shower recognition algorithm for lcal
DESCRIPTION
Shower recognition algorithm For LCAL. LCAL-group : K. Afanaciev, V. Drugakov, E. Kouznetsova, W. Lohmann, A. Stahl. LUMI Workshop, Zeuthen November 14, 2002. Tungsten absorber + Diamond sensor. R M ~ 1 cm. Sandwich LCAL geometry. Z - Segmentation : - PowerPoint PPT PresentationTRANSCRIPT
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Shower recognition algorithm
For LCAL
LCAL-group : K. Afanaciev, V. Drugakov,
E. Kouznetsova,
W. Lohmann, A. Stahl
LUMI Workshop, Zeuthen
November 14, 2002
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Sandwich LCALgeometry
Tungsten absorber + Diamond sensor
RM ~ 1 cm
Z - Segmentation :
Tungsten 3.5 mm Layer = Diamond 0.5 mm
(R,) - Segmentation :
12 Radial Layers
Square cells of about
0.50.5 cm
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Beam-beam Background
GUINEAPIG + BRAHMS( for √s = 500 GeV ) :
Per bunch crossing :
• ~15000 e± hits• ~20 TeV of total deposited
energy
(x,y)-distribution of the beamstrahlung energy:
Background averaged for 500 bunch crossings
The bulk of energy is deposited in the inner region (radial layers 1, 2 and 3)
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Energy distribution in background
Total number of particles corresponds to 500 bunch crossings
Most particles have energy of up to few GeV
A few particles have energy greater than 50 GeV.
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250 GeV particle
Energy deposition by 250 GeV e- :
Total energy deposited by 250 GeV electron is about 30 GeV
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Sandwich LCALbackground
Average background for 10 bunchcrossings
Longitudinal energy deposition profiles are a bit different for particle and background
Distribution of energy deposition of BG defines “good” and “bad” regions
250 GeV e- + BG :
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Particle recognitionalgorithm
1. Calculate average background and its RMS
2. Subtract average BG from data
3. Compare result with 3BG (RMS) (only for long. layers 4 - 17)
4. Find columns with > = 10 (of 14) such cells
5. Check neighbor columns to contain at least 7 “suspected” cells
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Why 3 ?
Number of recognized particles with 2 and 3 threshold
(100 real particles of 250 GeV)
2
3
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Fake rate due to high energetic
backgroundFake rate :
(BG of high energy + BG fluctuations)
( 50
0 b
un
chcr
oss
ing
s )
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Efficiency
Efficiency vs radius :
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Energy resolution
Energy resolution vs radius :
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Calibration curve
CONCLUSIONS
Though quite simple, this algorithm provides good efficiency and energy resolution.
We should find a way to improve its performance in “bad” regions.
Calibration curve for different conditions.