References
[1] S. Weinberg, “The making of the standard model,” The European Physical Journal C-Particles and Fields, vol. 34, no. 1, pp. 5–13, 2004.
[2] S. Chatrchyan et al., “Observation of a new boson at a mass of 125 gev with the cms experiment at the lhc,” Physics Letters B, vol. 716, no. 1, pp. 30–61, 2012.
[3] G. Aad et al., “Observation of a new particle in the search for the standard model higgs boson with the atlas detector at the lhc,” Physics Letters B, vol. 716, no. 1, pp. 1–29, 2012.
[4] M. E. Peskin and D. V. Schroeder, An introduction to quantum field theory. Boulder, CO: Westview, 1995.
[5] F. Mandl and G. Shaw, Quantum field theory. John Wiley & Sons, 2010.
[6] D. Goldberg, The standard model in a nutshell. Princeton, NJ: Princeton University Press, 2017.
[7] G.-C. Wick, “The evaluation of the collision matrix,” Physical review, vol. 80, no. 2, p. 268, 1950.
[8] M. Tanabashi et al., “Review of particle physics,” Phys. Rev. D, vol. 98, no. 3, p. 030001, Aug. 2018.
[9] R. Aaij et al., “Observation of the resonant character of the \(Z(4430{)}^{\ensuremath{-}}\) state,” Phys. Rev. Lett., vol. 112, no. 22, p. 222002, Jun. 2014.
[10] R. Aaij and others, “Observation of \(J/\psi p\) Resonances Consistent with Pentaquark States in \(\Lambda_b^0 \to J/\psi K^- p\) Decays,” Phys. Rev. Lett., vol. 115, p. 072001, 2015.
[11] E. Fermi, “An attempt of a theory of beta radiation.” Z. Phys., vol. 88, nos. UCRL-TRANS-726, pp. 161–177, 1934.
[12] S. L. Glashow, “Partial-symmetries of weak interactions,” Nuclear Physics, vol. 22, no. 4, pp. 579–588, 1961.
[13] A. Salam and J. C. Ward, “Electromagnetic and weak interactions,” Physics Letters, vol. 13, no. 2, pp. 168–171, 1964.
[14] F. Englert and R. Brout, “Broken symmetry and the mass of gauge vector mesons,” Physical Review Letters, vol. 13, no. 9, p. 321, 1964.
[15] P. W. Higgs, “Broken symmetries and the masses of gauge bosons,” Physical Review Letters, vol. 13, no. 16, p. 508, 1964.
[16] G. S. Guralnik, C. R. Hagen, and T. W. Kibble, “Global conservation laws and massless particles,” Physical Review Letters, vol. 13, no. 20, p. 585, 1964.
[17] S. Weinberg, “A model of leptons,” Physical review letters, vol. 19, no. 21, p. 1264, 1967.
[18] G. ’t Hooft and M. Veltman, “Regularization and renormalization of gauge fields,” Nuclear Physics B, vol. 44, no. 1, pp. 189–213, 1972.
[19] F. Hasert et al., “Observation of neutrino-like interactions without muon or electron in the gargamelle neutrino experiment,” Nuclear Physics B, vol. 73, no. 1, pp. 1–22, 1974.
[20] G. Arnison and others, “Experimental Observation of Isolated Large Transverse Energy Electrons with Associated Missing Energy at s**(1/2) = 540-GeV,” Phys. Lett., vol. B122, pp. 103–116, 1983.
[21] M. Banner and others, “Observation of Single Isolated Electrons of High Transverse Momentum in Events with Missing Transverse Energy at the CERN anti-p p Collider,” Phys. Lett., vol. B122, pp. 476–485, 1983.
[22] G. Arnison and others, “Experimental Observation of Lepton Pairs of Invariant Mass Around 95-GeV/c**2 at the CERN SPS Collider,” Phys. Lett., vol. B126, pp. 398–410, 1983.
[23] P. Bagnaia and others, “Evidence for Z0 —> e+ e- at the CERN anti-p p Collider,” Phys. Lett., vol. B129, pp. 130–140, 1983.
[24] C. S. Wu, E. Ambler, R. W. Hayward, D. D. Hoppes, and R. P. Hudson, “Experimental Test of Parity Conservation in Beta Decay,” Phys. Rev., vol. 105, pp. 1413–1414, 1957.
[25] N. Cabibbo, “Unitary Symmetry and Leptonic Decays,” Phys. Rev. Lett., vol. 10, pp. 531–533, 1963.
