Transcript Document

Calibration of the ATLAS Lar Barrel
Calorimeter with Electron Beams
[email protected]
19/09/2007
•The ATLAS e.m. barrel calorimeter
and status
• Calibration strategy
• Test-beam results
The ATLAS Calorimeter
•
LAr Calorimeters:
–
–
–
–
•
•
endcap A
em Barrel : (||<1.475) [Pb-LAr]
em End-caps : 1.4<||<3.2 [Pb-LAr]
Hadronic End-cap: 1.5<||<3.2 [Cu-LAr]
Forward Calorimeter: 3.2<||<4.9 [Cu,W-LAr]
~190K readout channels
Hadronic Barrel: Scintillating Tile/Fe calorimeter
barrel
endcap C
Physics Requirements
Discovery potential of Higgs (into γγ or 4e± ) determines most requirements for em calorimetry:
• Largest possible acceptance ( accordion, no phi cracks)
•
Large dynamic range : 20 MeV…2TeV ( 3 gains, 16bits)
•
Energy resolution (e±γ): E/E ~ 10%/√E  0.7%
 precise mechanics & electronics calibration (<0.25%)…
•
Linearity : 0.1 % (W-mass precision measurement)
 presampler (correct for dead material), layer weighting, electronics calibration
•
Particle id: e±-jets , γ/π0 (>3 for 50 GeV pt)  fine granularity
•
Position and angular measurements: 50 mrad/√E
 Fine strips, lateral/longitudinal segmentation
•
Hadronic – Et miss (for SUSY)
– Almost full 4π acceptance (η<4.9)
•
Jet resolution: E/E ~ 50%/√E  3% η<3,
•
Non-compensating calorimeter  granularity and longitudinal segmentation very important
to apply software weighting techniques
•
Speed of response (signal peaking time ~40ns) to suppress pile-up
and E/E ~ 100%/√E  10% 3<η<5
The E.M. Barrel ATLAS Calorimeter
Lead/Liquid Argon sampling calorimeter with
accordion shape :
back
middle
Main advantages:
strips
Presampler in
front of calo
up to  = 1.8
LAr as act. material inherently linear
Hermetic coverage (no cracks)
Longitudinal segmentation
High granularity (Cu etching)
Inherently radiation hard
Fast readout possible
EM Barrel: Wheels Insertion P3
M-wheel inside the cryostat, March 2003
EM Barrel: Wheels Insertion P3
ATLAS barrel calorimeter being moved to the IP, Nov. 2005
EM Barrel: Wheels Insertion P3
ATLAS endcap calorimeters installation, winter-spring 2006
Commissioning – The Road to Physics
1: Testbeams
2: Subdetector Installation,
Cosmic Ray Commissioning
~30 k events in barrel
Sommer ’07:
Global cosmic run
with DAQ of ATLAS detector
>03/07
weekly cosmic data taking
together with Endcap A
~100 k events
2005
2006
Final cool
down
2007
2008
3: First LHC
collisions
4: First Physics
Test beam Setups
Test-beam 2002:
Uniformity: 3 production modules /f scan
Linearity: E-scan 10 -245 GeV at eta=0.69 phi=0.28
thanks to special set-up to measure beam energy:
linearity of beam energy known to 3 10-4 and a constant
of 11 MeV (remnant magnet field)
Test-beam 2004 : (not covered here)
Combined test beam
full slice of of ATLAS detector
final electronics+ DAQ
Optimal Filtering Coefficients
ADC to GeV
Pedestals


E   Fj   a i ADCi  P 
j 1
 i1

2
5
Energy
pedestals and noise
Cells are read with no
input signal to obtain:
Pedestal
Noise
Noise autocorrelation
(OFC computation)
j
Raw Samples
ADC  MeV conversion
F = ADC2DAC DAC2A
 A2MeV
 fsamp
Scan input current (DAC)
Fit DAC vs ADC curve with
a second order polynomial,
outside of saturation region
Every 8 hours
Every 8 hours
Amplitude ( Energy)
LAr electronic calibration
The ionization signal is
sampled every 25 ns by a 12
bits ADC in 3 gains. Energy is
reconstructed offline (online in
ROD at ATLAS).
Pedestal subtracted
response to current pulse
All cells are pulsed with
a known current signal:
A delay between
calibration pulses and
DAQ is introduced
The full calibration
curve is reconstructed
(Δt=1ns) Every change
of cabling
On the Calibration of longitudinally Segmented
Sampling Calorimeter
Erec  d Eacc , d  1/fsamp 
Eact  Epas
Eact
Shower
Samp.frac. depends on shower composition.
Many short-ranged, low-energy particles are
created and absorbed in the Pb
(much higher cross-section
for photo-electric effect in Pb than LAr)
Sampl. fract. decreases with depth and radius
as such particles become more and more
towards the tails of the shower
Use one sampling fraction for
all compartments apply energy
dependent correction
Sampling Fraction Correction
Erec
1

