publication . Article . Preprint . Other literature type . 2017

Weakly supervised classification in high energy physics

Dery, Lucio Mwinmaarong; Nachman, Benjamin; Rubbo, Francesco; Schwartzman, Ariel;
Open Access
  • Published: 29 May 2017
  • Publisher: eScholarship, University of California
  • Country: United States
Abstract
As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. This paper presents a new approach called weakly supervised classification in which class proportions are the only input into the machine learning algorithm. Using one of the most challenging binary classification tasks in high energy physics - quark versus gluon tagging - we show that weakly supervised classification can match the performance of fully supervised algorithms. Furthermore, by design, the new algorithm is insensitive to any mis-modeling of discriminating features in the data by the simulation. Wea...
Subjects
arXiv: Computer Science::Machine Learning
ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITION
free text keywords: Nuclear & Particles Physics, Mathematical Sciences, Physical Sciences, Nuclear and High Energy Physics, Particle physics, Quark, Binary classification, Gluon, Physics, Robustness (computer science), Exploit, High Energy Physics - Phenomenology, Physics - Data Analysis, Statistics and Probability, Statistics - Machine Learning
19 references, page 1 of 2

[1] T. G. Dietterich, R. H. Lathrop and T. Lozano-Perez, Solving the multiple instance problem with axis-parallel rectangles, Arti cial Intelligence 89 (1997), no. 1 31 { 71.

[2] J. Amores, Multiple instance classi cation: Review, taxonomy and comparative study, Arti cial Intelligence 201 (2013) 81 { 105.

[3] D. Kotzias, M. Denil, N. de Freitas and P. Smyth, From group to individual labels using deep features, in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '15, (New York, NY, USA), pp. 597{606, ACM, 2015.

[4] G. Patrini, R. Nock, P. Rivera and T. Caetano, (almost) no label no cry, in Advances in Neural Information Processing Systems 27 (Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence and K. Q. Weinberger, eds.), pp. 190{198. Curran Associates, Inc., 2014.

[5] J. Gallicchio and M. D. Schwartz, Quark and Gluon Tagging at the LHC, Phys. Rev. Lett. 107 (2011) 172001 [1106.3076]. [OpenAIRE]

[6] ATLAS Collaboration, G. Aad et. al., Light-quark and gluon jet discrimination in pp collisions at ps = 7 TeV with the ATLAS detector, Eur. Phys. J. C74 (2014), no. 8 3023 [1405.6583].

[7] 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 (PhysTeV 2015) Les Houches, France, June 1-19, 2015, 2016. 1605.04692.

[8] D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, CoRR abs/1412.6980 (2014).

[9] F. Chollet, \Keras." https://github.com/fchollet/keras, 2015.

[10] CMS Collaboration, V Tagging Observables and Correlations, CMS-PAS-JME-14-002 (2014).

[11] ATLAS Collaboration, G. Aad et. al., Search for high-mass diboson resonances with boson-tagged jets in proton-proton collisions at ps = 8 TeV with the ATLAS detector, JHEP 12 (2015) 055 [1506.00962].

[12] CMS Collaboration, V. Khachatryan et. al., Search for the standard model Higgs boson produced through vector boson fusion and decaying to bb, Phys. Rev. D92 (2015), no. 3 032008 [1506.01010].

[13] CMS Collaboration, V. Khachatryan et. al., Measurement of electroweak production of two jets in association with a Z boson in proton-proton collisions at ps = 8 TeV, Eur. Phys. J. C75 (2015), no. 2 66 [1410.3153].

[14] ATLAS Collaboration, M. Aaboud et. al., Search for the Standard Model Higgs boson produced by vector-boson fusion and decaying to bottom quarks in ps = 8 TeV pp collisions with the ATLAS detector, JHEP 11 (2016) 112 [1606.02181].

[15] B. Bhattacherjee, S. Mukhopadhyay, M. M. Nojiri, Y. Sakaki and B. R. Webber, Quark-gluon discrimination in the search for gluino pair production at the LHC, JHEP 01 (2017) 044 [1609.08781].