[26] M. Kobayashi and T. Maskawa, “CP Violation in the Renormalizable Theory of Weak Interaction,” Prog. Theor. Phys., vol. 49, pp. 652–657, 1973.
[27] G. Aad and others, “Combined Measurement of the Higgs Boson Mass in \(pp\) Collisions at \(\sqrt{s}=7\) and 8 TeV with the ATLAS and CMS Experiments,” Phys. Rev. Lett., vol. 114, p. 191803, 2015.
[28] D. Hanneke, S. Fogwell, and G. Gabrielse, “New measurement of the electron magnetic moment and the fine structure constant,” Physical Review Letters, vol. 100, no. 12, p. 120801, 2008.
[29] R. H. Parker, C. Yu, W. Zhong, B. Estey, and H. Müller, “Measurement of the fine-structure constant as a test of the standard model,” Science, vol. 360, no. 6385, pp. 191–195, 2018.
[30] C. W. Misner, K. S. Thorne, J. A. Wheeler, and D. I. Kaiser, Gravitation. Princeton University Press, 2017.
[31] C. Rovelli, “Loop quantum gravity,” Living reviews in relativity, vol. 11, no. 1, p. 5, 2008.
[32] J. Polchinski, String Theory. Cambridge: Cambridge Univ. Press, 1998.
[33] E. Corbelli and P. Salucci, “The extended rotation curve and the dark matter halo of m33,” Monthly Notices of the Royal Astronomical Society, vol. 311, no. 2, pp. 441–447, 2000.
[34] V. Trimble, “Existence and nature of dark matter in the universe,” Annual review of astronomy and astrophysics, vol. 25, no. 1, pp. 425–472, 1987.
[35] P. A. R. Ade and others, “Planck 2015 results. XIII. Cosmological parameters,” Astron. Astrophys., vol. 594, p. A13, 2016.
[36] Y. Fukuda et al., “Evidence for oscillation of atmospheric neutrinos,” Physical Review Letters, vol. 81, no. 8, p. 1562, 1998.
[37] S. collaboration and others, “Measurement of the rate of nu_e+ d–> p+ p+ e^-interactions produced by 8B solar neutrinos at the sudbury neutrino observatory,” arXiv preprint nucl-ex/0106015, 2001.
[38] E. K. Akhmedov, G. C. Branco, and M. N. Rebelo, “Seesaw mechanism and structure of neutrino mass matrix,” Phys. Lett., vol. B478, pp. 215–223, 2000.
[39] A. G. Riess et al., “Type ia supernova discoveries at z> 1 from the hubble space telescope: Evidence for past deceleration and constraints on dark energy evolution,” The Astrophysical Journal, vol. 607, no. 2, p. 665, 2004.
[40] R. J. Adler, B. Casey, and O. C. Jacob, “Vacuum catastrophe: An elementary exposition of the cosmological constant problem,” American Journal of Physics, vol. 63, no. 7, pp. 620–626, 1995.
[41] G. Degrassi et al., “Higgs mass and vacuum stability in the standard model at nnlo,” Journal of High Energy Physics, vol. 2012, no. 8, p. 98, 2012.
[42] H.-Y. Cheng, “The strong cp problem revisited,” Physics Reports, vol. 158, no. 1, pp. 1–89, 1988.
[43] T. Appelquist and J. Carazzone, “Infrared singularities and massive fields,” Phys. Rev. D, vol. 11, no. 10, pp. 2856–2861, May 1975.
[44] W. Buchmuller and D. Wyler, “Effective Lagrangian Analysis of New Interactions and Flavor Conservation,” Nucl. Phys., vol. B268, pp. 621–653, 1986.
[45] S. Weinberg, “Baryon-and lepton-nonconserving processes,” Physical Review Letters, vol. 43, no. 21, p. 1566, 1979.
[46] R. D. Ball and others, “Parton distributions from high-precision collider data,” Eur. Phys. J., vol. C77, no. 10, p. 663, 2017.
[47] G. Altarelli and G. Parisi, “Asymptotic Freedom in Parton Language,” Nucl. Phys., vol. B126, pp. 298–318, 1977.
[48] Y. L. Dokshitzer, “Calculation of the Structure Functions for Deep Inelastic Scattering and e+ e- Annihilation by Perturbation Theory in Quantum Chromodynamics.” Sov. Phys. JETP, vol. 46, pp. 641–653, 1977.
[49] V. N. Gribov and L. N. Lipatov, “Deep inelastic e p scattering in perturbation theory,” Sov. J. Nucl. Phys., vol. 15, pp. 438–450, 1972.