Eacc
E 100
d(E) fsamp
1%
Correction to sampling fraction
in accordion:
- intrinsic E-dependence of s.f.
- I/E conversion
- out-of-cluster (fiducial volume) correction
Correction for Dead Material Losses
•
•
•
Accordion Sampling Calorimeter
– Segmentation in three longitudinal
compartments
Presampler
(Significant) amount of dead material upstream
(~2-3 X0)
– Cryostat wall, solenoid, …
Cryostat Walls
e-
Accordion
Calorimeter
Presampler
Material in front of the Accordion in ATLAS
•
Calibration Strategy:
– Use MC to understand effect of upstream
material
– Validate MC with test-beam data
– Derive calibration constants from MC
– Cross-check by applying calibration to testbeam.
DM Correction using the Presampler I
DM
Erec
 a EPS ?
Assume for a moment perfectly calibrated Lar calorimeter:
Opt. Linearity
Opt. Resolution
A simple weight is not
sufficient!
DM Correction using the Presampler II
e-
e+

e
Dead
Material
Shower
Dead
Material
Presampler
Accordion
Sampling fraction for PS can not be calculated
as for sampling calorimeter
Slope is smaller:
Secondary electrons:
• only traverse part of dead material
• are created in PS
• are backscattered from calorimeter
Offset not zero:
In the limit of hard Bremsstrahlung, no electron
traverses the pre-sampler
DM Correction using the Presampler II
Upstream
Erec
 a  b EPS

e-
e+
e
Dead
Material
Dead
Material
Presampler
Offset accounts for energy loss by particles
stopping before PS
- Ionisation energy loss
- low-E Bremsstrahlung photons energy
- photo-photonuclear interactions dependent
Weight accounts for energy loss (partly)
traversing the DM and the PS
DM Correction between PS and Strips
•
•
Significant amount of inactive material (~0.5 X0)
– Electronics boards and cables immersed in LAr
– Dependence on impact point
Shower already developed
(about 2-3 X0 before Accordion)

e-
e+
e
Dead
Material
Dead
Material
Presampler
•
Best correlation between measured
quantities and energy deposit in the
gap:
PS/strip
Erec
 c EPS EStrip
•
Empirically found
Final Calibration Formula
Offset: energy lost by
beam electron passing dead
material in front of calorimeter
Slope: energy lost by
particles produced in DM
(seeing effectively
a smaller amount of
dead material) in front of
calorimeter
Erec  a(E)  b(E) EPS  c(E) EPS Estrips 
e-
e+

e
Dead
Material
•
1
Eacc
E 100
d(E) fsamp
Shower
Dead
Material
Presampler
•
•
Correction to sampling fraction
in accordion:
- intrinsic E-dependence of s.f.
- I/E conversion
- out-of-cluster correction
Accordion
Good linearity and resolution achieved
Constants depend on impact point (upstream material) and on the energy.
– Can be parameterized.
Constants are derived from a MC simulation of the detector setup.
+Eleak
Data MC Comparison – Layer Energy Sharing
Most difficult: correct description of DM material
Band due to uncertainties in material estimation
Data MC Comparison – Layer Energy Sharing
Mean visible energy for 245 GeV eDeposited energies = f() in the
PS and in the 3 calorimeter
compartments before applying
the correction factors a,b,c,d
Data
PS
MC
Excellent Data / MC agreement
Strips
in all samplings
Middle
Back

