1. Status of the hadronic cross section (small angle) Federico Nguyen February 22 nd 2005  the 2002 data sample and available MC sets  trigger efficiency issues: data vs. mc  tracking efficiency issues: data vs. mc vs.   conclusions and spectra

2. Reprocessed 2002 data and MC we finished the MC productions with the simulation of the 2002 data conditions      MC, 6:1       MC, 1:1      MC, 6:1 1.new FILFO 2.trackmass window enlarged  m trk  MeV instead of  MeV 3.downscale for events with m miss  MeV is applied m miss (MeV) m trk (MeV)  density due to e  e    K  physics shark requirements:

3. L3 filter efficiency  Cosm Veto (2001 data)  L3 filter (2002 data) M  2 (GeV 2 ) after small angle selection a) 1 and only 1 vertex ( |z|  7 cm ) connected to 2 tracks b) each track with 50 o <  track < 130 o c) small angle  (     ) d) at least one track with log L  /L e  e) m trk > 130 MeV and the ellipse

4. Filfo efficiency: 2002 vs. 2001  FILFO M  2 (GeV 2 ) MC 2001 o data 2001 MC 2002 o data 2002 Filfo systematics is the main improvement from the reprocessing of 2002 data in 2002 mach. backgr. reduced with downscale laws, systematic uncertainty obtained from data in 2001 MC (6% difference!) was not even considered for the uncertainty in 2002 the MC-data disagreement is reduced to 1% main source of systematics understood and fixed, S. Müller, M. Moulson Memo n.288

5. based on the single particle method by M. Incagli (KLOE Memo n. 278) 1 and only 1 vertex in  < 8cm, |z| < 15cm with 2 tracks small angle photon,    o each track extrapolated to the ECAL à la T. Spadaro classify all clusters such that they belong to: each category may have associated 0, 1, 2 trigger sectors retrieved with the CTRG bank  rest of the event either or d  d d  distance btw cluster centroid and the extrap’d point of the track data sample size: L ~ 53 pb -1 Calorimeter trigger efficiency

6. Single track efficiency (1 sector) black = data, white = MC for the 2001 analysis we did not look at MC at all, what about the new MC reconstructed with DBV23? it is defined as the probability for the  + (  - ) to fire 1 sector, provided that…, it is obtained after the whole selection chain, run on both data (60 pb -1 ) and MC (70 pb -1 ) with polar angle 50 o – 55 o momentum of the  + (MeV) momentum of the   (MeV)

7. Single track efficiency (1 sector) with polar angle 85 o – 90 o black = data, white = MC momentum of the  + (MeV) momentum of the   (MeV) for the single track efficiency to fire 1 sector only the difference data-MC is of the order of 1-3%

8. Single track efficiency (2 sectors) for the single track efficiency to fire 2 sectors the agreement tends to be better with polar angle 85 o – 90 o momentum of the  + (MeV) momentum of the  - (MeV) black = data, white = MC

9. The rest of the event it consists of: fragments of the  cluster large angle photons secondary particles created by photons hitting the quadrupoles mach. bacgkr. events black = data, white = MC the normalization is provided by the events triggered by the  ’s M 2 (GeV 2 ) probability for 1 sector (barrel) probability for 1 sector (endcap) understanding the dynamics of the “rest” from MC seems hard…

10. Data – MC comparison M 2 (GeV 2 ) black = data, white = MC   MC /  data from the single track efficiencies, 8 (polar angle)  30 (momentum) bins, and from the MC kinematics, the M 2 efficiency is extracted the difference data- MC is at the 1% level, for the moment the efficiency from data is used without any correction…

11. Tracking efficiency with 2002 data M. Incagli’s selection of the data control samples:       1)  2 and only 2 clusters with E>30 MeV and 29 cm/ns < R/t < 32 cm/ns 2)  |M  – m  | < 20 MeV 3) a tagging track recognized as a pion by the Likelihood, extrapolating back to the IP     1)  1 or 2 clusters in the barrel with 5 ns < t < 8 ns 2) a tagging track recognized as a pion by the Likelihood, extrapolating back to the IP, and with |p-490| < 5 MeV