[50] J. C. Collins, D. E. Soper, and G. F. Sterman, “Factorization of Hard Processes in QCD,” Adv. Ser. Direct. High Energy Phys., vol. 5, pp. 1–91, 1989.
[51] G. P. Lepage, “A New Algorithm for Adaptive Multidimensional Integration,” J. Comput. Phys., vol. 27, p. 192, 1978.
[52] S. Höche, “Introduction to parton-shower event generators,” in Proceedings, Theoretical Advanced Study Institute in Elementary Particle Physics: Journeys Through the Precision Frontier: Amplitudes for Colliders (TASI 2014): Boulder, Colorado, June 2-27, 2014, 2015, pp. 235–295.
[53] C. Service graphique, “Overall view of the LHC. Vue d’ensemble du LHC,” Jun. 2014.
[54] B. Wolf, Handbook of ion sources. CRC press, 2017.
[55] T. Mc Cauley, “Collisions recorded by the CMS detector on 14 Oct 2016 during the high pile-up fill,” Nov-2016.
[56] G. Aad and others, “The ATLAS Experiment at the CERN Large Hadron Collider,” JINST, vol. 3, p. S08003, 2008.
[57] S. Chatrchyan and others, “The CMS Experiment at the CERN LHC,” JINST, vol. 3, p. S08004, 2008.
[58] A. A. Alves Jr. and others, “The LHCb Detector at the LHC,” JINST, vol. 3, p. S08005, 2008.
[59] K. Aamodt and others, “The ALICE experiment at the CERN LHC,” JINST, vol. 3, p. S08002, 2008.
[60] G. Anelli and others, “The TOTEM experiment at the CERN Large Hadron Collider,” JINST, vol. 3, p. S08007, 2008.
[61] O. Adriani and others, “The LHCf detector at the CERN Large Hadron Collider,” JINST, vol. 3, p. S08006, 2008.
[62] B. Acharya and others, “The Physics Programme Of The MoEDAL Experiment At The LHC,” Int. J. Mod. Phys., vol. A29, p. 1430050, 2014.
[63] G. L. Bayatian and others, CMS Physics: Technical Design Report Volume 1: Detector Performance and Software. Geneva: CERN, 2006.
[64] T. Sakuma and T. McCauley, “Detector and event visualization with sketchup at the cms experiment,” in Journal of physics: Conference series, 2014, vol. 513, p. 022032.
[65] S. Chatrchyan and others, “Description and performance of track and primary-vertex reconstruction with the CMS tracker,” JINST, vol. 9, no. 10, p. P10009, 2014.
[66] H. Spieler, Semiconductor detector systems, vol. 12. Oxford university press, 2005.
[67] The CMS electromagnetic calorimeter project: Technical Design Report. Geneva: CERN, 1997.
[68] S. Chatrchyan and others, “Performance of the CMS Hadron Calorimeter with Cosmic Ray Muons and LHC Beam Data,” JINST, vol. 5, p. T03012, 2010.
[69] A. M. Sirunyan and others, “Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at \(\sqrt{s}=\) 13 TeV,” JINST, vol. 13, no. 6, p. P06015, 2018.
[70] A. M. Sirunyan and others, “Particle-flow reconstruction and global event description with the CMS detector,” JINST, vol. 12, no. 10, p. P10003, 2017.
[71] S. Agostinelli and others, “GEANT4: A Simulation toolkit,” Nucl. Instrum. Meth., vol. A506, pp. 250–303, 2003.
[72] S. Abdullin, P. Azzi, F. Beaudette, P. Janot, and A. Perrotta, “The fast simulation of the CMS detector at LHC,” J. Phys. Conf. Ser., vol. 331, p. 032049, 2011.
[73] J. de Favereau et al., “DELPHES 3, A modular framework for fast simulation of a generic collider experiment,” JHEP, vol. 2, p. 057, 2014.
[74] M. Paganini, L. de Oliveira, and B. Nachman, “Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters,” Phys. Rev. Lett., vol. 120, no. 4, p. 042003, 2018.
[75] L. de Oliveira, M. Paganini, and B. Nachman, “Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis,” Comput. Softw. Big Sci., vol. 1, no. 1, p. 4, 2017.
[76] P. Billoir and S. Qian, “Simultaneous pattern recognition and track fitting by the Kalman filtering method,” Nucl. Instrum. Meth., vol. A294, pp. 219–228, 1990.
[77] R. Mankel, “A concurrent track evolution algorithm for pattern recognition in the HERA-B main tracking system,” DESY, Hamburg, DESY-97-054, Mar. 1997.
