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Extracting LER and HER bunch lengths from luminous region, using BaBar data

Extracting LER and HER bunch lengths from luminous region, using BaBar data. B. VIAUD, C. O’Grady U. Montr é al, SLAC With the great help of Witold Kozanecki !. Overview. Fit the Z-distribution

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Extracting LER and HER bunch lengths from luminous region, using BaBar data

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  1. Extracting LER and HER bunch lengths from luminous region, using BaBar data B. VIAUD, C. O’Grady U. Montréal, SLAC With the great help of Witold Kozanecki !

  2. Overview • Fit the Z-distribution • Z-distribution width behavior versus bunch number • Acceptance and resolution effects study • Conclusion

  3. Fit the theoretical distribution of the Z of the e+ e- collisions Z Distribution Formula Number of particles per bunch, Zc : Z where the bunchs meet Allowed to float

  4. 2.5 Million events: Bhabha and mumu • Tried to select periods where Zc is ~ constant: 0.7mm peak to peak • (have studied this with Toy Monte-Carlo by moving Zc 4mm) Data Sample <Z> mm June 10th time june 27th

  5. Fit of the measured Z distribution • Check the theoretical distribution can describe the shape of the data. • The data have not the same shape as the theoretical formula! Changing does not improve the agreement. (The c2 minimum depends only on ) s ~ 7.25 mm c2 ~13 Z [mm]

  6. Fit of the measured Z distribution • Better data/theory agreement if the waists Z-position or b*(y) are allowed to float in the fit • Waists Z positions / b*(y) values seem unlikely ! • Are they real ? Which other effect could simulate this lack of focalisation ?? c2 ~2.2 c2 ~2.4 Z [mm] Z [mm]

  7. Fit with a bunch longitudinal asymmetry • Better data/theory agreement with the following PDF : • when Zc , the waists Z-position ( common to HER and LER ) and the asymmetry factor A are allowed to float in the fit. Still strange waists. • Real ? c2 ~3.1

  8. Z and Z RMS variation as a function of the bunch number Z [mm] RMS Z [mm] 0 3492 Bunch number

  9. Z and Z RMS variation as a function of the bunch number • Origin of Z-RMS variation? Possible reasons: • Zc moving (around the waist) with bucket number: Studied this, by itself it can’t explain the observed RMS behaviour. • Bucket dependent bunch lengths (not studied yet) • These (Zc motion and RMS variation) could mess up the fit! • Fit with only buckets in 0.5us of the turn. Same answers: • Toy Monte-Carlo also excludes Zc/RMS motion as cause of discrepencey

  10. Resolution / acceptance effects • Resolution and acceptance depend on the angle between the e (μ) and the Z axis: • => Select a zone where the resolution is constant and • where the acceptance as a function of Z is constant • (Don’t understand Data/MC disagreement: mumu(MC) vs. Bhabha(Data)?) Data MC < Z> [mm] <Z> [mm] tan(λ1) tan(λ1)

  11. Resolution / acceptance effects • Fit of the Z distribution afer tan(λ1) cut • fixed waists Z position and Beta functions : • data/theory discrepancy still important : χ2 = 15 • floating waists Z position • floating Beta functions c2 ~1.6 Z [mm]

  12. Discrepancy between data and theory in Z distribution. Big, on scale of SVT resolution (~100mm). • One effect mimics a lack of focalisation (important!) in the data: • correct description of the data by the fit if the waists are very distant, • or if the b*(y) are 50% higher than expected • Or if we use a PDF accounting for a longitudinal bunch asymmetry • Variation of Z-distribution RMS as a function of the bunch number : not understood • not due to the variation of Zc around Z-waist • TOY MC studies show this effect cannot mess up the fit • At first order, does not seem to be due to a resolution/acceptance effect • One of our next main steps will be to use a complete MC simulation to evaluate precisely the bias introduced by the reconstruction and selection. • Ideas ? Suggestions ? Conclusion

  13. TOY MC studies • Can the Zc and Z-RMS variations mimic a lack of focalisation and/or mess up the fit enough to obtain • ?? • Performed a TOY MC study to check the effects of Zc and Z-RMS variations on the results of the fit • Generate two samples, with both waists at Z=0, Beta functions at 10.5 mm, and: • ADD the two samples and fit Zc and Z RMS variations can’t explain the waists’ Z position and Beta functions results

  14. Performed a TOY MC study to check the effects of Zc and Z-RMS variations on the results of the fit • Generate two samples, with both waists at Z=0, Beta functions at 10.5 mm, and: • Gather the two samples and fit • Floating Z-waists: • Floating Beta*: TOY MC studies (II)

