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De Castro Manzano, Pablo (2019) Statistical Learning and Inference at Particle Collider Experiments. [Ph.D. thesis]

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Abstract (italian or english)

Advances in data analysis techniques may play a decisive role in the discovery reach of particle collider experiments. However, the importing of expertise and methods from other data-centric disciplines such as machine learning and statistics faces significant hurdles, mainly due to the established use of different language and constructs. A large part of this document, also conceived as an introduction to the description of an analysis searching for non-resonant Higgs pair production in data collected by the CMS detector at the Large Hadron Collider (LHC), is therefore devoted to a broad redefinition of the relevant concepts for problems in experimental particle physics. The aim is to better connect these issues with those in other fields of research, so the solutions found can be repurposed.

The formal exploration of the properties of the statistical models at particle colliders is useful to highlight the main challenges posed by statistical inference in this context: the multi-dimensional nature of the models, which can be studied only in a generative manner via forward simulation of observations, and the effect of nuisance parameters. The first issue can be tackled with likelihood-free inference methods coupled with the use of low-dimensional summary statistics, which may be constructed either with machine learning techniques or through physically motivated variables (e.g. event reconstruction). The second, i.e. the misspecification of the generative model which is addressed by the inclusion of nuisance parameters, reduces the effectiveness of summary statistics constructed with machine-learning techniques.

A subset of the data analysis techniques formally discussed in the introductory part of the document are also exploited to study the non-resonant production process pp → HH → bbbb at the LHC in the context of the Standard Model (SM) and its extensions in effective fields theories (EFT), based on anomalous couplings of the Higgs field. Data collected in 2016 by the CMS detector and corresponding to a total of 35.9 fb−1 of proton-proton collisions are used to set an 95% confidence upper limit at 847 fb on the production cross section σ(pp → HH → bbbb) in the SM. Upper limits are also obtained for the cross sections corresponding to a representative set of points of the parameter space of EFT. The combination of those results with the ones obtained from the study of other decay channels of HH pairs is also discussed.

In addition, the exercise of reformulating the goals of high energy physics analysis as a statistical inference problem is combined with modern machine learning technologies to develop a new technique, referred to as inference-aware neural optimisation. The technique produces summary statistics which directly minimise the expected uncertainty on the parameters of interest, optimally accounting for the effect of nuisance parameters. The application of this technique to a synthetic problem demonstrates that the obtained summary statistics are considerable more effective than those obtained with standard supervised learning methods, when the effect of the nuisance parameters is significant. Assuming its scalability to LHC data scenarios, this technique has ground-breaking potential for analyses dominated by systematic uncertainties.

Abstract (a different language)

I progressi nelle tecniche di analisi dei dati possono giocare un ruolo decisivo nelle prospettive di scoperta degli esperimenti ai colliders, tuttavia l'acquisizione di expertise e nuove tecniche in machine learning e statistica da altre discipline quantitative incontra barriere significative, sopratutto causate dall'uso di diverso linguaggio e formalismi. Una gran parte di questo documento, pensata anche come introduzione alla descrizione di un'analisi che ricerca la produzione non risonante di coppie di bosoni di Higgs in dati raccolti dal rivelatore CMS al Large Hadron Collider (LHC), è per questo motivo rivolta ad una ridefinizione dei concetti rilevanti per i problemi in fisica sperimentale delle particelle elementari che permetta loro di venir collegati a quelli di altri campi di ricerca, in modo tale che le soluzioni trovate possano essere riutilizzate.

L'esplorazione formale delle proprietà dei modelli statistici ai colliders di particelle è utile per sottolineare le principali sfide poste dalla pratica dell'inferenza statistica: la natura multi-dimensionale dei modelli, che sono studiabili solamente con metodi generativi (cioè attraverso simulazioni), e l'effetto di parametri di disturbo. Il primo problema può essere affrontato con metodi di inferenza "likelihood-free", e con l'identificazione di summary statistics a bassa dimensionalità, che possono essere costruite con tecniche di machine learning o con l'uso di variabili motivate dalle caratteristiche fisiche dei processi studiati. Il secondo, ovvero la cattiva specificazione del modello generativo, che necessita pertanto l'inclusione di parametri di disturbo, riduce l'utilità delle summary statistics create con algoritmi di machine learning.