[78] R. Fruhwirth, W. Waltenberger, and P. Vanlaer, “Adaptive vertex fitting,” J. Phys., vol. G34, p. N343, 2007.
[79] W. Adam, R. Frühwirth, A. Strandlie, and T. Todor, “Reconstruction of Electrons with the Gaussian-Sum Filter in the CMS Tracker at the LHC,” 2005.
[80] V. Khachatryan and others, “Performance of Electron Reconstruction and Selection with the CMS Detector in Proton-Proton Collisions at sqrt(s) = 8 TeV,” JINST, vol. 10, no. 6, p. P06005, 2015.
[81] V. Khachatryan and others, “Performance of Photon Reconstruction and Identification with the CMS Detector in Proton-Proton Collisions at sqrt(s) = 8 TeV,” JINST, vol. 10, no. 8, p. P08010, 2015.
[82] C. Collaboration, “Pileup Removal Algorithms,” 2014.
[83] M. Cacciari, G. P. Salam, and G. Soyez, “The anti-\(k_t\) jet clustering algorithm,” JHEP, vol. 4, p. 063, 2008.
[84] V. Khachatryan and others, “Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV,” JINST, vol. 12, no. 2, p. P02014, 2017.
[85] A. M. Sirunyan and others, “Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV,” JINST, vol. 13, no. 5, p. P05011, 2018.
[86] M. L. Casado and others, “Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators,” 2017.
[87] A. G. Baydin et al., “Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model,” 2018.
[88] C. W. Gardiner, Handbook of stochastic methods: for physics, chemistry and the natural sciences; 3rd ed. Berlin: Springer, 2004.
[89] R. V. Hogg and A. T. Craig, Introduction to mathematical statistics.(5th edition). Upper Saddle River, New Jersey: Prentice Hall, 1995.
[90] K. Cranmer, “Practical Statistics for the LHC,” in Proceedings, 2011 European School of High-Energy Physics (ESHEP 2011): Cheile Gradistei, Romania, September 7-20, 2011, 2015, pp. 267–308.
[91] J. S. Conway, “Incorporating Nuisance Parameters in Likelihoods for Multisource Spectra,” in Proceedings, PHYSTAT 2011 Workshop on Statistical Issues Related to Discovery Claims in Search Experiments and Unfolding, CERN,Geneva, Switzerland 17-20 January 2011, 2011, pp. 115–120.
[92] K. Cranmer, G. Lewis, L. Moneta, A. Shibata, and W. Verkerke, “HistFactory: A tool for creating statistical models for use with RooFit and RooStats,” 2012.
[93] A. B. Owen, Monte carlo theory, methods and examples. 2013.
[94] D. B. Rubin, “Bayesianly justifiable and relevant frequency calculations for the applies statistician,” The Annals of Statistics, pp. 1151–1172, 1984.
[95] M. A. Beaumont, W. Zhang, and D. J. Balding, “Approximate bayesian computation in population genetics,” Genetics, vol. 162, no. 4, pp. 2025–2035, 2002.
[96] J. Brehmer, K. Cranmer, G. Louppe, and J. Pavez, “A Guide to Constraining Effective Field Theories with Machine Learning,” Phys. Rev., vol. D98, no. 5, p. 052004, 2018.
[97] J. Neyman and E. S. Pearson, “On the problem of the most efficient tests of statistical hypotheses,” Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, vol. 231, pp. 289–337, 1933.
[98] S. S. Wilks, “The large-sample distribution of the likelihood ratio for testing composite hypotheses,” The Annals of Mathematical Statistics, vol. 9, no. 1, pp. 60–62, 1938.
[99] A. Wald, “Tests of statistical hypotheses concerning several parameters when the number of observations is large,” Transactions of the American Mathematical society, vol. 54, no. 3, pp. 426–482, 1943.
[100] G. Cowan, K. Cranmer, E. Gross, and O. Vitells, “Asymptotic formulae for likelihood-based tests of new physics,” Eur. Phys. J., vol. C71, p. 1554, 2011.
[101] A. L. Read, “Presentation of search results: The CL(s) technique,” J. Phys., vol. G28, pp. 2693–2704, 2002.
[102] T. Junk, “Confidence level computation for combining searches with small statistics,” Nucl. Instrum. Meth., vol. A434, pp. 435–443, 1999.
[103] J. Neyman, “Outline of a theory of statistical estimation based on the classical theory of probability,” Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences, vol. 236, no. 767, pp. 333–380, 1937.
