, JHEP 08
Original
2004
Earlier work this paper cites.
J. Allison et al. , Geant4 developments and applications, IEEE Transactions on Nuclear Science 53
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.
B. Nachman and J. Thaler, Neural resampler for Monte Carlo reweighting with preserved uncertainties, Phys. Rev. D 102
Original
2007
Earlier work this paper cites.
J. D. Hunter, Matplotlib: A 2d graphics environment, Computing in Science & Engineering 9
2007
Earlier work this paper cites.
M. Bahr et al. , Herwig++ Physics and Manual, Eur. Phys. J. C 58
Original
2008
Earlier work this paper cites.

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A. Butter and T. Plehn, Generative Networks for LHC events, (2020), arXiv:2008.08558 [hep-ph]
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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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Then

J. Bellm et al. , Herwig 7.0/Herwig++ 3.0 release note, Eur. Phys. J. C 76
Original
2016
Later among the works it cites.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, et al. , Tensorflow: A system for large-scale machine learning., in OSDI , Vol. 16 (2016) pp. 265–283
2016
Later among the works it cites.
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
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Later among the works it cites.
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Later among the works it cites.

Beyond the bibliography

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Reading the bibliography…

2021

Neural Conditional Reweighting

Nachman, Benjamin, Thaler, Jesse

Understand

There is a growing use of neural network classifiers as unbinned, high-dimensional (and variable-dimensional) reweighting functions.

To date, the focus has been on marginal reweighting, where a subset of features are used for reweighting while all other features are integrated over.
There are some situations, though, where it is preferable to condition on auxiliary features instead of marginalizing over them.
In this paper, we introduce neural conditional reweighting, which extends neural marginal reweighting to the conditional case.

Built on

P. T. Komiske, E. M. Metodiev, and J. Thaler, Metric Space of Collider Events, Phys. Rev. Lett. 123
Original
1902
Earlier work this paper cites.
E. Bothmann et al. (Sherpa), Event Generation with Sherpa 2.2, SciPost Phys. 7
Original
1905
Earlier work this paper cites.
J. Brehmer, F. Kling, I. Espejo, and K. Cranmer, MadMiner: Machine learning-based inference for particle physics, Comput. Softw. Big Sci. 4
Original
1907
Earlier work this paper cites.
A. Andreassen and B. Nachman, Neural Networks for Full Phase-space Reweighting and Parameter Tuning, Phys. Rev. D 101
Original
1907
Earlier work this paper cites.
P. T. Komiske, R. Mastandrea, E. M. Metodiev, P. Naik, and J. Thaler, Exploring the Space of Jets with CMS Open Data, Phys. Rev. D 101
Original
1908
Earlier work this paper cites.
K. Cranmer, J. Brehmer, and G. Louppe, The frontier of simulation-based inference, Proc. Nat. Acad. Sci. 117
Original
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, Phys. Rev. Lett. 124
Original
1911
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.
A. Andreassen, B. Nachman, and D. Shih, Simulation Assisted Likelihood-free Anomaly Detection, Phys. Rev. D 101
Original
2001
Earlier work this paper cites.
J. Hollingsworth and D. Whiteson, Resonance Searches with Machine Learned Likelihood Ratios, (2020), arXiv:2002.04699 [hep-ph]
Original
2002
Earlier work this paper cites.
S. Agostinelli et al. (GEANT4), GEANT4–a simulation toolkit, Nucl. Instrum. Meth. A 506
2003
Earlier work this paper cites.
C. Badiali, F. Di Bello, G. Frattari, E. Gross, V. Ippolito, M. Kado, and J. Shlomi, Efficiency Parameterization with Neural Networks, Comput. Softw. Big Sci. 5
Original
2004
Earlier work this paper cites.
M. Crispim Romão, N. F. Castro, J. G. Milhano, R. Pedro, and T. Vale, Use of a generalized energy Mover’s distance in the search for rare phenomena at colliders, Eur. Phys. J. C 81
Original
2004
Earlier work this paper cites.
C. Cesarotti and J. Thaler, A Robust Measure of Event Isotropy at Colliders, JHEP 08
Original
2004
Earlier work this paper cites.
J. Allison et al. , Geant4 developments and applications, IEEE Transactions on Nuclear Science 53
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.
B. Nachman and J. Thaler, Neural resampler for Monte Carlo reweighting with preserved uncertainties, Phys. Rev. D 102
Original
2007
Earlier work this paper cites.
J. D. Hunter, Matplotlib: A 2d graphics environment, Computing in Science & Engineering 9
2007
Earlier work this paper cites.
M. Bahr et al. , Herwig++ Physics and Manual, Eur. Phys. J. C 58
Original
2008
Earlier work this paper cites.

