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1 What I did last year Nick Barlow Manchester Christmas meeting January 2005.

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Presentation on theme: "1 What I did last year Nick Barlow Manchester Christmas meeting January 2005."— Presentation transcript:

1 1 What I did last year Nick Barlow Manchester Christmas meeting January 2005

2 2 BaBar EMC stuff BaBar ElectroMagnetic Calorimeter system consists of: 6580 CsI(Tl) crystals in a barrel+endcap arrangement Readout electronics Cooling systems Lightpulser calibration/diagnostic system Monitoring hardware and software I was EMC operations manager from Jan-July Look after day-to-day operations of whole system Attend daily operations meeting to report on EMC problems/activities/plans Coordinate activities on Repair Opportunity Days I have been EMC trickle injection “expert” since last December PEP nowadays fills both electron and positron beams continuously to keep beam currents constant Implemented monitoring code to check data quality with trickle injection

3 3 EMC background studies In January, BaBar recorded special runs to study backgrounds Some runs with High Energy Ring (HER) only, some with Low Energy Ring (LER) only, some with both Look at backgrounds (number of EMC hits, EMC clusters, in random triggers) as function of HER current, LER current, luminosity Can then extrapolate to higher beam currents and luminosity

4 4 EMC background studies(2) Also look at where in the calorimeter the background hits from different sources are located: Christina Edgar is becoming the new EMC background expert LER only HER only Lumi only Total, extrapolated to predicted 2007 lumi

5 5 EMC projectivity correction In the past, positions of EMC hits (and the centroid of EMC clusters) were assumed to be at the front face of crystals Introduces a slight shift in theta because the crystals do not point back towards interaction point Corrected for by simple calibration function In latest software release, cluster position is taken to be 12.5cm into crystal “projectivity correction needed to be reevaluated New (much smaller) correction loaded into “conditions database”

6 6 B  J/  hh analysis Me, Yaw Ming Chia

7 7 Previous BaBar and Belle B 0  J/  +  - analyses BaBar (J.Boyd, J. Weatherall), 51fb -1 : BR(B 0  J/  +  - ) tot = (4.6±0.9)*10 -5 BR(B 0  J/   ) = (1.6 ±0.7)*10 -5 Belle Belle(ICHEP 04), 120fb -1 : BR(B 0  J/   ) = (2.8±0.4)*10 -5 BR(B 0  J/  f 2 ) < 1.5*10 -5 (90%CL) BR(B 0  J/  +  - ) nr <1.0*10 -5 (90%CL)

8 8 Analysis overview Will use all BaBar data up to summer 2005 (~300fb -1 ) Using BaBar’s new computing model (CM2) Eventstore based on ROOT I/O Centralised “skim” production – loose event selection, does combinatorics to reconstruct B candidates and stores them in skimmed collections Either run ntuple-making code, or use “interactive Kanga” – new tool to directly access skimmed collections Perform “blind” analysis – do not look at signal region in data until selection is finalised and systematics understood (as far as possible)

9 9 Selection issues Vertex cut: Previous BaBar analysis reconstructed  +  - as a  candidate, and cut on the difference of location of the  and J/  vertices We are currently reconstructing as 3 body decay B  J/   and cutting on  2 probability of vertex fit Better background rejection, but possibly worse data/MC agreement Pi0 selection: With current test skim selection (loose lepton PID, pi0Loose list) get LOTS of fake pi0s Select almost 50% of inclusive Jpsi MC, 20-30 B cands per event PID: Currently investigating whether to use pion ID for pion candidates, or require that they fail kaon ID criteria

10 10 Fit Using RooFit – unbinned maximum likelihood fitting package based on ROOT Perform 2D fit to beam-energy-substituted B mass (m ES ) and mass of hh system Combinatorial bg B  J/   non-res B  J/  Ks B  J/  

11 11 Short term plans Optimise selection cuts to maximise signal significance S/sqrt(S+B) Need to finalise loose “skim” cuts before 1 st February Generate MC for modes not already covered: J/  f 2, J/  +  0 non-res, J/  K + K - non-res Validate likelihood fit using toy MC, and (full+toy) MC experiments Look at systematics due to data/MC agreement in PID, pi0 selection, tracking etc. Eventually, “unblind”, look at data, measure Branching Fractions, then think about angular analysis etc.


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