[104] G. J. Feldman and R. D. Cousins, “A Unified approach to the classical statistical analysis of small signals,” Phys. Rev., vol. D57, pp. 3873–3889, 1998.
[105] W. A. Rolke, A. M. Lopez, and J. Conrad, “Limits and confidence intervals in the presence of nuisance parameters,” Nucl. Instrum. Meth., vol. A551, pp. 493–503, 2005.
[106] F. James and M. Roos, “MINUIT: A system for function minimization and analysis of the parameter errors and corrections,” Comput. Phys. Commun., vol. 10, nos. CERN-DD-75-20, pp. 343–367, 1975.
[107] R. A. Fisher, “Theory of statistical estimation,” Mathematical Proceedings of the Cambridge Philosophical Society, vol. 22, no. 5, pp. 700–725, 1925.
[108] H. Cramér, Mathematical methods of statistics (pms-9), vol. 9. Princeton university press, 2016.
[109] C. R. Rao, “Information and the accuracy attainable in the estimation of statistical parameters,” in Breakthroughs in statistics, Springer, 1992, pp. 235–247.
[110] P. S. Laplace, “Memoir on the probability of the causes of events,” Statistical Science, vol. 1, no. 3, pp. 364–378, 1986.
[111] T. M. Mitchell, Machine learning, 1st ed. New York, NY, USA: McGraw-Hill, Inc., 1997.
[112] V. N. Vapnik, “An overview of statistical learning theory,” IEEE transactions on neural networks, vol. 10, no. 5, pp. 988–999, 1999.
[113] J. Friedman, T. Hastie, and R. Tibshirani, The elements of statistical learning, vol. 1. Springer series in statistics New York, NY, USA: 2001.
[114] T. Nguyen and S. Sanner, “Algorithms for direct 0–1 loss optimization in binary classification,” in International conference on machine learning, 2013, pp. 1085–1093.
[115] I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT Press, 2016.
[116] G. Louppe, “Understanding random forests: From theory to practice,” arXiv preprint arXiv:1407.7502, 2014.
[117] Y. Freund and R. E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” Journal of computer and system sciences, vol. 55, no. 1, pp. 119–139, 1997.
[118] J. Friedman, T. Hastie, R. Tibshirani, and others, “Additive logistic regression: A statistical view of boosting (with discussion and a rejoinder by the authors),” The annals of statistics, vol. 28, no. 2, pp. 337–407, 2000.
[119] J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” Annals of statistics, pp. 1189–1232, 2001.
[120] L. Mason, J. Baxter, P. L. Bartlett, and M. R. Frean, “Boosting algorithms as gradient descent,” in Advances in neural information processing systems, 2000, pp. 512–518.
[121] L. Breiman, Classification and regression trees. Routledge, 2017.
[122] L. Breiman, “Bagging predictors,” Machine learning, vol. 24, no. 2, pp. 123–140, 1996.
[123] T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016, pp. 785–794.
[124] J. Nocedal and S. J. Wright, Numerical optimization, Second. New York, NY, USA: Springer, 2006.
[125] H. Robbins and S. Monro, “A stochastic approximation method,” The Annals of Mathematical Statistics, vol. 22, no. 3, pp. 400–407, 1951.
[126] S. Ruder, “An overview of gradient descent optimization algorithms,” arXiv preprint arXiv:1609.04747, 2016.
[127] G. Cybenko, “Approximation by superpositions of a sigmoidal function,” Mathematics of control, signals and systems, vol. 2, no. 4, pp. 303–314, 1989.
[128] A. G. Baydin, B. A. Pearlmutter, A. A. Radul, and J. M. Siskind, “Automatic differentiation in machine learning: A survey,” Journal of Marchine Learning Research, vol. 18, pp. 1–43, 2018.
[129] M. Abadi et al., “TensorFlow: Large-scale machine learning on heterogeneous systems.” 2015.
[130] A. Paszke et al., “Automatic differentiation in pytorch,” in NIPS-w, 2017.
[131] M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. R. Salakhutdinov, and A. J. Smola, “Deep sets,” in Advances in neural information processing systems, 2017, pp. 3391–3401.
[132] I. Henrion et al., “Neural message passing for jet physics,” 2017.
[133] D. Guest, K. Cranmer, and D. Whiteson, “Deep Learning and its Application to LHC Physics,” Ann. Rev. Nucl. Part. Sci., vol. 68, pp. 161–181, 2018.