Alcune delle tecniche di analisi dati formalmente discusse nella parte introduttiva sono anche sfruttate per studiare il processo di produzione pp->HH->bbbb a LHC nel contesto del modello standard (SM) e delle sue estensioni in teorie di campo efficace (EFT), basate su accoppiamenti anomali del campo di Higgs. Dati raccolti nel 2016 dal rivelatore CMS corrispondenti a un totale di 35.9 femtobarns inversi di collisioni protone-protone sono usati per fissare un limite al 95% di livello di confidenza a 847 fb sulla sezione d'urto di produzione sigma(pp->HH->bbbb) nello SM. Limiti superiori sono ottenuti anche per le sezioni d'urto corrispondenti ad un insieme rappresentativo di punti dello spazio dei parametri delle teorie EFT. E' altresì discussa la combinazione di questi risultati con quelli derivanti dallo studio di altri canali di decadimento delle coppie HH.

In aggiunta, il risultato dell'esercizio di riformulare i goals dell'analisi in fisica delle alte energie come un problema di inferenza statistica è stato combinato con l'uso di strumenti avanzati di machine learning per sviluppare una nuova tecnica, chiamata "inference-aware neural optimization", che produce summary statistics che minimizzano direttamente l'incertezza attesa sui parametri di interesse, tenendo conto in maniera ottimale dell'effetto dei parametri di disturbo. L'applicazione di questa tecnica ad un problema di test dimostra che le summary statistics ottenute con questo metodo sono considerevolmente più efficaci di quelle ottenute con approcci standard di supervised learning quando l'effetto dei parametri di disturbo è significativo. Assumendo la sua scalabilità a scenari di analisi dati a LHC, questa tecnica potrebbe rivelarsi rivoluzionaria per analisi dominate da incertezze sistematiche.

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EPrint type:Ph.D. thesis
Tutor:Dorigo, Tommaso
Ph.D. course:Ciclo 31 > Corsi 31 > FISICA
Data di deposito della tesi:18 June 2019
Anno di Pubblicazione:29 March 2019
Key Words:inference, learning, physics, LHC, CMS
Settori scientifico-disciplinari MIUR:Area 02 - Scienze fisiche > FIS/04 Fisica nucleare e subnucleare
Area 02 - Scienze fisiche > FIS/01 Fisica sperimentale
Struttura di riferimento:Dipartimenti > Dipartimento di Fisica e Astronomia "Galileo Galilei"
Codice ID:11977
Depositato il:08 Nov 2019 11:38
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Le url contenute in alcuni riferimenti sono raggiungibili cliccando sul link alla fine della citazione (Vai!) e tramite Google (Ricerca con Google). Il risultato dipende dalla formattazione della citazione.

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[4] M. E. Peskin and D. V. Schroeder, An introduction to quantum field theory. Boulder, CO: Westview, 1995. Cerca con Google

[5] F. Mandl and G. Shaw, Quantum field theory. John Wiley & Sons, 2010. Cerca con Google

[6] D. Goldberg, The standard model in a nutshell. Princeton, NJ: Princeton University Press, 2017. Cerca con Google

[7] G.-C. Wick, “The evaluation of the collision matrix,” Physical review, vol. 80, no. 2, p. 268, 1950. Cerca con Google

[8] M. Tanabashi et al., “Review of particle physics,” Phys. Rev. D, vol. 98, no. 3, p. 030001, Aug. 2018. Cerca con Google

[9] R. Aaij et al., “Observation of the resonant character of the Z 4430 state,” Phys. Rev. Lett., vol. 112, no. 22, p. 222002, Jun. 2014. Cerca con Google

[10] R. Aaij and others, “Observation of J/ψ Resonances Consistent with Pentaquark States in Λ_b → J/ψKp Decays,” Phys. Rev. Lett., vol. 115, p. 072001, 2015. Cerca con Google

[11] E. Fermi, “An attempt of a theory of beta radiation.” Z. Phys., vol. 88, nos. UCRL-TRANS-726, pp. 161–177, 1934. Cerca con Google

[12] S. L. Glashow, “Partial-symmetries of weak interactions,” Nuclear Physics, vol. 22, no. 4, pp. 579–588, 1961. Cerca con Google

