Progress in angular acceptance study Yuehong Xie University of Edinburgh 12 March 2009

Reminder of previous talk Talk in bs meeting 22 January 2009 Proposed a event-by-event method to include effects on angular distributions from detector geometry kinematic cuts particle efficiencies Discussed the possibility to calibrate particle efficiencies using control channels

This talk How well does our model work with fully simulated data? Predict Bs→J/yf average acceptance functions Develop methods to calibrate particle efficiencies

Full simulation test Test if the following factors can explain the observed angular acceptance detector geometry cuts on particle P and/or Pt dependence of particle reconstruction efficiency on kinematics (P and Pt) Use B+→J/yK+ cosqm in J/y rest frame w.r.t. B direction in this frame cosqK in B rest frame w.r.t. (0,0,1)

Detector geometry Only explicitly require charged tracks are inside Velo acceptance Pt/Pz >0.02 Pt/Pz <0.4 Effects of other detector geometrical acceptance enter the momentum-dependent particle efficiency

Explicit cuts Use standard DC06 selection Kaons: Pt > 1.3 GeV, P > 10 GeV Muons: Pt > 500 MeV J/y: Pt > 1 GeV

Particle efficiency e(P) Obtain efficiency as a function of P for muons and kaons respectively from MC truth muon kaon P (GeV) P (GeV)

Test method Get reconstructed distribution of an angle Follow steps in my previous talk to construct conditional pdf for each event Use known theoretical distributions Use per event angular acceptance function Sum of conditional pdf should match the reconstructed distribution if the considered factors in the per event angular acceptance model are adequate

cosqm known theoretical distribution: 1- cos2qm cosqm reconstructed sum of conditional pdf cosqm

Average efficiency of cosqm Relative difference from MC truth from conditional pdf cosqm cosqm

cosqK known theoretical distribution: flat cosqK reconstructed sum of conditional pdf cosqK

Average efficiency of cosqK from MC truth Relative difference from conditional pdf cosqK cosqK

Part I summary Our model describes the basic trend of the angular acceptance functions has prediction power Some fine tunings of the model are needed to reduce the ~10% relative difference e.g. consider particle efficiency as a function of P and Pt

Average acceptance in Bs →J/yf ~ 20% variation cosqtr cosy ftr

Effects of Pt/Pz>0.02 ~ 5% relative variation ftr cosqtr cosy

Effects of Pt/Pz<0.4 ~ 5% relative variation cosqtr cosy ftr

Effects of particle efficiencies < 5% relative variation cosqtr cosy ftr

Effects of Kinematic cuts ~20% relative variation cosqtr cosy ftr

Part II summary Shapes of angular acceptance functions in Bs→J/yf roadmap document are confirmed Decreasing order of importance Kinematic cuts Velo geometry Particle efficiencies

Obtain particle efficiency e(P) from B+→J/yK+ Assume an initial form of e(P) The P distribution of a certain particle can be predicted and compared with the reconstructed P distribution If the predicted and reconstructed P distributions don’t match, update e(P) Iterate until a perfect match

Iteration 0: start with em(P)=1 reconstructed predicted with em(P)=1 updated eff. muon P distributions in GeV em(P) for muons

Part III summary Have found a simple way to check consistency of assumed e(P) with data Aim to obtain e(P) w/o using MC truth w/o any assumption of B momentum spectrum A few steps away from success Still have some technical problems to get e(P) converged to the right form Non-trivial to deal with background