[134] P. Baldi, K. Cranmer, T. Faucett, P. Sadowski, and D. Whiteson, “Parameterized neural networks for high-energy physics,” The European Physical Journal C, vol. 76, no. 5, p. 235, 2016.
[135] D. Guest, J. Collado, P. Baldi, S.-C. Hsu, G. Urban, and D. Whiteson, “Jet Flavor Classification in High-Energy Physics with Deep Neural Networks,” Phys. Rev., vol. D94, no. 11, p. 112002, 2016.
[136] L. de Oliveira, M. Kagan, L. Mackey, B. Nachman, and A. Schwartzman, “Jet-images — deep learning edition,” JHEP, vol. 7, p. 069, 2016.
[137] F. Chollet and others, “Keras.” https://keras.io, 2015.
[138] “Performance of the DeepJet b tagging algorithm using 41.9/fb of data from proton-proton collisions at 13TeV with Phase 1 CMS detector,” Nov. 2018.
[139] M. Stoye, J. Kieseler, H. Qu, L. Gouskos, and M. Verzetti, “DeepJet: Generic physics object based jet multiclass classification for lhc experiments.”
[140] “Performance of Deep Tagging Algorithms for Boosted Double Quark Jet Topology in Proton-Proton Collisions at 13 TeV with the Phase-0 CMS Detector,” Jul. 2018.
[141] V. Innocente, L. Silvestris, D. Stickland, and others, “CMS software architecture: Software framework, services and persistency in high level trigger, reconstruction and analysis,” Computer Physics Communications, vol. 140, nos. 1-2, pp. 31–44, 2001.
[142] I. Bird and R. W. Jones, “LHC computing grid: Technical design report,” 2005.
[143] D. H. Guest et al., “Lwtnn/lwtnn: Version 2.8.” Nov-2018.
[144] M. Rieger, “CMSSW-dnn.” https://gitlab.cern.ch/mrieger/CMSSW-DNN, 2017.
[145] P. de Castro, M. Rieger, and others, “DeepJet integration.” https://github.com/cms-sw/cmssw/pull/19893, 2017.
[146] M. Stoye and others, “DeepJet software framework.” https://github.com/mstoye/DeepJet, 2017.
[147] P. De Castro Manzano et al., “Hemisphere Mixing: a Fully Data-Driven Model of QCD Multijet Backgrounds for LHC Searches,” PoS, vols. EPS-HEP2017, p. 370, 2017.
[148] A. M. Sirunyan and others, “Search for nonresonant Higgs boson pair production in the \(\mathrm{b\overline{b}b\overline{b}}\) final state at \(\sqrt{s} =\) 13 TeV,” Submitted to: JHEP, 2018.
[149] S. Chatrchyan and others, “Observation of a new boson with mass near 125 GeV in pp collisions at \(\sqrt{s}\) = 7 and 8 TeV,” JHEP, vol. 6, p. 081, 2013.
[150] G. Aad and others, “Measurements of the Higgs boson production and decay rates and constraints on its couplings from a combined ATLAS and CMS analysis of the LHC pp collision data at \(\sqrt{s}=7\) and 8 TeV,” JHEP, vol. 8, p. 045, 2016.
[151] A. M. Sirunyan and others, “Observation of \(\mathrm{t\overline{t}}\)H production,” Phys. Rev. Lett., vol. 120, no. 23, p. 231801, 2018.
[152] M. Aaboud and others, “Observation of Higgs boson production in association with a top quark pair at the LHC with the ATLAS detector,” Phys. Lett., vol. B784, pp. 173–191, 2018.
[153] C. O. Dib, R. Rosenfeld, and A. Zerwekh, “Double Higgs production and quadratic divergence cancellation in little Higgs models with T parity,” JHEP, vol. 5, p. 074, 2006.
[154] R. Grober and M. Muhlleitner, “Composite Higgs Boson Pair Production at the LHC,” JHEP, vol. 6, p. 020, 2011.
[155] R. Contino, M. Ghezzi, M. Moretti, G. Panico, F. Piccinini, and A. Wulzer, “Anomalous Couplings in Double Higgs Production,” JHEP, vol. 8, p. 154, 2012.
[156] M. J. Dolan, C. Englert, and M. Spannowsky, “New Physics in LHC Higgs boson pair production,” Phys. Rev., vol. D87, no. 5, p. 055002, 2013.
[157] S. Dawson, A. Ismail, and I. Low, “What’s in the loop? The anatomy of double Higgs production,” Phys. Rev., vol. D91, no. 11, p. 115008, 2015.
