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Energy Calibration with Compton Data

Energy Calibration with Compton Data. June 23, 2006. Outline. Revisit “Z” using DA Compton calibration SA Compton calibration Using production on carbon, lead SA gains vs LMS How to improve LMS performance

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Energy Calibration with Compton Data

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  1. Energy Calibration with Compton Data June 23, 2006

  2. Outline • Revisit “Z” using DA Compton calibration • SA Compton calibration • Using production on carbon, lead • SA gains vs LMS • How to improve LMS performance • Need a consistent normalization scheme for the LMS gains during the production period.

  3. Target-Hycal Distancefrom Double Arm Calibration Calibration procedure: Gain correction factors are found through cluster coordinates and Z (E(el)=Eb/[1+2*Eb/me*sin2((el)/2)]) Incorrect value of Z will result in elasticity distribution not centered exactly at 1 ! try to vary z, recalibrate, check elasticity after before Mean=1.001 Mean=1.017 (e1+e2)/Eb (e1+e2)/Eb

  4. Target-Hycal Distance from Double Arm Calibration (cont) Mean of the elasticity distribution vs hycal-target distance (Z) used in calibration for 5 groups of runs 731.4 < Z < 732.1 cm Resolution does not change with z

  5. Target-Hycal Distancefrom Compton kinematics • Could obtain Z from Compton kinematics, setting z1=z2: • z=(Eb*r1*r2/me/2)0.5 • Not so sensitive to the gains • Sensitive to beam misalignment: changing x by 1 mm changes z by ~6-7 cm Z (cm) Run 4871, Be target

  6. Target-Hycal Distancerun by run Difference between the first group of carbon runs and the rest of the runs should be: 7.62 cm We see: ~6.52 9Be : Z=732.08 cm 12C (ave): Z ~733 cm

  7. Single Arm Compton Calibration • Objectives: • Monitor gain change during the production data taking • Compare with the LMS behavior • Use LMS gains in the part of HyCal where no Compton gains are available • Procedure: • 320 production runs with carbon target and 70 runs with the lead target • Look for events for one neutral cluster and fill the following distribution:ecl/eC, • eC=Eb/(1+Eb/me*sin2(/2)) • Fit ! gain corr=1/mean carbon lead

  8. SA Calibration: PSCarbon ~ 30 % eff. PS cut Require 1 hit in the PS within the physics time window <5% eff

  9. SA Calibration: PS (cont)Lead ~ 8% eff. Very low PS efficiency for lead runs! lose most of events

  10. Life and times of module 1494 SA Lead SA carbon LMS Reference pmt Gain correction factors relative to the snake calibrations vs run #

  11. 2 parts to life of module 1494 Relative to snake #1 Relative to snake #2 LMS data is not properly normalized for this time period Looks good

  12. Look closer SA gains: stat. errors are small (for carbon) Systematic uncertainty comes mostly from possible beam misalignment: shift of 1 mm in X results in ~1% change of the gain value LMS and SA gains are consistent but LMS is more “scattered”

  13. SA gains vs LMS carbon lead lead carbon carbon carbon Could find correlation between the LMS and SA gains

  14. Tri-modal behavior of LMS What are we going to do about this?

  15. Summary • ~120 modules can be calibrated with the SA gains • Good agreement with the Double Arm gains • Reasonable agreement is observed between the SA and LMS gains for both the carbon and lead targets • Can/Will find the correlation between LMS and SA gains for every module • LMS gains look reasonable but are scattered and sometime “tri-modal”! what to do about it? • Parameterize LMS with SA gains? • How to correct for “tri-modality”? • Need to use a consistent scheme for LMS normalization throughout the run ! currently it is wrong for runs <4838

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