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A. Butter et al., The Machine Learning Landscape of Top Taggers , SciPost Phys. 7
Original
1902
Earlier work this paper cites.
G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld, N. Tishby et al., Machine learning and the physical sciences , Rev. Mod. Phys. 91
Original
1903
Earlier work this paper cites.
J. Alsing and B. Wandelt, Nuisance hardened data compression for fast likelihood-free inference , Mon. Not. Roy. Astron. Soc. 488
Original
1903
Earlier work this paper cites.
S. Wunsch, S. Jörger, R. Wolf and G. Quast, Reducing the dependence of the neural network function to systematic uncertainties in the input space , Comput. Softw. Big Sci. 4
Original
1907
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.
L. Bradshaw, R. K. Mishra, A. Mitridate and B. Ostdiek, Mass Agnostic Jet Taggers , SciPost Phys. 8
Original
1908
Earlier work this paper cites.
B. Nachman, A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty , SciPost Phys. 8
Original
1909
Earlier work this paper cites.
D. Bourilkov, Machine and Deep Learning Applications in Particle Physics , Int. J. Mod. Phys. A 34
Original
1912
Earlier work this paper cites.
C. Collaboration, A deep neural network to search for new long-lived particles decaying to jets , Machine Learning: Science and Technology (2020) , [ 1912.12238 ]
Original
1912
Earlier work this paper cites.
B. Andersson, G. Gustafson, G. Ingelman and T. Sjostrand, Parton Fragmentation and String Dynamics , Phys. Rept. 97
1983
Earlier work this paper cites.
T. Sjostrand, Jet Fragmentation of Nearby Partons , Nucl. Phys. B 248
1984
Earlier work this paper cites.
B. R. Webber, A QCD Model for Jet Fragmentation Including Soft Gluon Interference , Nucl. Phys. B 238
1984
Earlier work this paper cites.
G. Kasieczka and D. Shih, Robust Jet Classifiers through Distance Correlation , Phys. Rev. Lett. 125
Original
2001
Earlier work this paper cites.
S. Wunsch, S. Jörger, R. Wolf and G. Quast, Optimal statistical inference in the presence of systematic uncertainties using neural network optimization based on binned Poisson likelihoods with nuisance parameters , Comput. Softw. Big Sci. 5
Original
2003
Earlier work this paper cites.
J.-C. Winter, F. Krauss and G. Soff, A Modified cluster hadronization model , Eur. Phys. J. C 36
2004
Earlier work this paper cites.
J. M. Clavijo, P. Glaysher and J. M. Katzy, Adversarial domain adaptation to reduce sample bias of a high energy physics classifier , 2005.00568
Original
2005
Earlier work this paper cites.
T. Sjostrand, 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.
G. Kasieczka, B. Nachman, M. D. Schwartz and D. Shih, Automating the ABCD method with machine learning , Phys. Rev. D 103
Original
2007
Earlier work this paper cites.
G. J. Székely, M. L. Rizzo and N. K. Bakirov, Measuring and testing dependence by correlation of distances , Ann. Statist. 35
2007
Earlier work this paper cites.
T. Sjostrand, S. Mrenna and P. Z. Skands, A Brief Introduction to PYTHIA 8.1 , Comput. Phys. Commun. 178
Original
2008
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.
M. Cacciari, G. P. Salam and G. Soyez, The anti- k t k_{t} jet clustering algorithm , JHEP 04
Original
2008
Earlier work this paper cites.
J. M. Butterworth, A. R. Davison, M. Rubin and G. P. Salam, Jet substructure as a new Higgs search channel at the LHC , Phys. Rev. Lett. 100
Original
2008
Earlier work this paper cites.

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G. J. Székely and M. L. Rizzo, Brownian distance covariance , Ann. Appl. Stat. 3
2009
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Original

Then

J. Dolen, P. Harris, S. Marzani, S. Rappoccio and N. Tran, Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure , JHEP 05
Original
2016
Later among the works it cites.
J. Bellm et al., Herwig 7.0/Herwig++ 3.0 release note , Eur. Phys. J. C 76
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2016
Later among the works it cites.
L. de Oliveira, M. Kagan, L. Mackey, B. Nachman and A. Schwartzman, Jet-Images – Deep Learning Edition. , JHEP 07
Original
2016
Later among the works it cites.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean et al., Tensorflow: A system for large-scale machine learning. , in OSDI , vol. 16, pp. 265–283, 2016
2016
Later among the works it cites.
G. Louppe, M. Kagan and K. Cranmer, Learning to Pivot with Adversarial Networks , Advances in Neural Information Processing Systems 30

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2021

A Cautionary Tale of Decorrelating Theory Uncertainties

Ghosh, Aishik, Nachman, Benjamin

Understand

A variety of techniques have been proposed to train machine learning classifiers that are independent of a given feature.

While this can be an essential technique for enabling background estimation, it may also be useful for reducing uncertainties.
We carefully examine theory uncertainties, which typically do not have a statistical origin.
We will provide explicit examples of two-point (fragmentation modeling) and continuous (higher-order corrections) uncertainties where decorrelating significantly reduces the apparent uncertainty while the actual uncertainty is much larger.