[13] A. Salam and J. C. Ward, “Electromagnetic and weak interactions,” Physics Letters, vol. 13, no. 2, pp. 168–171, 1964. Cerca con Google

[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. Cerca con Google

[15] P. W. Higgs, “Broken symmetries and the masses of gauge bosons,” Physical Review Letters, vol. 13, no. 16, p. 508, 1964. Cerca con Google

[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. Cerca con Google

[17] S. Weinberg, “A model of leptons,” Physical review letters, vol. 19, no. 21, p. 1264, 1967. Cerca con Google

[18] G. ’t Hooft and M. Veltman, “Regularization and renormalization of gauge fields,” Nuclear Physics B, vol. 44, no. 1, pp. 189–213, 1972. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[25] N. Cabibbo, “Unitary Symmetry and Leptonic Decays,” Phys. Rev. Lett., vol. 10, pp. 531–533, 1963. Cerca con Google

[26] M. Kobayashi and T. Maskawa, “CP Violation in the Renormalizable Theory of Weak Interaction,” Prog. Theor. Phys., vol. 49, pp. 652–657, 1973. Cerca con Google

[27] G. Aad and others, “Combined Measurement of the Higgs Boson Mass in pp Collisions at √s=7 and 8 TeV with the ATLAS and CMS Experiments,” Phys. Rev. Lett., vol. 114, p. 191803, 2015. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[30] C. W. Misner, K. S. Thorne, J. A. Wheeler, and D. I. Kaiser, Gravitation. Princeton University Press, 2017. Cerca con Google

[31] C. Rovelli, “Loop quantum gravity,” Living reviews in relativity, vol. 11, no. 1, p. 5, 2008. Cerca con Google

[32] J. Polchinski, String Theory. Cambridge: Cambridge Univ. Press, 1998. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[35] P. A. R. Ade and others, “Planck 2015 results. XIII. Cosmological parameters,” Astron. Astrophys., vol. 594, p. A13, 2016. Cerca con Google

[36] Y. Fukuda et al., “Evidence for oscillation of atmospheric neutrinos,” Physical Review Letters, vol. 81, no. 8, p. 1562, 1998. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[42] H.-Y. Cheng, “The strong cp problem revisited,” Physics Reports, vol. 158, no. 1, pp. 1–89, 1988. Cerca con Google

[43] T. Appelquist and J. Carazzone, “Infrared singularities and massive fields,” Phys. Rev. D, vol. 11, no. 10, pp. 2856–2861, May 1975. Cerca con Google

[44] W. Buchmuller and D. Wyler, “Effective Lagrangian Analysis of New Interactions and Flavor Conservation,” Nucl. Phys., vol. B268, pp. 621–653, 1986. Cerca con Google

[45] S. Weinberg, “Baryon-and lepton-nonconserving processes,” Physical Review Letters, vol. 43, no. 21, p. 1566, 1979. Cerca con Google

[46] R. D. Ball and others, “Parton distributions from high-precision collider data,” Eur. Phys. J., vol. C77, no. 10, p. 663, 2017. Cerca con Google

[47] G. Altarelli and G. Parisi, “Asymptotic Freedom in Parton Language,” Nucl. Phys., vol. B126, pp. 298–318, 1977. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[51] G. P. Lepage, “A New Algorithm for Adaptive Multidimensional Integration,” J. Comput. Phys., vol. 27, p. 192, 1978. Cerca con Google

[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. Cerca con Google

[53] C. Service graphique, “Overall view of the LHC. Vue d’ensemble du LHC,” Jun. 2014. Cerca con Google

[54] B. Wolf, Handbook of ion sources. CRC press, 2017. Cerca con Google

[55] T. Mc Cauley, “Collisions recorded by the CMS detector on 14 Oct 2016 during the high pile-up fill,” Nov-2016. Cerca con Google

[56] G. Aad and others, “The ATLAS Experiment at the CERN Large Hadron Collider,” JINST, vol. 3, p. S08003, 2008. Cerca con Google

[57] S. Chatrchyan and others, “The CMS Experiment at the CERN LHC,” JINST, vol. 3, p. S08004, 2008. Cerca con Google

