Progress in the cone analysis: Optimization and systematic checks n Introduction n Performance n Systematic checks n Conclusions n Introduction n Performance n Systematic checks n Conclusions H. Ruiz (IFAE Barcelona), J. Nowell (Imperial College,London), F. Teubert, A. Moutoussi (CERN) ALEPH

Introduction largecorrelated n FSI uncertainty large and correlated between experiments. understand FSI reducing the systematic on m W n Effort has been put in trying to understand FSI, with the aim of reducing the systematic on m W. April n In April, some promising results based on different cone algorithms were shown during a LEP-WW workshop. –A reduction of ~2 in CR error was achieved with a 20% loss in stats n Since then: –The algorithm has been optimized (moved to hybrid cones) –Some new models have been tested. –Systematic checks have been made.

Attacking FSI Introduction: n Two different approaches: Use of observables to put a limit in the effects: 1) Use of observables to put a limit in the effects: From Q analysis, BE effect is expected to be drastically reduced (25MeV  ~5MeV) For CR only extreme models discarded (sk1 high kI)  second approach needed. Redesign the analysis to make it less sensitive to FSI 2) Redesign the analysis to make it less sensitive to FSI  optimize jets algorithm. Durham chosen by optimizing just statistical error n Requirements on the new algorithm: –Efficient against the different CR models –Minimum deviation from ‘standard’ analysis

The ‘new’ jet algorithm... n Idea: n Idea: FSI effects on m W come mainly from inter-jet region, where –Ambiguity in clustering can occur. –Momentum interchange is possible between particles from different Ws. –Multiplicity variations are stronger (particle flow analysis, CR). n Proposed solution: –Apply a jet algorithm that excludes information from the interjet region  cone algorithm. n Price to pay: loss of statistical power. Introduction:

The hybrid cone algorithm n The best performant cone algorithm tested is the hybrid: –Take the particles of a given Durham jet. –Find the cone of a given ‘radius’ that contains maximum momentum. –Recompute direction of jet excluding particles outside the cone. Particles outside are used for energy computation n Only one parameter: opening angle R –R can be tuned to optimize stat and syst combination. –In the limit of large R, ‘standard’ analysis is recovered. n Can be easily applied on Zs and semileptonic events for systematic checks. RIntroduction:

Performance against CR models  standard reference: 189GeV

Statistical degradation Performance : R=0.75

Performance with R=0.75

Some details... n Energy fraction inside the cone: n Stat error vs energy fraction in cone: R=0.75 Performance:

Higher energies...

For kI=0.65 no clear trend  shifts averaged

Statistical degradation No large variation

Performance: Impact in m W n Using data: n The systematic component of the error is strongly reduced with cones. n In all scenarios the total error is smaller. n The comparison gets better for the combination of the 4 experiments. very very preliminary

 M W vs E cm 100MeV200MeV Average: very preliminary

Limits on kappa? (R=0.75) kIkI kIkI Integral 22 Results compatible to No CR within ~1sigma, preferance for some CR No useful limit on kappa. very preliminary

Systematics n Cone algorithm may have different systematics than Durham. –Example: the angular distribution of EFs within a jet affects cone and Durham in different ways. n Some checks done: –Usual comparison Jetset-Herwig:  m W ~10MeV (=standard). –Apply cone analysis on semi-leptonic events. –Systematic data-MC comparison of: A) EF distributions. B) Jet properties. C) Effect of cones.

Semileptonics for the proposed R=0.75

A) Angular distribution of EFs around Durham jet axis  Angle to jet axis (rad) Energy (arbitr.) MC standard MC ski Data

Another angle... n Not only angle to jet axis matters: n ‘Azimutal symmetry’ of jet affected by: –jets nearby. –CR?  closest jet other W furthest jet other W other jet same W angle (rad)

…defining the angle...   angle to inter-jet plane rad  rad Same W 1 2 closest jet other W

 -angle for energy Energy (arbitr.) all EFs Adding momentum of all energy flows inside the cone (R=0.75) MC standard Data  angle (rad)

B) Jet properties Energy of the jets (GeV) Durham Cone MC standard MC ski Data

Azimuthal angle Azimutal angle of the jets (rad) Durham Cone MC standard MC ski Data

Jets mass Jet mass (GeV) Durham Cone MC standard MC ski Data

C) Effect of cones: momentum kick pp p dur p cone   p (GeV)  (rad) MC standard MC ski Data

… zoom on kicks...  p (GeV)  (rad) MC standard MC ski Data

On Zs  p (GeV)  (rad) pp p dur p cone  MC standard Data Herwig

On Zs  p (GeV)  (rad) pp p dur p cone  MC standard Data Herwig

Angular bias (on Z events) Durham R (rad) Durham Photons to charged: All neutral to charged: MC standard Data Angle between two components within a jet:

Conclusions n The cone analysis: –Reduces total m W error –Reduces strongly CR systematic yielding a more robust analysis n No evidence for additional systematics from: –Jetset vs Herwig –Semi-leptonic test –Hadronic Ws and on Zs n Plans: fine tuning of R & more MC Models (Ariadne)