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Dealing with central events in Run 10 Au+Au collisions

Dealing with central events in Run 10 Au+Au collisions. Mihael Makek Weizmann Institute of Science. HBD Meeting, 2/6/2010. Clustering algorithm („Weizmann clusterizer“). loop through all fired pads (“CellList”): find pads with 3 pe < charge < 100 pe  seed pads

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Dealing with central events in Run 10 Au+Au collisions

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  1. Dealing with central events in Run 10 Au+Au collisions Mihael Makek Weizmann Institute of Science HBD Meeting, 2/6/2010

  2. Clustering algorithm („Weizmann clusterizer“) • loop through all fired pads (“CellList”): • find pads with 3 pe < charge < 100 pe  seed pads • build clusters around seed pads by summing the first neighbour pads with charge > 1 pe • loop through the clusters („BlobList“) and merge clusters if they have overlaping pads • Matching of the CA tracks to HBD: • loop through the clusters again and find the one that is the closest to the track projection point M. Makek

  3. Minimum bias data 200 GeV Au+Au nCentral = 278, nCells = 1655 M. Makek

  4. Subtraction of pedestals • Method 1, define the average charge per pad: • Method 2, define the average charge per fired pad: • where a[pad] normalizes pad area • Look at the <ch>pp as a function of centrality (nCentral) • Derive this dependence module by module • Subtract charge from each pad according to nCentral M. Makek

  5. Subtraction of pedestals 1 • Correction functions: all modules example: WN2 • Possible causes of variations: • zero suppression + gain difference • reverse bias voltage M. Makek

  6. After subtraction of pedestals 1 nCentral = 278, nCells = 670 M. Makek

  7. After subtraction of pedestals 1 • Run WIS clusterizer on subtracted data • Electron tracks • Matching distribu-tions for different nCentral bins • 3s cuts on hbddf and hbddz (mom. corrected) • Select only clusters with size > 1 pad • Subtracted random background gene-rated by projecting tracks to different module M. Makek

  8. Subtraction of pedestals 2 • Correction functions: example: WN2 M. Makek

  9. After subtraction of pedestals 2 nCentral = 278, nCells = 530 M. Makek

  10. Comparison of the subtraction methods M. Makek

  11. Comparison of the subtraction methods M. Makek

  12. Comparison of the subtraction methods M. Makek

  13. Comparison of the subtraction methods M. Makek

  14. Comparison of the subtraction methods M. Makek

  15. Comparison of the subtraction methods M. Makek

  16. Summary • After subtraction of pedestals we do see electron signal in the most central events! • Even after subtraction we are still picking additional charge • (need to subtract more?!) • Needs to optimized: • S/B ratio is dropping for the most central events • Electron „efficiency“ decreasing for the most central events M. Makek

  17. Outlook • Ideas to proceed: • Optimize subtraction empirically monitoring the electron efficiency and validating results with the Accumulator • Optimize lower threshold of the seed pad (e.g. 35 pe) • expected effect: low charge background reduction • Optimize upper threshold of the seed pad (e.g. 10050 pe): • expected effect: reduction of the high charge clusters • Optimize lower threshold for the neighbouring pads according to centrality • (e.g. 1  0.06 * nCentral). Expected effects: • reduction of the cluster size according to centrality • reduction of the cluster charge to centrality M. Makek

  18. BACKUP slide: electron ID cuts • Using central arms: • quality = 31, 51, 63 • n0 > 2 • abs(emcdz+1.0) < 10.0, abs(emcdphi-0.00023) < 0.030 • ecore/mom > 0.6 • chi2/npe0 < 10 • disp < 5 • prob > 0.01 • ecore > 0.15 • Using HBD: • abs(phbdz) < 27.0 • remove tracks projecting to EN2 • 3 sigma matching in hbddz and hbddphi • HBD clustersize > 1 M. Makek

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