December, 16 20081 Calibration of electromagnetic calorimeter of Hall A DVCS experiment Eric...
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![Page 1: December, 16 20081 Calibration of electromagnetic calorimeter of Hall A DVCS experiment Eric FUCHEY…](https://reader036.fdocuments.us/reader036/viewer/2022070616/5a4d1c007f8b9ab0599ef17f/html5/thumbnails/1.jpg)
December, 16 2008 1
Calibration of electromagnetic calorimeter of Hall A DVCS
experiment
Eric FUCHEYPh.D Student @ Laboratoire de Physique
CorpusculaireUMR 6533 CNRS/IN2P3Université Blaise Pascal
Clermont-Ferrand
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December, 16 2008 2
Outline• 1- Motivations of calibration• 2- Real data calibration
– Method– Results
• 3- Simulated data calibration– Method– Results
• 4- Conclusion
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December, 16 2008 3
Outline• 1- Motivations of calibration• 2- Real data calibration
– Method– Results
• 3- Simulated data calibration– Method– Results
• 4- Conclusion
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December, 16 2008 4
Experimental layout• Calibration for ep->ep0
• Incident beam : E = 5.75 GeV during experiment– Scattered electron measured by HRS– 2 photons issued from 0 measured by 132 PbF2 blocks
calorimeter– Recoil proton detected by proton array -> not used
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December, 16 2008 5
• Looking at real and simulated 0 data sets• We measure
• We can reconstruct for each event a missing mass:
with and
• We can either reconstruct for one block a missing mass distribution for all events with one photon hitting this block.
222XXX PEM
Xeep
2212 ' qqkMkE pX 221
2 ' qqkkPX
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December, 16 2008 6
Mp2
• Reconstructed missing mass distribution in each block depends on block position in calorimeter.
• Effects assumed to come only from calorimeter. => necessity to calibrate calorimeter to correct these effects.
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December, 16 2008 7
• For analysis, we need to fit 0 data set with 0 simulated set to extract cross section
• unconstistency of resolutions for real and simulated.=> necessity to calibrate and to smear simulated.
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December, 16 2008 8
Outline• 1- Motivations of calibration• 2- Real data calibration
– Method– Results
• 3- Simulated data calibration– Method– Results
• 4- Conclusion
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December, 16 2008 9
Method of calibration (real)• Goal of calibration: to find for each block of the
calorimeter a coefficient in order to reconstruct in each block a missing mass squared in accordance with Mp
2.
• Coefficient of the form: for block# ( = 0,…,89)
222pXX MMM
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December, 16 2008 10
Method of calibration (real)• For an event i, a photon is in block #a, the other is in
block #b.
• Correcting each photon energy:
• Recomputing MX2 with these corrected energies, and
filling #a and #b blocks distributions.
iX
aX
i
a
E
Mq
2
2
iX
bX
i
b
E
Mq
2
2
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December, 16 2008 11
Method of calibration (real)• Fit distributions by a gaussian
-> Mean of the gaussian => New missing mass squared value. New calibration coefficient
• Correct 2 photons to reconstruct missing mass -> calibration coefficients for each block are correlated. necessity to calibrate by steps. necessity to estimate calibration quality for each step.
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December, 16 2008 12
Corrections• Before filling distributions, we apply some corrections:
– By effects of resolution, missing mass squared and invariant mass ( ) are correlated
– -> possibility to improve resolution. with R = 13 GeV
– Additional cuts:• 0.4 GeV2 < MX
2 < 1.4 GeV2 ; 0.105 GeV < m < 0.165 GeV.• k’ = pHRS +/- 4.5% ; r-function > 0.005• Each photon is in a calibrated block, and E > 1.0 GeV• -6.0 cm < zvtx < 7.5 cm
mmGeVRMMrawXcorrX )(22
2212
21 qqqqm
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December, 16 2008 13
Quality calibration control
• Once iteration completed :– Plot reconstructed missing mass squared value for each
block over all blocks– Fit by a constant– Check for deviation from reference value, and dispersion
(RMS).
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December, 16 2008 14
Results (real)• Quick convergence from iteration 0 to 5-6• After, averaged MX
2 oscillates, RMS is stable.=> Converged
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December, 16 2008 15
Results (real)• Ex: Iteration #11 Dispersion is low, Calibration has
converged
• Pion mass deviation less than 1 MeV
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December, 16 2008 16
Outline• 1- Motivations of calibration
– Experimental layout• 2- Real data calibration
– Method– Results
• 3- Simulated data calibration– Method– Results
• 4- Conclusion
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December, 16 2008 17
Method of calibration (simulated)• Same principle, except we try to match simulated with
data:
– Searching for a calibration coefficient:
– Searching to bring each block simulated resolution ( of gaussian fit) to real block resolution.
=> also need for each block a « smearing » coefficient
realXsimuXX MMM,
2
,
22
222realsimu
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December, 16 2008 18
Method of calibration (simulated)• For an event i, a photon is in block #a, the other is in block #b.
• Correcting each photon energy:
• Smearing each photon energy: picking a random variable in a gaussian distribution.
: corrected energy, :
• Recomputing MX2 with these corrected energies, and filling #a
and #b blocks distributions.
iX
aX
i
a
E
Mq
4
2
iX
bX
i
b
E
Mq
4
2
2
22
arealasimu
a
2
22
brealbsimu
b
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December, 16 2008 19
Quality calibration control
• Once iteration completed :– Plot MX
2|simu - MX2|real for each block over all blocks and (MX
2|simu) - (MX
2|real) for each block over all blocks – Fit each plot by a constant
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December, 16 2008 20
Results (simulated)• Averaged MX
2 converges until iteration 15, then stable• RMS does not stop decreasing: <0.001 since iteration
#30
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December, 16 2008 21
Results (simulated)
• Since ~iteration #20, seems to be converged.• Check out for resolution to conclude
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December, 16 2008 22
Results (simulated)• Quick convergence averaged (MX
2) of from iteration 0 to 6-7, stable after.
• averaged (MX2) RMS stable since Iteration #15
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December, 16 2008 23
Results (simulated)
• Invariant mass deviation due to calibration is still less than 1 MeV
• Since ~iteration #20, dispersion is low, calibration has converged.• Only advantage to go further is to improve MX
2 dispersion.
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December, 16 2008 24
Outline• 1- Motivations of calibration
– Experimental layout– Consequences on measurements
• 2- Real data calibration– Method– Results
• 3- Simulated data calibration– Method– Results
• 4- Conclusion
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December, 16 2008 25
Conclusion• Our goal was to calibrate the calorimeter with our set of
Kin3 0 data and our set of Kin3 0 simulated to be able to make consistent fit for analysis.
• We have reached regime of stability for both real calibration and simulated calibration iteration processes=> Calibration has been succesfully completed.