Abstract: A strongly interacting dark sector can give rise to a class of signatures dubbed dark showers, where in analogy to the strong sector in the Standard Model, the dark sector undergoes its own showering and hadronization, before decaying into Standard Model final states. When the typical decay lengths of the dark sector mesons are larger than a few centimeters (and no larger than a few meters) they give rise to the striking signature of emerging jets, characterized by a large multiplicity of displaced vertices.In this article we consider the general reinterpretation of the CMS search for emerging jets plus prompt jets into arbitrary new physics scenarios giving rise to emerging jets. More concretely, we consider the cases where the SM Higgs mediates between the dark sector and the SM, for several benchmark decay scenarios. Our procedure is validated employing the same model than the CMS emerging jet search. We find that emerging jets can be the leading probe in regions of parameter space, in particular when considering the so-called gluon portal and dark photon portal decay benchmarks. With the current 16.1 fb-1 of luminosity this search can exclude down to O\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \mathcal{O} $$\end{document}(20)% exotic branching ratio of the SM Higgs, but a naive extrapolation to the 139 fb-1 luminosity employed in the current model-independent, indirect bound of 16 % would probe exotic branching ratios into dark quarks down to below 10 %. Further extrapolating these results to the HL-LHC, we find that one can pin down exotic branching ratio values of 1%, which is below the HL-LHC expectations of 2.5-4 %. We make our recasting code publicly available, as part of the LLP Recasting Repository.

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.