[58] A. A. Alves Jr. and others, “The LHCb Detector at the LHC,” JINST, vol. 3, p. S08005, 2008. Cerca con Google

[59] K. Aamodt and others, “The ALICE experiment at the CERN LHC,” JINST, vol. 3, p. S08002, 2008. Cerca con Google

[60] G. Anelli and others, “The TOTEM experiment at the CERN Large Hadron Collider,” JINST, vol. 3, p. S08007, 2008. Cerca con Google

[61] O. Adriani and others, “The LHCf detector at the CERN Large Hadron Collider,” JINST, vol. 3, p. S08006, 2008. Cerca con Google

[62] B. Acharya and others, “The Physics Programme Of The MoEDAL Experiment At The LHC,” Int. J. Mod. Phys., vol. A29, p. 1430050, 2014. Cerca con Google

[63] G. L. Bayatian and others, CMS Physics: Technical Design Report Volume 1: Detector Performance and Software. Geneva: CERN, 2006. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[66] H. Spieler, Semiconductor detector systems, vol. 12. Oxford university press, 2005. Cerca con Google

[67] The CMS electromagnetic calorimeter project: Technical Design Report. Geneva: CERN, 1997. Cerca con Google

[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. Cerca con Google

[69] A. M. Sirunyan and others, “Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at √s=13 TeV,” JINST, vol. 13, no. 6, p. P06015, 2018. Cerca con Google

[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. Cerca con Google

[71] S. Agostinelli and others, “GEANT4: A Simulation toolkit,” Nucl. Instrum. Meth., vol. A506, pp. 250–303, 2003. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[78] R. Fruhwirth, W. Waltenberger, and P. Vanlaer, “Adaptive vertex fitting,” J. Phys., vol. G34, p. N343, 2007. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[82] C. Collaboration, “Pileup Removal Algorithms,” 2014. Cerca con Google

[83] M. Cacciari, G. P. Salam, and G. Soyez, “The anti-kt jet clustering algorithm,” JHEP, vol. 4, p. 063, 2008. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[86] M. L. Casado and others, “Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators,” 2017. Cerca con Google

[87] A. G. Baydin et al., “Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model,” 2018. Cerca con Google

[88] C. W. Gardiner, Handbook of stochastic methods: for physics, chemistry and the natural sciences; 3rd ed. Berlin: Springer, 2004. Cerca con Google

[89] R. V. Hogg and A. T. Craig, Introduction to mathematical statistics.(5th edition). Upper Saddle River, New Jersey: Prentice Hall, 1995. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[93] A. B. Owen, Monte carlo theory, methods and examples. 2013. Cerca con Google

[94] D. B. Rubin, “Bayesianly justifiable and relevant frequency calculations for the applies statistician,” The Annals of Statistics, pp. 1151–1172, 1984. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[101] A. L. Read, “Presentation of search results: The CL(s) technique,” J. Phys., vol. G28, pp. 2693–2704, 2002. Cerca con Google

[102] T. Junk, “Confidence level computation for combining searches with small statistics,” Nucl. Instrum. Meth., vol. A434, pp. 435–443, 1999. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[107] R. A. Fisher, “Theory of statistical estimation,” Mathematical Proceedings of the Cambridge Philosophical Society, vol. 22, no. 5, pp. 700–725, 1925. Cerca con Google

[108] H. Cramér, Mathematical methods of statistics (pms-9), vol. 9. Princeton university press, 2016. Cerca con Google

[109] C. R. Rao, “Information and the accuracy attainable in the estimation of statistical parameters,” in Breakthroughs in statistics, Springer, 1992, pp. 235–247. Cerca con Google

[110] P. S. Laplace, “Memoir on the probability of the causes of events,” Statistical Science, vol. 1, no. 3, pp. 364–378, 1986. Cerca con Google

[111] T. M. Mitchell, Machine learning, 1st ed. New York, NY, USA: McGraw-Hill, Inc., 1997. Cerca con Google

[112] V. N. Vapnik, “An overview of statistical learning theory,” IEEE transactions on neural networks, vol. 10, no. 5, pp. 988–999, 1999. Cerca con Google

[113] J. Friedman, T. Hastie, and R. Tibshirani, The elements of statistical learning, vol. 1. Springer series in statistics New York, NY, USA: 2001. Cerca con Google

