Abstract
Extracting maximal information from experimental data requires access to the likelihood function, which however is never directly available for complex experiments like those performed at high energy colliders. Theoretical predictions are obtained in this context by Monte Carlo events, which do furnish an accurate but abstract and implicit representation of the likelihood. Strategies based on statistical learning are currently being developed to infer the likelihood function explicitly by training a continuous-output classifier on Monte Carlo events. In this paper, we investigate the usage of Monte Carlo events that incorporate the dependence on the parameters of interest by reweighting. This enables more accurate likelihood learning with less training data and a more robust learning scheme that is more suited for automation and extensive deployment. We illustrate these advantages in the context of LHC precision probes of new Effective Field Theory interactions.
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J. Brehmer, K. Cranmer, G. Louppe and J. Pavez, Constraining Effective Field Theories with Machine Learning, Phys. Rev. Lett. 121 (2018) 111801 [arXiv:1805.00013] [INSPIRE].
J. Brehmer, K. Cranmer, G. Louppe and J. Pavez, A Guide to Constraining Effective Field Theories with Machine Learning, Phys. Rev. D 98 (2018) 052004 [arXiv:1805.00020] [INSPIRE].
J. Brehmer, G. Louppe, J. Pavez and K. Cranmer, Mining gold from implicit models to improve likelihood-free inference, Proc. Nat. Acad. Sci. 117 (2020) 5242 [arXiv:1805.12244] [INSPIRE].
J. Brehmer, F. Kling, I. Espejo and K. Cranmer, MadMiner: Machine learning-based inference for particle physics, Comput. Softw. Big Sci. 4 (2020) 3 [arXiv:1907.10621] [INSPIRE].
S. Chen, A. Glioti, G. Panico and A. Wulzer, Parametrized classifiers for optimal EFT sensitivity, JHEP 05 (2021) 247 [arXiv:2007.10356] [INSPIRE].
S. Chatterjee, S. Rohshap, R. Schöfbeck and D. Schwarz, Learning the EFT likelihood with tree boosting, arXiv:2205.12976 [INSPIRE].
S. Chatterjee et al., Tree boosting for learning EFT parameters, Comput. Phys. Commun. 277 (2022) 108385 [arXiv:2107.10859] [INSPIRE].
E. Arganda et al., A method for approximating optimal statistical significances with machine-learned likelihoods, Eur. Phys. J. C 82 (2022) 993 [arXiv:2205.05952] [INSPIRE].
E. Arganda, A.D. Perez, M. de los Rios and R.M. Sandá Seoane, Machine-learned exclusion limits without binning, Eur. Phys. J. C 83 (2023) 1158 [arXiv:2211.04806] [INSPIRE].
R. Gomez Ambrosio et al., Unbinned multivariate observables for global SMEFT analyses from machine learning, JHEP 03 (2023) 033 [arXiv:2211.02058] [INSPIRE].
K. Kong, K.T. Matchev, S. Mrenna and P. Shyamsundar, New Machine Learning Techniques for Simulation-Based Inference: InferoStatic Nets, Kernel Score Estimation, and Kernel Likelihood Ratio Estimation, arXiv:2210.01680 [INSPIRE].
J. Hollingsworth and D. Whiteson, Resonance Searches with Machine Learned Likelihood Ratios, arXiv:2002.04699 [INSPIRE].
S. Rizvi, M. Pettee and B. Nachman, Learning likelihood ratios with neural network classifiers, JHEP 02 (2024) 136 [arXiv:2305.10500] [INSPIRE].
K. Kondo, Dynamical Likelihood Method for Reconstruction of Events With Missing Momentum. 1: Method and Toy Models, J. Phys. Soc. Jap. 57 (1988) 4126 [INSPIRE].
P. Artoisenet, V. Lemaitre, F. Maltoni and O. Mattelaer, Automation of the matrix element reweighting method, JHEP 12 (2010) 068 [arXiv:1007.3300] [INSPIRE].
F. Fiedler, A. Grohsjean, P. Haefner and P. Schieferdecker, The Matrix Element Method and its Application in Measurements of the Top Quark Mass, Nucl. Instrum. Meth. A 624 (2010) 203 [arXiv:1003.1316] [INSPIRE].
T. Martini and P. Uwer, Extending the Matrix Element Method beyond the Born approximation: Calculating event weights at next-to-leading order accuracy, JHEP 09 (2015) 083 [arXiv:1506.08798] [INSPIRE].