Similar

A. Butter and T. Plehn, Generative Networks for LHC events, (2020), arXiv:2008.08558 [hep-ph]
Original
2008
Cited alongside, same era.
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.
T. Cai, J. Cheng, N. Craig, and K. Craig, Linearized optimal transport for collider events, Phys. Rev. D 102
Original
2008
Cited alongside, same era.
A. Andreassen, S. Hsu, B. Nachman, N. Suaysom, A. Suresh, Parameter Estimation using Neural Networks in the Presence of Detector Effects, Phys. Rev. D 103
Original
2010
Cited alongside, same era.
CMS Collaboration, Simulated dataset QCD_Pt-1000to1400_TuneZ2_7TeV_pythia6 in AODSIM format for 2011 collision data (SM Exclusive), CERN Open Data Portal 10.7483/OPENDATA.CMS.96U2.3YAH (2016a)

Then

J. Bellm et al. , Herwig 7.0/Herwig++ 3.0 release note, Eur. Phys. J. C 76
Original
2016
Later among the works it cites.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, et al. , Tensorflow: A system for large-scale machine learning., in OSDI , Vol. 16 (2016) pp. 265–283
2016
Later among the works it cites.
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
Later among the works it cites.
F. Chollet, Keras, https://github.com/fchollet/keras (2017)
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…

2011

Cited alongside, same era.

CMS Collaboration, Simulated dataset QCD_Pt-1400to1800_TuneZ2_7TeV_pythia6 in AODSIM format for 2011 collision data (SM Exclusive), CERN Open Data Portal 10.7483/OPENDATA.CMS.RC9V.B5KX (2016b)

2011

Cited alongside, same era.

CMS Collaboration, Simulated dataset QCD_Pt-1800_TuneZ2_7TeV_pythia6 in AODSIM format for 2011 collision data (SM Exclusive), CERN Open Data Portal 10.7483/OPENDATA.CMS.CX2X.J3KW (2016c)

2011

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C. Cesarotti, M. Reece, and M. J. Strassler, The Efficacy of Event Isotropy as an Event Shape Observable, (2020), arXiv:2011.06599 [hep-ph]

Original

2011

Cited alongside, same era.

M. Sugiyama, T. Suzuki, and T. Kanamori, Density Ratio Estimation in Machine Learning (Cambridge University Press, 2012)

2012

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M. Cacciari, G. P. Salam, and G. Soyez, FastJet User Manual, Eur. Phys. J. C72

Original

2012

Cited alongside, same era.

T. Böhlen, F. Cerutti, M. Chin, A. Fassò, A. Ferrari, P. Ortega, A. Mairani, P. Sala, G. Smirnov, and V. Vlachoudis, The fluka code: Developments and challenges for high energy and medical applications, Nuclear Data Sheets 120

2014

Cited alongside, same era.

M. Selvaggi, DELPHES 3: A modular framework for fast-simulation of generic collider experiments, Proceedings, 15th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2013): Beijing, China, May 16-21, 2013 , J. Phys. Conf. Ser. 523

2014

Cited alongside, same era.

D. Reichelt, P. Richardson, and A. Siodmok, Improving the Simulation of Quark and Gluon Jets with Herwig 7, Eur. Phys. J. C 77

Original

2017