[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. Cerca con Google

[115] I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT Press, 2016. Cerca con Google

[116] G. Louppe, “Understanding random forests: From theory to practice,” arXiv preprint arXiv:1407.7502, 2014. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[119] J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” Annals of statistics, pp. 1189–1232, 2001. Cerca con Google

[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. Cerca con Google

[121] L. Breiman, Classification and regression trees. Routledge, 2017. Cerca con Google

[122] L. Breiman, “Bagging predictors,” Machine learning, vol. 24, no. 2, pp. 123–140, 1996. Cerca con Google

[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. Cerca con Google

[124] J. Nocedal and S. J. Wright, Numerical optimization, Second. New York, NY, USA: Springer, 2006. Cerca con Google

[125] H. Robbins and S. Monro, “A stochastic approximation method,” The Annals of Mathematical Statistics, vol. 22, no. 3, pp. 400–407, 1951. Cerca con Google

[126] S. Ruder, “An overview of gradient descent optimization algorithms,” arXiv preprint arXiv:1609.04747, 2016. Cerca con Google

[127] G. Cybenko, “Approximation by superpositions of a sigmoidal function,” Mathematics of control, signals and systems, vol. 2, no. 4, pp. 303–314, 1989. Cerca con Google

[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. Cerca con Google

[129] M. Abadi et al., “TensorFlow: Large-scale machine learning on heterogeneous systems.” 2015. Cerca con Google

[130] A. Paszke et al., “Automatic differentiation in pytorch,” in NIPS-w, 2017. Cerca con Google

[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. Cerca con Google

[132] I. Henrion et al., “Neural message passing for jet physics,” 2017. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[136] L. de Oliveira, M. Kagan, L. Mackey, B. Nachman, and A. Schwartzman, “Jet-images — deep learning edition,” JHEP, vol. 7, p. 069, 2016. Cerca con Google

[137] F. Chollet and others, “Keras.” https://keras.io, 2015. Vai! Cerca con Google

[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. Cerca con Google

[139] M. Stoye, J. Kieseler, H. Qu, L. Gouskos, and M. Verzetti, “DeepJet: Generic physics object based jet multiclass classification for lhc experiments.” Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[142] I. Bird and R. W. Jones, “LHC computing grid: Technical design report,” 2005. Cerca con Google

[143] D. H. Guest et al., “Lwtnn/lwtnn: Version 2.8.” Nov-2018. Cerca con Google

[144] M. Rieger, “CMSSW-dnn.” https://gitlab.cern.ch/mrieger/CMSSW-DNN, 2017. Vai! Cerca con Google

[145] P. de Castro, M. Rieger, and others, “DeepJet integration.” https://github.com/cms-sw/cmssw/pull/19893, 2017. Vai! Cerca con Google

[146] M. Stoye and others, “DeepJet software framework.” https://github.com/mstoye/DeepJet, 2017. Vai! Cerca con Google

[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. Cerca con Google

[148] A. M. Sirunyan and others, “Search for nonresonant Higgs boson pair production in the Cerca con Google

bbbarbbbar final state at √s=13 TeV,” Submitted to: JHEP, 2018. Cerca con Google

[149] S. Chatrchyan and others, “Observation of a new boson with mass near 125 GeV in pp collisions at √s = 7 and 8 TeV,” JHEP, vol. 6, p. 081, 2013. Cerca con Google

[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 √s=7 and 8 TeV,” JHEP, vol. 8, p. 045, 2016. Cerca con Google

[151] A. M. Sirunyan and others, “Observation of ttbarH production,” Phys. Rev. Lett., vol. 120, no. 23, p. 231801, 2018. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[154] R. Grober and M. Muhlleitner, “Composite Higgs Boson Pair Production at the LHC,” JHEP, vol. 6, p. 020, 2011. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[159] A. M. Sirunyan and others, “Measurements of properties of the Higgs boson decaying into the four-lepton final state in pp collisions at √s=13 TeV,” JHEP, vol. 11, p. 047, 2017. Cerca con Google

[160] D. de Florian and others, “Handbook of LHC Higgs Cross Sections: 4. Deciphering the Nature of the Higgs Sector,” 2016. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[165] A. Carvalho et al., “Analytical parametrization and shape classification of anomalous HH production in the EFT approach,” 2016. Cerca con Google

