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Shower recognition algorithm For LCAL

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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. Tungsten absorber + Diamond sensor. R M ~ 1 cm. Sandwich LCAL geometry. Z - Segmentation : - PowerPoint PPT Presentation
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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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Page 1: Shower recognition algorithm  For LCAL

Shower recognition algorithm

For LCAL

LCAL-group : K. Afanaciev, V. Drugakov,

E. Kouznetsova,

W. Lohmann, A. Stahl

LUMI Workshop, Zeuthen

November 14, 2002

Page 2: Shower recognition algorithm  For LCAL

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

Page 3: Shower recognition algorithm  For LCAL

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)

Page 4: Shower recognition algorithm  For LCAL

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.

Page 5: Shower recognition algorithm  For LCAL

250 GeV particle

Energy deposition by 250 GeV e- :

Total energy deposited by 250 GeV electron is about 30 GeV

Page 6: Shower recognition algorithm  For LCAL

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 :

Page 7: Shower recognition algorithm  For LCAL

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

Page 8: Shower recognition algorithm  For LCAL

Why 3 ?

Number of recognized particles with 2 and 3 threshold

(100 real particles of 250 GeV)

2

3

Page 9: Shower recognition algorithm  For LCAL

Fake rate due to high energetic

backgroundFake rate :

(BG of high energy + BG fluctuations)

( 50

0 b

un

chcr

oss

ing

s )

Page 10: Shower recognition algorithm  For LCAL

Efficiency

Efficiency vs radius :

Page 11: Shower recognition algorithm  For LCAL

Energy resolution

Energy resolution vs radius :

Page 12: Shower recognition algorithm  For LCAL

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.


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