1 / 21

K tracking efficiency & geometrical acceptance : a K (p K , q K )

K tracking efficiency & geometrical acceptance : a K (p K , q K ). We use the tag in the handle emisphere to have in the signal emisphere a “ pure” beam of K + (K - ) The signal is flagged as Kaon with standard cut on momentum and IP distance

marina
Download Presentation

K tracking efficiency & geometrical acceptance : a K (p K , q K )

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. K tracking efficiency & geometrical acceptance : aK(pK,qK) • We use the tag in the handle emisphere to have in the signal emisphere a “pure” beam of K+(K-) • The signal is flagged as Kaon with standard cut on momentum and IP distance • Background to the signal is mainly due to early 3 body decay of the K, whose secondary can mimic a K • We use the minimum distance between the signal track and the extrapolated track from the handle as check parameter • The shape of the DR distribution forbackgroundis taken fromMC • “ “ for signal is taken from MC and from double tagged event

  2. signal Handle K track extrapolated aK(pK,qK) signal selection Once we found a “signal” K compute the distance of closest approch between the first hit of its track and the track extrapolated from the handle: Four K definition cuts : 1) q opposite to the “handle” 2) 70 < PK < 130 MeV 3) Rpca < 10 cm 4) -20 < zpca < 20 cm We monitor the background contamination of the signal looking at the tracks minimum distance computed at the point of closest aproach.

  3. K track eff. = fit to Dr BLUE  K from MC RED  K from 2 tag GREEN  bck from MC The fit to the distribution of the distance of minimum approach between the signal track and the extrapolated track is made using MC and 2 tag shape for the signal and MC for the background shape Dr (cm)

  4. K shape uncertainties The Dr distribution in the K region is slightly overestimated by the fit with K shape from MC and underestimated by the fit with the K shape from 2 tag. The differences between the 2 fits gives the sistematic on the K shape Fit – signal : MC shape Dr (cm) signal Fit – signal : 2tag shape Dr (cm) Dr (cm)

  5. aK- versus time Handle : K+ Signal : K- aK- 2001 We check the stability of aK versus time. The 2001-2002 data were divided in chunk of  6 pb-1each. The two different results account for the 2 different shape choice for the K contribution. 2002 IntLum/6 (pb-1)

  6. Sistematic : handle tag Systematic on the K tracking eff. can be due to what happen in the opposite emisphere. Thus we measured the tracking efficiency with respect to the kind of handle tag aK- BLUE: Kpp0 RED : Kmn BLACK: all tag All the variations seem to be within statistical error. There is no evidence for dependence of the eff from the handle tag. IntLum/5 (pb-1)

  7. aK+vsaK- BLUE = aK+ RED = aK- aK The nuclear interactions of K- in the beam pipe and in the DC wall reduce aK-in comparison to aK+ by more than 1 % IntLum/6pb-1

  8. aK+ with respect qK and pK We divide the qK in 6 bin in the range 30< qK <90 and the K momentum in 6 bin in the range 70< pK<130 (Mev/c) aK- Nevents qbin Pbin qbin qbin = 10 deg Pbin = 10 MeV/c Pbin

  9. Summary • The K tracking efficiency times the geometrical acceptance aK has been measured using the tag tecnique at fraction of % level • The aK has been measured independently for positive and negative K • The sistematics due to the uncertainty on shape of the signal and due to tag bias have been evaluated • The aK has been measured versus the time in step of  6pb-1 • A memo is in preparation

  10. Tag Background evaluation The use of the K+(K-) tag decay ( Kmn and Kpp0) allow us to select a pure K-(K+) beam. Eventual pollution of the tag reflects in a systematic underestimation of the absolute BR measured. We made a first attempt to estimated this background using a sample of 4 pb-1 of 2002 data • We assumed that the background fraction in the events with one tag decay is small. • There is no background in the events where both K+ and K- undergo a tag decay (double tagged events) • We compare the single and double tag kinematic distribution: the differences can be due to the background ( and , to some extent, to slightly different acceptance ) • The statistical power of this analysis is limited by the rate of double tagged decay in K+K- events ( 10% of the total in the stream)

  11. Tag bck: Kinematic variables • The control variables was chosen both in the lab and in the center of mass frame: • Momentum of the K charged secondary in the K frame • Angle between the K flight path and the charged secondary in the K frame • Angle between the charge secondary and the K in the lab • Number of clusters associated at the K decay product ( ≤1 for Kmn and ≤3 for Kpp0) • Energy of the cluster associated to the charged secondary • Time of flight of the charged secondary Only the shape can be compared due to the different yelds of single and double tag events

  12. Charged secondary momentum in K frame Linear scale Log scale Mev/c Mev/c Normalized comparison between single and double tag events Red = difference of the 2 histo Blue = statistic uncertainty Mev/c

  13. Cos(q) between K and secondary in K frame Linear scale Log scale Red = difference of the 2 histo Blue = statistic uncertainty

  14. Cos(q) between K and secondary in lab frame Linear scale Log scale

  15. Number of secondary cluster associated Linear scale Log scale Ncluster ≤1 for Kmn Ncluster ≤3 for Kpp0 Red = difference of the 2 histo Blue = statistic uncertainty

  16. Energy of the cluster associated to the charged secondary Log scale Linear scale MeV MeV MeV

  17. Time of flight of the charged secondary Linear scale Log scale ns ns ns

  18. Background statistic estimator To build a conservative background estimator I have to measure the deviation from statistic fluctuation of the difference of the two sets of histos. We define: d(n) = abs [ his2tag(n) – his1tag(n) ] For bin n sd(n)2 = ( shis1(n))2 + (shis2(n))2 For each bin I consider the quantity e(n) = d(n) - sd(n) . This variable gives the deviation of d(n)from the statistical fluctuation and is > 0 if the bin is bigger then statistica fluctuation and < 0 is underfluctuate. The sum over all the bins of e(n) is a upperlimit to the background.

  19. First results on  3 pb-1 of 2002 Positive Tag

  20. First results on  3 pb-1 of 2002 Negative Tag

  21. Backgroung on negative tag? The difference between the 1 tag and the 2 tag distribution settles in the signal region.. True background ??? • Conclusion: • There is no evidence for a clear background contamination in the single tag events, at least at fraction of % level • We are working out a robust statistic estimator for the background level (or limit) • Work in progress..

More Related