[158] J. Baglio, A. Djouadi, R. Gröber, M. M. Mühlleitner, J. Quevillon, and M. Spira, “The measurement of the Higgs self-coupling at the LHC: theoretical status,” JHEP, vol. 4, p. 151, 2013.
[159] A. M. Sirunyan and others, “Measurements of properties of the Higgs boson decaying into the four-lepton final state in pp collisions at \(\sqrt{s}=13\) TeV,” JHEP, vol. 11, p. 047, 2017.
[160] D. de Florian and others, “Handbook of LHC Higgs Cross Sections: 4. Deciphering the Nature of the Higgs Sector,” 2016.
[161] D. de Florian and J. Mazzitelli, “Higgs Boson Pair Production at Next-to-Next-to-Leading Order in QCD,” Phys. Rev. Lett., vol. 111, p. 201801, 2013.
[162] S. Dawson, S. Dittmaier, and M. Spira, “Neutral Higgs boson pair production at hadron colliders: QCD corrections,” Phys. Rev., vol. D58, p. 115012, 1998.
[163] S. Borowka et al., “Higgs Boson Pair Production in Gluon Fusion at Next-to-Leading Order with Full Top-Quark Mass Dependence,” Phys. Rev. Lett., vol. 117, no. 1, p. 012001, 2016.
[164] D. de Florian and J. Mazzitelli, “Higgs pair production at next-to-next-to-leading logarithmic accuracy at the LHC,” JHEP, vol. 9, p. 053, 2015.
[165] A. Carvalho et al., “Analytical parametrization and shape classification of anomalous HH production in the EFT approach,” 2016.
[166] G. Aad and others, “Search for Higgs boson pair production in the \(b\bar{b}b\bar{b}\) final state from pp collisions at \(\sqrt{s} = 8\) TeVwith the ATLAS detector,” Eur. Phys. J., vol. C75, no. 9, p. 412, 2015.
[167] A. M. Sirunyan and others, “Search for Higgs boson pair production in the \(bb\tau\tau\) final state in proton-proton collisions at \(\sqrt{(}s)=8\text{ }\text{ }\mathrm{TeV}\),” Phys. Rev., vol. D96, no. 7, p. 072004, 2017.
[168] M. Aaboud and others, “Search for pair production of Higgs bosons in the \(b\bar{b}b\bar{b}\) final state using proton-proton collisions at \(\sqrt{s} = 13\) TeV with the ATLAS detector,” 2018.
[169] A. M. Sirunyan and others, “Search for resonant and nonresonant Higgs boson pair production in the \(\mathrm{b}\overline{\mathrm{b}}\mathit{\ell \nu \ell \nu }\) final state in proton-proton collisions at \(\sqrt{s}=13\) TeV,” JHEP, vol. 1, p. 054, 2018.
[170] A. M. Sirunyan and others, “Search for Higgs boson pair production in events with two bottom quarks and two tau leptons in proton-proton collisions at \(\sqrt s\) =13TeV,” Phys. Lett., vol. B778, pp. 101–127, 2018.
[171] A. M. Sirunyan and others, “Search for Higgs boson pair production in the \(\gamma\gamma\mathrm{b\overline{b}}\) final state in pp collisions at \(\sqrt{s}=\) 13 TeV,” 2018.
[172] A. M. Sirunyan and others, “Search for production of Higgs boson pairs in the four b quark final state using large-area jets in proton-proton collisions at \(\sqrt{s}=\) 13 TeV,” 2018.
[173] A. Falkowski, “Higgs Basis: Proposal for an EFT basis choice for LHC HXSWG,” Mar. 2015.
[174] A. Carvalho, M. Dall’Osso, T. Dorigo, F. Goertz, C. A. Gottardo, and M. Tosi, “Higgs Pair Production: Choosing Benchmarks With Cluster Analysis,” JHEP, vol. 4, p. 126, 2016.
[175] A. M. Sirunyan and others, “Search for resonant pair production of Higgs bosons decaying to bottom quark-antiquark pairs in proton-proton collisions at 13 TeV,” JHEP, vol. 8, p. 152, 2018.
[176] J. Alwall et al., “The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations,” JHEP, vol. 7, p. 079, 2014.
[177] B. Hespel, D. Lopez-Val, and E. Vryonidou, “Higgs pair production via gluon fusion in the Two-Higgs-Doublet Model,” JHEP, vol. 9, p. 124, 2014.
[178] R. D. Ball and others, “Parton distributions for the LHC Run II,” JHEP, vol. 4, p. 040, 2015.