Built on

A. Butter et al., The Machine Learning Landscape of Top Taggers , SciPost Phys. 7
Original
1902
Earlier work this paper cites.
G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld, N. Tishby et al., Machine learning and the physical sciences , Rev. Mod. Phys. 91
Original
1903
Earlier work this paper cites.
J. Alsing and B. Wandelt, Nuisance hardened data compression for fast likelihood-free inference , Mon. Not. Roy. Astron. Soc. 488
Original
1903
Earlier work this paper cites.
S. Wunsch, S. Jörger, R. Wolf and G. Quast, Reducing the dependence of the neural network function to systematic uncertainties in the input space , Comput. Softw. Big Sci. 4
Original
1907
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.
L. Bradshaw, R. K. Mishra, A. Mitridate and B. Ostdiek, Mass Agnostic Jet Taggers , SciPost Phys. 8
Original
1908
Earlier work this paper cites.
B. Nachman, A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty , SciPost Phys. 8
Original
1909
Earlier work this paper cites.
D. Bourilkov, Machine and Deep Learning Applications in Particle Physics , Int. J. Mod. Phys. A 34
Original
1912
Earlier work this paper cites.
C. Collaboration, A deep neural network to search for new long-lived particles decaying to jets , Machine Learning: Science and Technology (2020) , [ 1912.12238 ]
Original
1912
Earlier work this paper cites.
B. Andersson, G. Gustafson, G. Ingelman and T. Sjostrand, Parton Fragmentation and String Dynamics , Phys. Rept. 97
1983
Earlier work this paper cites.
T. Sjostrand, Jet Fragmentation of Nearby Partons , Nucl. Phys. B 248
1984
Earlier work this paper cites.
B. R. Webber, A QCD Model for Jet Fragmentation Including Soft Gluon Interference , Nucl. Phys. B 238
1984
Earlier work this paper cites.
G. Kasieczka and D. Shih, Robust Jet Classifiers through Distance Correlation , Phys. Rev. Lett. 125
Original
2001
Earlier work this paper cites.
S. Wunsch, S. Jörger, R. Wolf and G. Quast, Optimal statistical inference in the presence of systematic uncertainties using neural network optimization based on binned Poisson likelihoods with nuisance parameters , Comput. Softw. Big Sci. 5
Original
2003
Earlier work this paper cites.
J.-C. Winter, F. Krauss and G. Soff, A Modified cluster hadronization model , Eur. Phys. J. C 36
2004
Earlier work this paper cites.
J. M. Clavijo, P. Glaysher and J. M. Katzy, Adversarial domain adaptation to reduce sample bias of a high energy physics classifier , 2005.00568
Original
2005
Earlier work this paper cites.
T. Sjostrand, 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.
G. Kasieczka, B. Nachman, M. D. Schwartz and D. Shih, Automating the ABCD method with machine learning , Phys. Rev. D 103
Original
2007
Earlier work this paper cites.
G. J. Székely, M. L. Rizzo and N. K. Bakirov, Measuring and testing dependence by correlation of distances , Ann. Statist. 35
2007
Earlier work this paper cites.
T. Sjostrand, S. Mrenna and P. Z. Skands, A Brief Introduction to PYTHIA 8.1 , Comput. Phys. Commun. 178
Original
2008
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.
M. Cacciari, G. P. Salam and G. Soyez, The anti- k t k_{t} jet clustering algorithm , JHEP 04
Original
2008
Earlier work this paper cites.
J. M. Butterworth, A. R. Davison, M. Rubin and G. P. Salam, Jet substructure as a new Higgs search channel at the LHC , Phys. Rev. Lett. 100
Original
2008
Earlier work this paper cites.

Similar

G. J. Székely and M. L. Rizzo, Brownian distance covariance , Ann. Appl. Stat. 3
2009
Cited alongside, same era.
T. Gleisberg, S. Hoeche, F. Krauss, M. Schonherr, S. Schumann, F. Siegert et al., Event generation with SHERPA 1.1 , JHEP 02
Original
2009
Cited alongside, same era.
O. Kitouni, B. Nachman, C. Weisser and M. Williams, Enhancing searches for resonances with machine learning and moment decomposition , JHEP 21
Original
2010
Cited alongside, same era.
S. D. Ellis, C. K. Vermilion and J. R. Walsh, Recombination Algorithms and Jet Substructure: Pruning as a Tool for Heavy Particle Searches , Phys. Rev. D 81
Original
2010
Cited alongside, same era.
D. Krohn, J. Thaler and L.-T. Wang, Jet Trimming , JHEP 02
Original

Then

J. Dolen, P. Harris, S. Marzani, S. Rappoccio and N. Tran, Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure , JHEP 05
Original
2016
Later among the works it cites.
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.
L. de Oliveira, M. Kagan, L. Mackey, B. Nachman and A. Schwartzman, Jet-Images – Deep Learning Edition. , JHEP 07
Original
2016
Later among the works it cites.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean et al., Tensorflow: A system for large-scale machine learning. , in OSDI , vol. 16, pp. 265–283, 2016
2016
Later among the works it cites.
G. Louppe, M. Kagan and K. Cranmer, Learning to Pivot with Adversarial Networks , Advances in Neural Information Processing Systems 30

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

2010

Cited alongside, same era.

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

Original

2011

Cited alongside, same era.

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

Original

2012

Cited alongside, same era.

J. Thaler and K. Van Tilburg, Maximizing Boosted Top Identification by Minimizing N-subjettiness , JHEP 02

Original

2012

Cited alongside, same era.

J. Stevens and M. Williams, uBoost: A boosting method for producing uniform selection efficiencies from multivariate classifiers , JINST 8

Original

2013

Cited alongside, same era.

G. J. Székely and M. L. Rizzo, The distance correlation t-test of independence in high dimension , J. Multivar. Anal. 117

2013

Cited alongside, same era.

R. D. Ball et al., Parton distributions with LHC data , Nucl. Phys. B 867

Original

2013

Cited alongside, same era.

A. J. Larkoski, G. P. Salam and J. Thaler, Energy Correlation Functions for Jet Substructure , JHEP 06

Original

2013

Cited alongside, same era.

Original

2017