Data/MC Comparisons – Radial Extension
First layer:
MC uncertainty shown
but not visible
We do not know why this
Is, can be
- detector geometry ?
- beam spread ?
- cross-talk
- G4 physics problem ?
•Good description
also for asymmetry
Data/MC Comparisons – Total Energy Distribution
Need to fold in
acceptance correction
for electrons having
lost large energy
in „far“ material
(from beam-line
simulation)
MC uncertainty contains variation of „far“ material: air in beam-line and beam-pipe windows
Linearity Result
within 0.1% for 15-180 GeV, E=10 GeV 4 per mil too low, reason unclear…
Systematics
..within 0.1%
Resolution Result
Good resolution while preserving good linearity
Data MC comparison - Resolution
Preliminary
Phi-impact correction
not applied
Resolution is much
better described in
new G4 version !
G4.8 has
completely revised
multiple-scattering
Current to Energy Factor in ATLAS Barrel EM Calorimeter
G. Unal: ATLAS-SIM 09/05
Assuming calo is simple condensator
and knowing Lar drift time:
fI/E  15 nA/MeV
From calculation using field-Maps:
fI/E  14.2nA/MeV
Pb absorber
electrode
From comparison of data and MC:
G4.7 : fI/E  16.0 nA/MeV
G4.8 : fI/E  14.4 nA/MeV
Much better understanding of absolute energy scale
from first principles !
(Some effects missing in simulation and calculation,e.g.
recombination effect in Lar)
Calibration Parameter vs Eta
Erec  a(E)  b(E, ) EPS  c(E,h) EPS Estrips 
,
,
1
Eacc
E 100
fsamp (E , )
E=245 GeV e-,
scan in 
Internal ATLAS
modul number
related to 
Uniformity barrel results
Module P13
245.6 GeV
Module P15
245.7 GeV
Resolution
0,7-0,9%
Uniformity
0,44%
0,44%
0,7-0,9%
TDR requirement:
0.7%
Conclusion
Precise calibration of em calorimeter need to take em physics effects
- variation of sampling fraction with depth  energy
- dead material correction
This is only possible using a MC and requires excellent description by MC
As an alternative calibration parameters can be extracted using a fit
(based on correct functional form of calibration formula)
In ATLAS presently both strategies are followed
In the test-beam it has been demonstrated:
1)
2)
MC describes data well
Calibrations parameters extracted from MC, lead
to linearity of 0.1% and optimal resolution (~10% 1/sqrt(E))
- 0.44 % global uniformity over one module (shown for 2 modules)
MC-based calibration presently extended to hadron calibration
 Challenging since MC much less reliable
Upstream fraction vs E,eta
 Impact

point:
=0.4, =0
 Accordion:
24.5 X0 thick
Calibration Constants - 2004 Run
Dependence on upstream material
•
All parameters rise when material is added
– More energy lost upstream, later part of the shower is measured.
Sensitivity to DM Material
Beam energy
accuracy
•
Procedure works also for larger
amounts of upstream matter
– Linear within the beam
energy accuracy
Sensitivity to DM Material
Apply calibration constants derived for slightly different setup
– Upstream material overestimated by 0.3 X0
- Upstream material underestimated by 0.3 X0
CTB simulation
•
•
Resulting error within 1% for E >50 GeV
2% for E >50 GeV
Longitudinal leakage
Linearity: small leakage contribution,
use of the average value only.
ELeakage ECalo   ELeak
a
Uniformity: correlation of leakage/energy
in the back E3
E Leak
E Leak
ELeak
ELeak
=1
b
E3
η,E3   α  β  E3
If no leakage parameterization,
becomes a dominant effect for
uniformity (0.6% contribution)
Energy scale
P13/P15 ~ 5 10-4 !
P13
0.34% rms
P15
0.34 %
P13/P15 0.24%
Uniformity over 300 cells < 0.5 %
Normalized energy
Understanding of the uniformity
D x Df = 0.8 x 0.15 181 cells

From ATLAS physics TDR
Source
Contribution to uniformity
Mechanics: Pb + Ar gap
< 0.25 %
Calibration: amplitude + stability
< 0.25 %
Signal Reconstruction + inductance
< 0.3 %
F modulation + longitudinal leakage
< 0.25 %
Over  < 0.8 region (181 cells)
• Correlated non-uniformity P13/P15:
0.29 %
• Uncorrelated non-uniformity : 0.17 % (P15) and 0.17 % (P13)
0.5 %
Data/MC Comparisons – Layer Fractions
E=10 GeV
E=50 GeV
• Fraction of under electron peak can be estimated by looking at late showers: E1/(E2+ E3)
• Pions depositing most of energy in Lar deposit large fraction electromagnetically,
but shower later than electrons
• f MC-pion + (1-f) MC-electron gives good description of MC
• Effect of pion contamination on reconstructed energy can be estimated from
simulated energy distributions -> effect is negliable
• shift of energy distribution with/without E1/(E2+ E3) is negliable
Correlation of passive material with Eps
Indeed 1 MIP !
This difference
causes the
linearity problem for
mip
wPS (EPS )  0.6 wPS