T. Martini and P. Uwer, The Matrix Element Method at next-to-leading order QCD for hadronic collisions: Single top-quark production at the LHC as an example application, JHEP 05 (2018) 141 [arXiv:1712.04527] [INSPIRE].
J.M. Campbell, W.T. Giele and C. Williams, The Matrix Element Method at Next-to-Leading Order, JHEP 11 (2012) 043 [arXiv:1204.4424] [INSPIRE].
S. Prestel and M. Spannowsky, HYTREES: Combining Matrix Elements and Parton Shower for Hypothesis Testing, Eur. Phys. J. C 79 (2019) 546 [arXiv:1901.11035] [INSPIRE].
D. Atwood and A. Soni, Analysis for magnetic moment and electric dipole moment form-factors of the top quark via \({e}^{+}{e}^{-}\to t\overline{t }\), Phys. Rev. D 45 (1992) 2405 [INSPIRE].
M. Diehl and O. Nachtmann, Optimal observables for the measurement of three gauge boson couplings in e+e− → W+W−, Z. Phys. C 62 (1994) 397 [INSPIRE].
I. Dunietz et al., How to extract CP violating asymmetries from angular correlations, Phys. Rev. D 43 (1991) 2193 [INSPIRE].
A.S. Dighe, I. Dunietz and R. Fleischer, Extracting CKM phases and \({B}_{s}-\overline{B }s\) mixing parameters from angular distributions of nonleptonic B decays, Eur. Phys. J. C 6 (1999) 647 [hep-ph/9804253] [INSPIRE].
G. Durieux and Y. Grossman, Probing CP violation systematically in differential distributions, Phys. Rev. D 92 (2015) 076013 [arXiv:1508.03054] [INSPIRE].
G. Durieux, M. Perelló, M. Vos and C. Zhang, Global and optimal probes for the top-quark effective field theory at future lepton colliders, JHEP 10 (2018) 168 [arXiv:1807.02121] [INSPIRE].
J. Pretz and F. Müller, Extraction of Azimuthal Asymmetries using Optimal Observables, Eur. Phys. J. C 79 (2019) 47 [arXiv:1811.09452] [INSPIRE].
B. Bortolato, J.F. Kamenik, N. Košnik and A. Smolkovič, Optimized probes of CP -odd effects in the \(t\overline{t }h\) process at hadron colliders, Nucl. Phys. B 964 (2021) 115328 [arXiv:2006.13110] [INSPIRE].
D.A. Faroughy, J.F. Kamenik, N. Košnik and A. Smolkovič, Probing the CP nature of the top quark Yukawa at hadron colliders, JHEP 02 (2020) 085 [arXiv:1909.00007] [INSPIRE].
Y. Gao et al., Spin Determination of Single-Produced Resonances at Hadron Colliders, Phys. Rev. D 81 (2010) 075022 [arXiv:1001.3396] [INSPIRE].
J.S. Gainer et al., Exploring Theory Space with Monte Carlo Reweighting, JHEP 10 (2014) 078 [arXiv:1404.7129] [INSPIRE].
S. Frixione and B.R. Webber, Matching NLO QCD computations and parton shower simulations, JHEP 06 (2002) 029 [hep-ph/0204244] [INSPIRE].
S. Alioli, P. Nason, C. Oleari and E. Re, A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX, JHEP 06 (2010) 043 [arXiv:1002.2581] [INSPIRE].
R. Frederix et al., Higgs pair production at the LHC with NLO and parton-shower effects, Phys. Lett. B 732 (2014) 142 [arXiv:1401.7340] [INSPIRE].
K. Arnold, J. Bella, J. Bellm, J. Bozzi, M. Brieg, F. Campanario et al., REPOLO: REweighting POwheg events at Leading Order, https://www.itp.kit.edu/vbfnlo/wiki/lib/exe/fetch.php?media=documentation:repolo_1.0.pdf
O. Mattelaer, On the maximal use of Monte Carlo samples: re-weighting events at NLO accuracy, Eur. Phys. J. C 76 (2016) 674 [arXiv:1607.00763] [INSPIRE].
A. Andreassen and B. Nachman, Neural Networks for Full Phase-space Reweighting and Parameter Tuning, Phys. Rev. D 101 (2020) 091901 [arXiv:1907.08209] [INSPIRE].