  15. Performed a TOY MC study to check the effects of Zc and Z-RMS variations on the results of the fit • Generate two samples, with both waists at Z=0, Beta functions at 10.5 mm, and: • Gather the two samples and fit • Floating Z-waists: • Floating Beta*: TOY MC studies (III) Zc and Z RMS variations can’t explain the waists’ Z and Beta functions results

  16. Z RMS wrt the bunch number: theory prediction • Study directly Zc, given by the fit, with Z-waists fixed at 0 mm and (-2,2) mm • With the values of Zc and of the bunch length ( XX mm ) found by the fit on the global sample, use the theoretical formula to find the theoretical RMS (green dots) Zc [mm] Zc [mm] Z waists at (0,0) mm Z waists at (-2,2) mm RMS Z [mm] RMS Z [mm] 0 3492 Bunch number 0 3492 Bunch number

  17. Z RMS wrt the bunch number: theory prediction • Best case: Z-waists fixed at (+2.5,+2.5) mm Zc [mm] Z-RMS [mm] | | 0 3492 Bunch number

  18. Z RMS wrt the bunch number: only theory prediction RMS Z [mm] RMS Z [mm] Theory Data (Z0,HER;Z0,LER) = (0;0),(-2;2),(-5;5) ,(-10;10) mm Zc [mm] Zc [mm] Z-RMS variation wrt bunch number can’t be explained by the variation of Zc around the waists.  We can’t use this variation to find the waists Z-position.

  19. Z RMS wrt the bunch number: theory prediction • Z-waists fixed at (-5,5) mm and (-10,10) mm Zc [mm] Zc [mm] RMS Z [mm] RMS Z [mm] 0 3492 Bunch number 0 3492 Bunch number

  20. Z RMS wrt the bunch number: theory prediction • Z-waists fixed at (0,0) mm, after the tan(λ1) cut Zc [mm] Z RMS variation is not an acceptance effect. Z-RMS [mm] | | 0 3492 Bunch number

  21. Simultaneous fit of the 2 distributions measured before and after RF voltage change : • One c2 per distribution : fit and to minimise • Use the following constraint : Reminder: principles (II) 3.8 MV 3.2 MV s ~ 7.2 mm s ~ 7.6 mm -30 0 30 -30 0 30 Z [mm] Z [mm]

  22. Reminder: TOY MC • A TOY Monte Carlo has been written to validate the method. • Generated distributions with • Perform the fit, obtain : • avec c2 = 1.15 • => Seems to work HER (x/y) 32.2 / 10.6 mm 30 / 1.05 nm LER (x/y) 32.2 / 10.6 mm 31.3 / 10.3 nm

  23. Reminder: Fit to the data • with c2 = 14 !! • => Does not match the data. 3.8 MV Z [mm]

  24. first step : looked at Z vs cos(theta) Resolution effects Z [mm] Cos(theta)

  25. Beamspot from Bhabhas and mm • Aim : providing each 10 minutes a measurement of the • centroids, sizes and X-Z tilt of the interaction region • => Need to get online a sample of well reconstructed primary vertices • Take events from 28 ( out of 30 ) L3 executables (via the trickle stream ), and perform tracking using 28 executables on 8 dual processor nodes • tracks reconstructed using DCH and SVT • Select two-prong events • Reconstruct the two tracks common vertex • centroids calculated in the SVT frame (attached to the beam pipe) • All our results are obtained in that frame (not in MCC frame) • Accumulate samples of ~5000 vertices between 2 updates • ( 10 minutes of data taking ). Interaction region seen as the (X,Y,Z) • distribution of primary vertices • Results made available both in EPICS and in the AmbientDataBase

  26. Sensitivity to RF voltage modifications s Z mm 0.05 mm June 7th temps june 12th

  27. Cuts used to select two-prong events • See TrkOprMon/TrkTwoProngSelector.cc • Take the list of reconstructed tracks TrkSelTracks • Select the 2 tracks with the highest |p| • For these 2 tracks, calculate docaXY, docaZ, tanDip, and x,y,z position at POCA • Reconstruct the difference between the 2 Z at poca, and deltado (seem to be impact param of one track wrt the other, check) • Reconstruct the boost of the current event, from the sum of the above momenta: magnitude of the boost and x,y slopes • Go to the frame which Z axis is parallel to the boost, and reconstruct the diff between the PT (the same than in CMS), the difference between the phi in this new frame, the Delta D (see later the def) • Boost to CMS, reco angle between tracks, diff in |p|, inv Mass.

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