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Register a new userLHC Higgs CP Sensitive Observables in H to tau^+ tau^-; tau^pm to (3pi)^pm nu and Machine Learning Benefits
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In phenomenological preparation for new measurements one searches for the carriers of quality signatures. Often, the first approach quantities may be difficult to measure or to provide sufficiently precise predictions for comparisons. Complexity of necessary details grow with precision. To achieve the goal one can not break the theory principles, and take into account effects which could be ignored earlier. Mixed approach where dominant effects are taken into account with intuitive even simplistic approach was developed. Non dominant corrections were controlled with the help of Monte Carlo simulations. Concept of Optimal Variables was successfully applied for many measurements. New techniques, like Machine Learning, offer solutions to exploit multidimensional signatures. Complementarity of these new and old approaches is studied for the example of Higgs Boson CP-parity measurements in H to tau^+tau^-, tau^pm to nu (3pi)^pm cascade decays.
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Machine Learning (ML) techniques are rapidly finding a place among the methods of High Energy Physics data analysis. Different approaches are explored concerning how much effort should be put into building high-level variables based on physics insight into the problem, and when it is enough to rely on low-level ones, allowing ML methods to find patterns without explicit physics model. In this paper we continue the discussion of previous publications on the CP state of the Higgs boson measurement of the H to tau tau decay channel with the consecutive tau^pm to rho^pm nu; rho^pm to pi^pm pi^0 and tau^pm to a_1^pm nu; a_1^pm to rho^0 pi^pm to 3 pi^pm cascade decays. The discrimination of the Higgs boson CP state is studied as a binary classification problem between CP-even (scalar) and CP-odd (pseudoscalar), using Deep Neural Network (DNN). Improvements on the classification from the constraints on directly non-measurable outgoing neutrinos are discussed. We find, that once added, they enhance the sensitivity sizably, even if only imperfect information is provided. In addition to DNN we also evaluate and compare other ML methods: Boosted Trees (BT), Random Forest (RF) and Support Vector Machine (SVN).
The hitherto unobserved purely leptonic decays B^pm -> tau^pm u and B^pm -> mu^pm u are of much interest at current and future runs of the e+e- B factories. Such decays are sensitive to charged Higgs bosons (H^pm) at the tree-level and provide essentially model independent constraints on tanbeta/m_{H^pm}. We include the large corrections to the H^pm-ub coupling induced by virtual SUSY effects and show that the bounds on tanbeta/m_{H^pm} can be significantly weakened or strengthened.
The consecutive steps of cascade decay initiated by H to tau tau can be useful for the measurement of Higgs couplings and in particular of the Higgs boson parity. In the previous papers we have found, that multi-dimensional signatures of the tau^pm to pi^pm pi^0 nu and tau^pm to 3pi^pm nu decays can be used to distinguish between scalar and pseudoscalar Higgs state. The Machine Learning techniques (ML) of binary classification, offered break-through opportunities to manage such complex multidimensional signatures. The classification between two possible CP states: scalar and pseudoscalar, is now extended to the measurement of the hypothetical mixing angle of Higgs boson parity states. The functional dependence of H to tau tau matrix element on the mixing angle is predicted by theory. The potential to determine preferred mixing angle of the Higgs boson events sample including $tau$-decays is studied using Deep Neural Network. The problem is adressed as classification or regression with the aim to determine the per-event: a) probability distribution (spin weight) of the mixing angle; b) parameters of the functional form of the spin weight; c) the most preferred mixing angle. Performance of methods are evaluated and compared. Numerical results are collected.
In this paper, we discuss application of the TauSpinner package as a simulation tool for measuring the CP state of the newly discovered Higgs boson using the transverse spin correlations in the H to tau tau decay channel. We discuss application for its main background Z/gamma* to tau tau as well. The TauSpinner package allows one to add, with the help of weights, transverse spin correlations corresponding to any mixture of scalar/pseudoscalar state, on already existing events using information from the kinematics of outgoing tau leptons and their decay products only. This procedure can be used when polarimetric vectors of the taus decays and density matrix for tau-pair production are not stored with the event sample. We concentrate on the well-defined effects for the Higgs (or Higgs-like scalar) decays, which are physically separated from the production processes. TauSpinner also allows to reintroduce (or remove) spin correlations to events from Drell-Yan Z/gamma* to tau tau process, the main background for the Higgs parity observables, again with the help of weights only. From the literature, we recall well-established observables, developed for measuring the CP of the Higgs, and use them as benchmarks for illustrating applications of the TauSpinner package. We also include a description of the code and prepared testing examples.
We derive the decay widths for the leptonic decays of heavy charged pseudoscalars to massive sterile neutrinos, $M^{pm} to ell^{pm} + N$, within the frameworks involving the Standard Model and two-Higgs doublets (type II). We then apply the result to $B^pm to tau^pm +$ missing momentum of the Belle/BaBar experimental results, in order to measure directly the relevant parameter space, including the mixing parameter $U_{tau N}$.
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