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Register a new userCP violation in a light Higgs boson decay from tau-spin correlations at a linear collider
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Andre Rouge
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We present a new method to measure the transverse spin correlation in the $Htotau^+tau^-$ decay. The method has been devised to be insensitive to the beamstrahlung which affects the definition of the beam energy at a linear collider. In the case of two $tautopi^ u$ decays, using the anticipated detector performance of the TESLA project, we get a promising estimation of the error expected on the measurement of a CP violating phase.
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This report summarizes the progress in the study of Higgs physics at a future linear electron positron collider at center-of-mass energies up to about 1000 GeV and high luminosity. After the publication of the TESLA Technical Design Report, an extended ECFA/DESY study on linear collider physics and detectors was performed. The paper summarizes the status of the studies with main emphasis on recent results obtained in the course of the workshop.
The di-photon decay channel of the lightest Higgs boson is considerd as a probe to explore CP violation in the Minimal Supersymmetric Standard Model (MSSM). The scalar/pseudo-scalar mixing is considered along with CP violation entering through the Higgs-sfermion-sfermion couplings, with and without light sparticles. The impact of a light stop on the decay width and Branching Ratio (BR) is established through a detailed study of the amplitude of the process H1 --> gamma.gamma. The other sparticles have little influence even when they are light. With a suitable combination of other MSSM parameters, a light stop can change the BR by more than 50 % with a CP-violating phase phi_mu ~ 90 deg., while the change is almost nil with a heavy stop.
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).
In the Standard Model, the Higgs boson is a CP even state with CP conserving couplings; any deviations from this would be a sign of new physics. These CP properties can be probed by measuring Higgs decays to tau lepton pairs: the transverse correlation between the tau spins depends on CP. This paper develops such an analysis, using full simulation of signal and background events in the International Large Detector concept for the International Linear Collider. We consider Higgs-strahlung events e+ e- -> Z H) in which the Z boson decays to electrons, muons, or hadrons, and the Higgs boson decays to $tau$ leptons, which then decay either to (pi+ nu) or (pi+ pi0 nu). Assuming 2 ab^{-1} of integrated luminosity at a center-of-mass energy of 250~GeV, the mixing angle psi_CP between even and odd CP components of the tau pair from Higgs boson decays can be measured to a precision of 75~mrad (4.3 deg).
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.
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