[179] S. Wertz and V. Lemaitre, “Search for Higgs boson pair production in the \(\mathrm{b\bar{b}} \ell \nu \ell \nu\) final state with the CMS detector,” 2018.
[180] F. Pedregosa et al., “Scikit-learn: Machine learning in python,” Journal of machine learning research, vol. 12, no. Oct, pp. 2825–2830, 2011.
[181] A. M. Sirunyan and others, “Measurement of the inelastic proton-proton cross section at \(\sqrt{s}=13\) TeV,” JHEP, vol. 7, p. 161, 2018.
[182] “CMS Luminosity Measurements for the 2016 Data Taking Period,” CERN, Geneva, CMS-PAS-LUM-17-001, 2017.
[183] J. Butterworth and others, “PDF4LHC recommendations for LHC Run II,” J. Phys., vol. G43, p. 023001, 2016.
[184] “Procedure for the LHC Higgs boson search combination in Summer 2011,” CERN, Geneva, CMS-NOTE-2011-005. ATL-PHYS-PUB-2011-11, Aug. 2011.
[185] “Combination of searches for Higgs boson pair production in proton-proton collisions at \(\sqrt{s} = 13~\mathrm{TeV}\),” CERN, Geneva, CMS-PAS-HIG-17-030, 2018.
[186] P. De Castro and T. Dorigo, “INFERNO: Inference-Aware Neural Optimisation,” 2018.
[187] S. N. Wood, “Statistical inference for noisy nonlinear ecological dynamic systems,” Nature, vol. 466, no. 7310, p. 1102, 2010.
[188] K. Cranmer, J. Pavez, and G. Louppe, “Approximating likelihood ratios with calibrated discriminative classifiers,” arXiv preprint arXiv:1506.02169, 2015.
[189] C. Adam-Bourdarios, G. Cowan, C. Germain, I. Guyon, B. Kégl, and D. Rousseau, “The Higgs boson machine learning challenge,” in Proceedings of the nips 2014 workshop on high-energy physics and machine learning, 2015, vol. 42, pp. 19–55.
[190] D. Basu, “On partial sufficiency: A review,” in Selected works of debabrata basu, Springer, 2011, pp. 291–303.
[191] D. A. Sprott, “Marginal and conditional sufficiency,” Biometrika, vol. 62, no. 3, pp. 599–605, 1975.
[192] D. Tran, A. Kucukelbir, A. B. Dieng, M. Rudolph, D. Liang, and D. M. Blei, “Edward: A library for probabilistic modeling, inference, and criticism,” arXiv preprint arXiv:1610.09787, 2016.
[193] A. Hocker and others, “TMVA—toolkit for multivariate data analysis, in proceedings of 11th international workshop on advanced computing and analysis techniques in physics research,” Amsterdam, The Netherlands, 2007.
[194] P. Baldi, P. Sadowski, and D. Whiteson, “Searching for exotic particles in high-energy physics with deep learning,” Nature communications, vol. 5, p. 4308, 2014.
[195] R. M. Neal, “Computing likelihood functions for high-energy physics experiments when distributions are defined by simulators with nuisance parameters,” in PHYSTAT-lhc workshop on statistical issues for lhc physics, 2007, pp. 111–118.
[196] J. Brehmer, G. Louppe, J. Pavez, and K. Cranmer, “Mining gold from implicit models to improve likelihood-free inference,” 2018.
[197] J. Brehmer, K. Cranmer, G. Louppe, and J. Pavez, “Constraining effective field theories with machine learning,” arXiv preprint arXiv:1805.00013, 2018.
[198] J. Brehmer, K. Cranmer, G. Louppe, and J. Pavez, “A guide to constraining effective field theories with machine learning,” arXiv preprint arXiv:1805.00020, 2018.
[199] B. Jiang, T.-y. Wu, C. Zheng, and W. H. Wong, “Learning summary statistic for approximate bayesian computation via deep neural network,” arXiv preprint arXiv:1510.02175, 2015.
[200] G. Louppe, M. Kagan, and K. Cranmer, “Learning to pivot with adversarial networks,” in Advances in neural information processing systems, 2017, pp. 982–991.
[201] P. de Castro, “Code and manuscript for the paper "inferno: Inference-aware neural optimisation",” GitHub repository. https://github.com/pablodecm/paper-inferno; GitHub, 2018.
[202] J. V. Dillon et al., “TensorFlow distributions,” 2017.
[203] R. Barlow, “Extended maximum likelihood,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 297, no. 3, pp. 496–506, 1990.