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We present a new tree boosting algorithm designed for the measurement of parameters in the context of effective field theory (EFT).
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J. Brehmer, K. Cranmer, G. Louppe, J. Pavez, A guide to constraining effective field theories with machine learning, Phys. Rev. D 98 (2018) 052004 · 2018
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P. De Castro, T. Dorigo, INFERNO: Inference-Aware Neural Optimisation, Comput. Phys. Commun. 244 (2019) 170 · 2019
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T. Chen, C. Guestrin, XGBoost: A scalable tree boosting system, KDD ’16, Association for Computing Machinery, 2016 · 2016
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J. Brehmer, K. Cranmer, G. Louppe, J. Pavez, Constraining effective field theories with machine learning, Phys. Rev. Lett. 121 (2018) 111801 · 2018
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J. Brehmer, G. Louppe, J. Pavez, K. Cranmer, Mining gold from implicit models to improve likelihood-free inference, Proc. Nat. Acad. Sci. 117 (2020) 5242 · 2020
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J. Ellis, M. Madigan, K. Mimasu, V. Sanz, T. You, Top, Higgs, diboson and electroweak fit to the standard model effective field theory, JHEP 04 (2021) 279 · 2021
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J. J. Ethier, G. Magni, F. Maltoni, L. Mantani, E. R. Nocera, J. Rojo, E. Slade, E. Vryonidou, C. Zhang, Combined SMEFT interpretation of Higgs, diboson, and top quark data from the LHC, JHEP 11 (2021) 089 · 2021
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J. J. Ethier, R. Gomez-Ambrosio, G. Magni, J. Rojo, SMEFT analysis of vector boson scattering and diboson data from the LHC Run II, Eur. Phys. J. C 81 (6) (2021) 560 · 2021
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Boosted Information Trees: Implementation in Python2 and Python3, https://github.com/BIT4EFT/BIT (2021)
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I. Brivio, SMEFTsim 3.0 — A practical guide, JHEP 04 (2021) 073 · 2021
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