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Caron, S., Ruiz de Austri, R., & Zhang, Z. Y. (2023). Mixture-of-Theories training: can we find new physics and anomalies better by mixing physical theories? J. High Energy Phys., 03(3), 004–37pp.
Abstract: Model-independent search strategies have been increasingly proposed in recent years because on the one hand there has been no clear signal for new physics and on the other hand there is a lack of a highly probable and parameter-free extension of the standard model. For these reasons, there is no simple search target so far. In this work, we try to take a new direction and ask the question: bearing in mind that we have a large number of new physics theories that go beyond the Standard Model and may contain a grain of truth, can we improve our search strategy for unknown signals by using them “in combination”? In particular, we show that a signal hypothesis based on a large, intermingled set of many different theoretical signal models can be a superior approach to find an unknown BSM signal. Applied to a recent data challenge, we show that “mixture-of-theories training” outperforms strategies that optimize signal regions with a single BSM model as well as most unsupervised strategies. Applications of this work include anomaly detection and the definition of signal regions in the search for signals of new physics.
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Gomez Ambrosio, R., ter Hoeve, J., Madigan, M., Rojo, J., & Sanz, V. (2023). Unbinned multivariate observables for global SMEFT analyses from machine learning. J. High Energy Phys., 03(3), 033–66pp.
Abstract: Theoretical interpretations of particle physics data, such as the determination of the Wilson coefficients of the Standard Model Effective Field Theory (SMEFT), often involve the inference of multiple parameters from a global dataset. Optimizing such interpretations requires the identification of observables that exhibit the highest possible sensitivity to the underlying theory parameters. In this work we develop a flexible open source frame-work, ML4EFT, enabling the integration of unbinned multivariate observables into global SMEFT fits. As compared to traditional measurements, such observables enhance the sensitivity to the theory parameters by preventing the information loss incurred when binning in a subset of final-state kinematic variables. Our strategy combines machine learning regression and classification techniques to parameterize high-dimensional likelihood ratios, using the Monte Carlo replica method to estimate and propagate methodological uncertainties. As a proof of concept we construct unbinned multivariate observables for top-quark pair and Higgs+Z production at the LHC, demonstrate their impact on the SMEFT parameter space as compared to binned measurements, and study the improved constraints associated to multivariate inputs. Since the number of neural networks to be trained scales quadratically with the number of parameters and can be fully parallelized, the ML4EFT framework is well-suited to construct unbinned multivariate observables which depend on up to tens of EFT coefficients, as required in global fits.
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Gariazzo, S., Martinez-Mirave, P., Mena, O., Pastor, S., & Tortola, M. (2023). Non-unitary three-neutrino mixing in the early Universe. J. Cosmol. Astropart. Phys., 03(3), 046–18pp.
Abstract: Deviations from unitarity in the three-neutrino mixing canonical picture are expected in many physics scenarios beyond the Standard Model. The mixing of new heavy neutral leptons with the three light neutrinos would in principle modify the strength and flavour structure of charged-current and neutral-current interactions with matter. Non-unitarity effects would therefore have an impact on the neutrino decoupling processes in the early Universe and on the value of the effective number of neutrinos, Neff. We calculate the cosmological energy density in the form of radiation with a non-unitary neutrino mixing matrix, addressing the possible interplay between parameters. Highly accurate measurements of Neff from forthcoming cosmological observations can provide independent and complementary limits on the departures from unitarity. For completeness, we relate the scenario of small deviations from unitarity to non-standard neutrino interactions and compare the forecasted constraints to other existing limits in the literature.
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Alvarez, M., Cantero, J., Czakon, M., Llorente, J., Mitov, A., & Poncelet, R. (2023). NNLO QCD corrections to event shapes at the LHC. J. High Energy Phys., 03(3), 129–24pp.
Abstract: In this work we perform the first ever calculation of jet event shapes at hadron colliders at next-to-next-to leading order (NNLO) in QCD. The inclusion of higher order corrections removes the shape difference observed between data and next-to-leading order predictions. The theory uncertainty at NNLO is comparable to, or slightly larger than, existing measurements. Except for narrow kinematical ranges where all-order resummation becomes important, the NNLO predictions for the event shapes considered in the present work are reliable. As a prime application of the results derived in this work we provide a detailed investigation of the prospects for the precision determination of the strong coupling constant and its running through TeV scales from LHC data.
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Baeza-Ballesteros, J., Bijnens, J., Husek, T., Romero-Lopez, F., Sharpe, S. R., & Sjo, M. (2024). The three-pion K-matrix at NLO in ChPT. J. High Energy Phys., 03(3), 048–43pp.
Abstract: The three-particle K-matrix, K-df,K-3, is a scheme-dependent quantity that parametrizes short-range three-particle interactions in the relativistic-field-theory three-particle finite-volume formalism. In this work, we compute its value for systems of three pions in all isospin channels through next-to-leading order in Chiral Perturbation Theory, generalizing previous work done at maximum isospin. We obtain analytic expressions through quadratic order (or cubic order, in the case of zero isospin) in the expansion about the three-pion threshold.
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