[166] G. Aad and others, “Search for Higgs boson pair production in the bbbarbbbar final state from pp collisions at √s=8 Cerca con Google

TeV with the ATLAS detector,” Eur. Phys. J., vol. C75, no. 9, p. 412, 2015. Cerca con Google

[167] A. M. Sirunyan and others, “Search for Higgs boson pair production in the bbττ final state in proton-proton collisions at Cerca con Google

√(s)=8 TeV,” Phys. Rev., vol. D96, no. 7, p. 072004, 2017. Cerca con Google

[168] M. Aaboud and others, “Search for pair production of Higgs bosons in the Cerca con Google

bbbarbbbar final state using proton-proton collisions at √s=13 TeV with the ATLAS detector,” 2018. Cerca con Google

[169] A. M. Sirunyan and others, “Search for resonant and nonresonant Higgs boson pair production in the b bbarℓνℓν final state in proton-proton collisions at √s=13 TeV,” JHEP, vol. 1, p. 054, 2018. Cerca con Google

[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 √s=13TeV,” Phys. Lett., vol. B778, pp. 101–127, 2018. Cerca con Google

[171] A. M. Sirunyan and others, “Search for Higgs boson pair production in the γγbbbar final state in pp collisions at √s=13 TeV,” 2018. Cerca con Google

[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 √s=13 TeV,” 2018. Cerca con Google

[173] A. Falkowski, “Higgs Basis: Proposal for an EFT basis choice for LHC HXSWG,” Mar. 2015. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[178] R. D. Ball and others, “Parton distributions for the LHC Run II,” JHEP, vol. 4, p. 040, 2015. Cerca con Google

[179] S. Wertz and V. Lemaitre, “Search for Higgs boson pair production in the Cerca con Google

bbbarℓνℓν final state with the CMS detector,” 2018. Cerca con Google

[180] F. Pedregosa et al., “Scikit-learn: Machine learning in python,” Journal of machine learning research, vol. 12, no. Oct, pp. 2825–2830, 2011. Cerca con Google

[181] A. M. Sirunyan and others, “Measurement of the inelastic proton-proton cross section at √s=13 TeV,” JHEP, vol. 7, p. 161, 2018. Cerca con Google

[182] “CMS Luminosity Measurements for the 2016 Data Taking Period,” CERN, Geneva, CMS-PAS-LUM-17-001, 2017. Cerca con Google

[183] J. Butterworth and others, “PDF4LHC recommendations for LHC Run II,” J. Phys., vol. G43, p. 023001, 2016. Cerca con Google

[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. Cerca con Google

[185] “Combination of searches for Higgs boson pair production in proton-proton collisions at √s=13 TeV,” CERN, Geneva, CMS-PAS-HIG-17-030, 2018. Cerca con Google

[186] P. De Castro and T. Dorigo, “INFERNO: Inference-Aware Neural Optimisation,” 2018. Cerca con Google

[187] S. N. Wood, “Statistical inference for noisy nonlinear ecological dynamic systems,” Nature, vol. 466, no. 7310, p. 1102, 2010. Cerca con Google

[188] K. Cranmer, J. Pavez, and G. Louppe, “Approximating likelihood ratios with calibrated discriminative classifiers,” arXiv preprint arXiv:1506.02169, 2015. Cerca con Google

[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. Cerca con Google

[190] D. Basu, “On partial sufficiency: A review,” in Selected works of debabrata basu, Springer, 2011, pp. 291–303. Cerca con Google

[191] D. A. Sprott, “Marginal and conditional sufficiency,” Biometrika, vol. 62, no. 3, pp. 599–605, 1975. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[196] J. Brehmer, G. Louppe, J. Pavez, and K. Cranmer, “Mining gold from implicit models to improve likelihood-free inference,” 2018. Cerca con Google

[197] J. Brehmer, K. Cranmer, G. Louppe, and J. Pavez, “Constraining effective field theories with machine learning,” arXiv preprint arXiv:1805.00013, 2018. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Cerca con Google

[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. Vai! Cerca con Google

[202] J. V. Dillon et al., “TensorFlow distributions,” 2017. Cerca con Google

[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. Cerca con Google

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