12. Cleaning the     sample the data control sample must be cleaned up of possible biases, such as monotracks… these are chacterized by small energy releases in 1 or 2 cells of the calorimeter

13. The candidate track The candidate track must satisfy the following cuts (on data): 1) charge must be opposite wrt tagging track 2) first hit must have  50 cm 3) the point of closest approach (PCA) of backward track extrapolation must have  PCA  8cm and |z PCA |  7cm 4)  2 algorithm to assign the track based on the conservation of momenta known from BMOM, the tagging track (and the   for the 1 st sample) samples: 15 pb -1 refiltered from RAW, 30 (  6) pb -1 MC  20 (  6) pb -1 MC  From MC: take the KINE track and look for the DTFS track of the same sign having the same 2) and 3) features

14. Comparison data - MC the point at p ~ 475 MeV reflects the gap due to the merging of the 2 control samples, in the remaining parts the agreement is good black = MC, white = data

15. Fit to the ratio data/MC the agreement is on the level of 0.5- 0.6%, at maximum, much more     statistics from raw could help in curing the 450-500 momentum region

16. Comparison  from MC at the moment, the only way to distinguish btw muons and pions is with the m trk, so the tracking (single and global) efficiency is estimated from MC

17. Definition of  /  events (I) M  2  [0.37,0.42] GeV 2 the assumption is that the Likelihood efficiency is high for  event      m trk (MeV) probability for the  track to have log L  /L e > 0, conditioned to the track-to-cluster association  no man’s land

18. Definition of  /  events (II)      M  2  [0.57,0.62] GeV 2 M  2  [0.87,0.92] GeV 2 m trk (MeV) ee  contamination @ high M   can be neglected to 1st order

19. On the way to the absolute spectrum Trigger Filfo Tracking Vertex Trackmass blue = efficiency estimated from MC red = efficiency estimated from data single track efficiency, then combined from the downscaled events tagging track method applied both on data/MC to cross check, then MC is used from MC, as a function of KINE momenta, checks started from MC, as a function of reconstructed momenta Acceptance from MC, as a function of KINE momenta

20. Background data MC       MC  M 2 (GeV 2 ) at first approximation we (Stefan) did evaluate it, normalizing to the luminosity scale factor, where N is the # of counts (in data and MC) after the whole selection d  vis /dM 2 (nb/GeV 2 )

21. Absolute spectrum M 2 (GeV 2 )  = 0.01 GeV 2, L = 242 pb -1 d  /dM 2 (nb/GeV 2 ) very very preliminary

22. On the way to F  from the ratio:  events Trigger Filfo Tracking Vertex Trackmass blue = efficiency estimated from MC red = efficiency estimated from data single track efficiency, then combined from the downscaled events tagging track method applied only on MC from MC, as a function of KINE momenta, checks started from MC, as a function of reconstructed momenta Acceptance from MC, as a function of KINE momenta

23. Background M 2 (GeV 2 ) at first approximation we (Stefan) did evaluate it, normalizing to the luminosity scale factor, where N is the # of counts (in data and MC) after the whole selection d  vis /dM 2 (nb/GeV 2 ) data MC       MC 

24. Pion form factor from the ratio M 2 (GeV 2 ) very very preliminary

25. Conclusions this is only the beginning of the story, 1) trigger: MC yet does not help in understanding, the rest of the event is crucial, keep only events with sectors fired only by cluster-assoc.-to-tracks? a glance at the DC trigger started already, either for systematics or for the selection 2) tracking: I am trying to reproduce results and systematics à la 2001 with the prescription from M. Incagli, need more statistics (from raw) for the 2 body reaction 3) I started the systematics of the vertex, to say if relaxing it, implies negligible differences in resolution and background tracks 4) background: Stefan evaluated with LSF