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 07 (2014) 079 [arXiv:1405.0301] [INSPIRE].
C. Degrande et al., Automated one-loop computations in the standard model effective field theory, Phys. Rev. D 103 (2021) 096024 [arXiv:2008.11743] [INSPIRE].
G. Cowan, K. Cranmer, E. Gross and O. Vitells, Asymptotic formulae for likelihood-based tests of new physics, Eur. Phys. J. C 71 (2011) 1554 [Erratum ibid. 73 (2013) 2501] [arXiv:1007.1727] [INSPIRE].
J. Neyman and E.S. Pearson, On the Problem of the Most Efficient Tests of Statistical Hypotheses, Phil. Trans. Roy. Soc. Lond. A 231 (1933) 289 [INSPIRE].
A. Falkowski, M. Gonzalez-Alonso, A. Greljo and D. Marzocca, Global constraints on anomalous triple gauge couplings in effective field theory approach, Phys. Rev. Lett. 116 (2016) 011801 [arXiv:1508.00581] [INSPIRE].
D.R. Green, P. Meade and M.-A. Pleier, Multiboson interactions at the LHC, Rev. Mod. Phys. 89 (2017) 035008 [arXiv:1610.07572] [INSPIRE].
A. Butter et al., The Gauge-Higgs Legacy of the LHC Run I, JHEP 07 (2016) 152 [arXiv:1604.03105] [INSPIRE].
R. Franceschini et al., Electroweak Precision Tests in High-Energy Diboson Processes, JHEP 02 (2018) 111 [arXiv:1712.01310] [INSPIRE].
G. Panico, F. Riva and A. Wulzer, Diboson interference resurrection, Phys. Lett. B 776 (2018) 473 [arXiv:1708.07823] [INSPIRE].
A. Azatov, J. Elias-Miro, Y. Reyimuaji and E. Venturini, Novel measurements of anomalous triple gauge couplings for the LHC, JHEP 10 (2017) 027 [arXiv:1707.08060] [INSPIRE].
A. Azatov, D. Barducci and E. Venturini, Precision diboson measurements at hadron colliders, JHEP 04 (2019) 075 [arXiv:1901.04821] [INSPIRE].
J. Baglio, S. Dawson and S. Homiller, QCD corrections in Standard Model EFT fits to WZ and WW production, Phys. Rev. D 100 (2019) 113010 [arXiv:1909.11576] [INSPIRE].
A. Paszke et al., PyTorch: An Imperative Style, High-Performance Deep Learning Library, arXiv:1912.01703 [INSPIRE].
D.P. Kingma and J. Ba, Adam: A Method for Stochastic Optimization, arXiv:1412.6980 [INSPIRE].
DELPHES 3 collaboration, DELPHES 3, A modular framework for fast simulation of a generic collider experiment, JHEP 02 (2014) 057 [arXiv:1307.6346] [INSPIRE].
R.T. d’Agnolo et al., Learning new physics from an imperfect machine, Eur. Phys. J. C 82 (2022) 275 [arXiv:2111.13633] [INSPIRE].
A. Kusina et al., nCTEQ15 — Global analysis of nuclear parton distributions with uncertainties, PoS DIS2015 (2015) 041 [arXiv:1509.01801] [INSPIRE].
D.B. Clark, E. Godat and F.I. Olness, ManeParse: A Mathematica reader for Parton Distribution Functions, Comput. Phys. Commun. 216 (2017) 126 [arXiv:1605.08012] [INSPIRE].
T. Sjostrand, S. Mrenna and P.Z. Skands, PYTHIA 6.4 Physics and Manual, JHEP 05 (2006) 026 [hep-ph/0603175] [INSPIRE].
T. Sjostrand, S. Mrenna and P.Z. Skands, A Brief Introduction to PYTHIA 8.1, Comput. Phys. Commun. 178 (2008) 852 [arXiv:0710.3820] [INSPIRE].
Acknowledgments
A.W. is supported by the grant PID2020-115845GB- I00/AEI/10.13039/501100011033. G.P. was supported in part by the MIUR under contract 2017FMJFMW (PRIN2017).
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Chen, S., Glioti, A., Panico, G. et al. Boosting likelihood learning with event reweighting. J. High Energ. Phys. 2024, 117 (2024). https://doi.org/10.1007/JHEP03(2024)117
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DOI: https://doi.org/10.1007/JHEP03(2024)117