19 references, page 1 of 2
Abstract
As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. This paper presents a new approach called weakly supervised classification in which class proportions are the only input into the machine learning algorithm. Using one of the most challenging binary classification tasks in high energy physics - quark versus gluon tagging - we show that weakly supervised classification can match the performance of fully supervised algorithms. Furthermore, by design, the new algorithm is insensitive to any mis-modeling of discriminating features in the data by the simulation. Wea...
Subjects
arXiv: Computer Science::Machine Learning
ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITION
free text keywords: Nuclear & Particles Physics, Mathematical Sciences, Physical Sciences, Nuclear and High Energy Physics, Particle physics, Quark, Binary classification, Gluon, Physics, Robustness (computer science), Exploit, High Energy Physics - Phenomenology, Physics - Data Analysis, Statistics and Probability, Statistics - Machine Learning
19 references, page 1 of 2

[1] T. G. Dietterich, R. H. Lathrop and T. Lozano-Perez, Solving the multiple instance problem with axis-parallel rectangles, Arti cial Intelligence 89 (1997), no. 1 31 { 71.

[2] J. Amores, Multiple instance classi cation: Review, taxonomy and comparative study, Arti cial Intelligence 201 (2013) 81 { 105.

[3] D. Kotzias, M. Denil, N. de Freitas and P. Smyth, From group to individual labels using deep features, in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '15, (New York, NY, USA), pp. 597{606, ACM, 2015.

[4] G. Patrini, R. Nock, P. Rivera and T. Caetano, (almost) no label no cry, in Advances in Neural Information Processing Systems 27 (Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence and K. Q. Weinberger, eds.), pp. 190{198. Curran Associates, Inc., 2014.

[5] J. Gallicchio and M. D. Schwartz, Quark and Gluon Tagging at the LHC, Phys. Rev. Lett. 107 (2011) 172001 [1106.3076]. [OpenAIRE]

[6] ATLAS Collaboration, G. Aad et. al., Light-quark and gluon jet discrimination in pp collisions at ps = 7 TeV with the ATLAS detector, Eur. Phys. J. C74 (2014), no. 8 3023 [1405.6583].

[7] 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 (PhysTeV 2015) Les Houches, France, June 1-19, 2015, 2016. 1605.04692.

[8] D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, CoRR abs/1412.6980 (2014).

[9] F. Chollet, \Keras." https://github.com/fchollet/keras, 2015.

[10] CMS Collaboration, V Tagging Observables and Correlations, CMS-PAS-JME-14-002 (2014).

[11] ATLAS Collaboration, G. Aad et. al., Search for high-mass diboson resonances with boson-tagged jets in proton-proton collisions at ps = 8 TeV with the ATLAS detector, JHEP 12 (2015) 055 [1506.00962].

[12] CMS Collaboration, V. Khachatryan et. al., Search for the standard model Higgs boson produced through vector boson fusion and decaying to bb, Phys. Rev. D92 (2015), no. 3 032008 [1506.01010].

[13] CMS Collaboration, V. Khachatryan et. al., Measurement of electroweak production of two jets in association with a Z boson in proton-proton collisions at ps = 8 TeV, Eur. Phys. J. C75 (2015), no. 2 66 [1410.3153].

[14] ATLAS Collaboration, M. Aaboud et. al., Search for the Standard Model Higgs boson produced by vector-boson fusion and decaying to bottom quarks in ps = 8 TeV pp collisions with the ATLAS detector, JHEP 11 (2016) 112 [1606.02181].

[15] B. Bhattacherjee, S. Mukhopadhyay, M. M. Nojiri, Y. Sakaki and B. R. Webber, Quark-gluon discrimination in the search for gluino pair production at the LHC, JHEP 01 (2017) 044 [1609.08781].

19 references, page 1 of 2
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publication . Article . Preprint . Other literature type . 2017

Weakly supervised classification in high energy physics

Dery, Lucio Mwinmaarong; Nachman, Benjamin; Rubbo, Francesco; Schwartzman, Ariel;