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Impact of Dead LAr EM Calo Cells on Electron Trigger Efficiencies

Impact of Dead LAr EM Calo Cells on Electron Trigger Efficiencies. Andrew Lowe Royal Holloway, University of London. Introduction. The effect of dead cells in the LAr EM Calorimeter on electron trigger selection efficiencies was studied Dead cells = cells that are unresponsive

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Impact of Dead LAr EM Calo Cells on Electron Trigger Efficiencies

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  1. Impact of Dead LAr EMCalo Cells on ElectronTrigger Efficiencies Andrew Lowe Royal Holloway, University of London

  2. Introduction • The effect of dead cells in the LAr EM Calorimeter on electron trigger selection efficiencies was studied • Dead cells = cells that are unresponsive • There are two parallel electronics readouts from the calorimeter • Part that creates the LVL1 Trigger Towers • Part which goes to the RODs for LVL2 and EF • Must ensure that the set of cells that are dead is the same at LVL1 and HLT • Expect that: dead cells  incorrect reconstruction of cluster energies  e/ objects fail to pass cuts  trigger selection efficiency decreases Andrew Lowe, RHUL

  3. Method (1) • Source data: single electron || ≤ 2.5, with pile-up, no LAr or Tile calo noise, PT spectrum flat between 7-80 GeV, low-lumi (design-lumi to follow) • Reconstruction using offline release 8.0.2 • LArDeadCellTools: suite of helper classes • Class containing info and status of cell • Container mapping compact identifiers to the previous class • Container mapping compact identifiers to compact identifiers • Randomly kill cells and store information in StoreGate • AlgTool “LArCellKiller” zeros cell energies at LVL1 and HLT (two instances of this tool, one for LVL1, one for HLT) • MakeCorrection(LArCell*) method invoked for each cell every event • Retrieves containers and do a look-up to see if cell is supposed to be dead • Same list used at LVL1 and HLT  same cells dead at LVL1 and HLT • HLT performance analysed using TrigEgammaAnalysis package Andrew Lowe, RHUL

  4. Method (2) • Dead cells are assumed to be uniformly distributed in LAr EM calorimeter • 0, 0.1, 0.2, 1.0 percent dead cells simulated • This reflects estimates of dead cells that may be expected • More data to come • No smart corrections (to correctly reconstruct energy) applied to account for dead cells • All samplings treated equally • ~10000 events per sample • Configuration of dead cells changes every event • Avoids bias from “lucky/unlucky” distributions of dead cells • Examine e25i trigger efficiencies as a function of the percentage of dead cells Andrew Lowe, RHUL

  5. e/ Framework • There is a non-trivial discrepancy between the efficiencies that the TrigEgammaAnalysis package calculates and those that are published in the HLT TDR and elsewhere – expect ~80% efficiency wrt LVL1, but get ~76% • Small increase (< 1%) in efficiency if events that have a track found by xKalman and not by IDScan are skipped, instead of calculating a “fudge-factor” to normalise the efficiencies, to account for events with tracks missed by IDScan • This is something that was done before, but appears to have never appeared in a publicly-accessible version of the Framework • Discrepancy can only be accounted for if the undocumented TRT cuts buried inside EFIDCalo are switched off • Manuel Diaz Gomez confirmed that these cuts were switched off when he generated the efficiencies for use in the TDR, because the TRT reconstruction was not performant • Running my own version of the Framework with my own tweaks and modifications, but cuts are unchanged from those in the main branch Andrew Lowe, RHUL

  6. Results (1) Andrew Lowe, RHUL

  7. Results (2) • Expect that the efficiencies will not be identical because of changes in the reconstruction from 6.0.4 to 8.0.2 • Note that data with LAr and Tile calo noise was used, so efficiencies will be slightly lower than those I obtain with noise switched off Andrew Lowe, RHUL

  8. Results (3) • Electron selection efficiency, wrt LVL1 at EFIDCalo, vs. MC PT for 0% dead cells Andrew Lowe, RHUL

  9. Results (4) • Mean efficiency (calculated using a fit to data between 30-80 GeV), wrt LVL1 at EFIDCalo, vs. percentage of dead cells • Observe systematic decrease in electron selection efficiency Andrew Lowe, RHUL

  10. Results (5) • Decrease in electron selection efficiency: 1% dead cells (a realistic upper-limit) results in: • 1.3 ± 0.3% drop in average electron selection total efficiency • 1.2 ± 0.3% drop in average electron selection efficiency wrt LVL1 • Trend is linear, as can be seen in plot Andrew Lowe, RHUL

  11. Future Work • Process data for 0.5% and 2.0% dead cells • Process data for 0% – 2.0% dead FEBs • Expect interesting results because a FEB instruments a region of contiguous cells in a sampling • Repeat study with design-lumi data, but first: • Repeat study using physics samples to study impact on Higgs channels for a number of Higgs masses • H  ZZ(*)  4e • H  ZZ(*)  2e2µ Andrew Lowe, RHUL

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