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Search for Diffractive Z→ μμ

Search for Diffractive Z→ μμ. Tamsin Edwards University of Manchester Diffractive EB 20 th Feb 2004. Rapidity Gaps. Diffractive events have a region of the detector with little or no energy deposited: a rapidity gap Can search for rapidity gaps independently from proton tagging

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Search for Diffractive Z→ μμ

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  1. Search for Diffractive Z→μμ Tamsin EdwardsUniversity of Manchester Diffractive EB20th Feb 2004

  2. Rapidity Gaps • Diffractive events have a region of the detector with little or no energy deposited: a rapidity gap • Can search for rapidity gaps independently from proton tagging • Single diffraction and double pomeron exchange events have forward rapidity gaps • Search for forward gaps using the calorimeter and Luminosity Monitor South +ve η North -ve η

  3. Datasets • Datasets • Zero bias & min bias trigger data • Runs: 151852-176852 • Dates: 20th Apr ’02 - 12th May ’03 • Reco version: p14.05.02 • Jet + Gap trigger data • Run range: 164407 - 176852 • Dates: 20th Sep ’02 - 12th May ‘03 • Reco version: p14.05.02 • Z candidates • Emily’s ‘2nd stage’ TMB skim, derived from Common Sample group ‘1MuLoose’ skims • Runs: 173481-181100* • Dates: 28th Feb ’03 - 13th Sep ‘03 • Reco: various p14 * not using pre-173352 data, as dimuon trigger had Fastz requirement

  4. Data samples: noise • Noise sample: • Zero bias trigger [random BX selection] • Exclude bad SMT/CFT/Jet/Calorimeter runs • Veto LM using L1 a/o terms: • North, South, Fastz, AHalo, PHalo terms off • Veto good vertex: • primary vertex (if any) has < 3 tracks • Require number of calorimeter cells in read-out > 0 • Start: 357K events • Sample: 2.8K events • Data quality issues: • ~35% triggered events cut because there were no cells read-out • (caveat: not including bad run exclusion) • Not many have good vertex: 21%, c.f. 32% with FastZ on(ditto) • Waiting to run through data using good LBN list: will cut ~30% triggered data

  5. Data samples: min bias • Minimum bias: • Min bias trigger [requires Fastz] • Exclude bad SMT/CFT/Jet/Calorimeter runs • Number of calorimeter cells in read-out > 0 • Start: 443K events • Final sample: 333K events • Similar data quality issues

  6. Data samples: jet • 25GeV inclusive jet sample: • JT_25TT_NG trigger [requires at least 1 L3 jet with pT > 25GeV] • Exclude bad SMT/CFT/Jet/Calorimeter runs • Require good vertex: • primary vertex has > 2 tracks matched • Veto jet in forward region: • highest pT jet has |ηjet1| < 2.4 • Highest pT jet passes Jet ID cuts and L1 scalar ET cut • Require number of calorimeter cells in read-out > 0 • Start: 328K events • Final sample: 243K events • Even more worrying that some events have no cells (will check again after LBN list is used)

  7. Data samples: Z • Z candidate sample: • Use standard Z analysis cuts • default parameters in WZReco • Exclude bad SMT/CFT/Muon/Jet/Met/Cal runs • Require number of calorimeter cells in read-out > 0 • Start: 227K events • Final sample: 10.2K events • Four events had no cells in read-out

  8. Calorimeter variable: energy sum • Calorimeter cell energy sum: • EM and FH layers • cell EEM > 100MeV, cell EFH > 200MeV • |η| > 2.6 • EM layers extend to 4.1, FH layers to 5.2 • Use log10(cell energy sum) to separate noise/gap events from forward energy events (proton break-up)

  9. Log10(energy sum): North Areas are normalised if cell energy sum = 0, i.e. all cells in event were below threshold, then put event in this ‘underflow’ bin

  10. Log10(energy sum): South hopefully excluded when using good LBN list Areas are normalised

  11. Log10(energy sum): North with LM off Require LM off* * also required FastZ, AHalo, PHalo off due to a/o term inefficiency Areas are normalised low energy events: gaps

  12. Log10(energy sum): South with LM off Areas are normalised high energy: LM inefficient

  13. Log10(energy sum): North with LM off Areas are normalised log (cell energy sum), (GeV)

  14. Log10(energy sum): South with LM off Areas are normalised log (cell energy sum), (GeV)

  15. Selection of diffractive Z candidates • Gap cuts: • Luminosity Monitor: • require detector off • also require Fastz, AHalo and Phalo off • Energy sum: • log (EM+FH energy sum) < 1.0 • Require energy on the opposite side, to select single diffractive events: • At least two of these three: • LM on • log(EM energy sum) > 0.7 • log(FH energy sum) > 0.7 if both > 0.7, equivalent to log (EM+FH) > 1.0 Total number of Z candidates: 10259 Gap N candidates: 31 Gap S candidates: 44

  16. Mass of all Z candidates

  17. Mass of diffractive Gap N candidates

  18. Mass of diffractive Gap S candidates

  19. Contamination by non-diffractive events Percentage of events with log(energy sum) < 1.0 on one side: LMs are v.efficient (~ 95%).Cut at 1.0 is harsh • Min bias events: • non-diffractive (because of FastZ) • softer than jet & Z events • Jet events: • inclusive • Z events: • inclusive • less foward energy than jet events • Jet with LM off andZ with LM off: • diffractive events + contamination from LM inefficiency

  20. Next steps • Can we estimate the contamination of this sample using this data? • At least an upper limit? • Use bad LBN list for all samples • Check for double counted events in bias & jet samples • If bad data not removed with LBN lists: • check for hot towers • use hot cell killer • talk to experts about strange events (e.g. jet events with no cells)

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