|
|
Bas i Beneito, A., Gargalionis, J., Herrero-Garcia, J., Santamaria, A., & Schmidt, M. A. (2024). An EFT approach to baryon number violation: lower limits on the new physics scale and correlations between nucleon decay modes. J. High Energy Phys., 07(7), 004–37pp.
Abstract: Baryon number is an accidental symmetry of the Standard Model at the Lagrangian level. Its violation is arguably one of the most compelling phenomena predicted by physics beyond the Standard Model. Furthermore, there is a large experimental effort to search for it including the Hyper-K, DUNE, JUNO, and THEIA experiments. Therefore, an agnostic, model-independent, analysis of baryon number violation using the power of Effective Field Theory is very timely. In particular, in this work we study the contribution of dimension six and seven effective operators to |triangle(B – L)| = 0, 2 nucleon decays taking into account the effects of Renormalisation Group Evolution. We obtain lower limits on the energy scale of each operator and study the correlations between different decay modes. We find that for some operators the effect of running is very significant.
|
|
|
|
Bagnaschi, E., Ellis, J., Madigan, M., Mimasu, K., Sanz, V., & You, T. (2022). SMEFT analysis of m(W). J. High Energy Phys., 08(8), 308–22pp.
Abstract: We use the Fitmaker tool to incorporate the recent CDF measurement of mW in a global fit to electroweak, Higgs, and diboson data in the Standard Model Effective Field Theory (SMEFT) including dimension-6 operators at linear order. We find that including any one of the SMEFT operators O-HWB, O-HD, O (l) (l) or O ((3)) (H l) with a non-zero coefficient could provide a better fit than the Standard Model, with the strongest pull for O-HD and no tension with other electroweak precision data. We then analyse which tree-level single-field extensions of the Standard Model could generate such operator coefficients with the appropriate sign, and discuss the masses and couplings of these fields that best fit the CDF measurement and other data. In particular, the global fit favours either a singlet Z 0 vector boson, a scalar electroweak triplet with zero hypercharge, or a vector electroweak triplet with unit hypercharge, followed by a singlet heavy neutral lepton, all with masses in the multi-TeV range for unit coupling.
|
|
|
|
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.
|
|
