1. Calibration of the ATLAS Lar Barrel Calorimeter with Electron Beams Tancredi.Carli@cern.ch 19/09/2007 The ATLAS e.m. barrel calorimeter and status Calibration strategy Test-beam results

2. LAr Calorimeters: –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 The ATLAS Calorimeter

3. 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/π 0 (>3 for 50 GeV p t )  fine granularity Position and angular measurements: 50 mrad/√E  Fine strips, lateral/longitudinal segmentation Hadronic – E t miss (for SUSY) –Almost full 4π acceptance (η<4.9) Jet resolution:  E /E ~ 50%/√E  3% η<3, and  E /E ~ 100%/√E  10% 3<η<5 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 Physics Requirements

4. >22 X 0 Lead/Liquid Argon sampling calorimeter with accordion shape : Presampler in front of calo up to  = 1.8 The E.M. Barrel ATLAS Calorimeter middle back strips Main advantages: LAr as act. material inherently linear Hermetic coverage (no cracks) Longitudinal segmentation High granularity (Cu etching) Inherently radiation hard Fast readout possible

5. EM Barrel: Wheels Insertion P3 M-wheel inside the cryostat, March 2003

6. EM Barrel: Wheels Insertion P3 ATLAS barrel calorimeter being moved to the IP, Nov. 2005

7. EM Barrel: Wheels Insertion P3 ATLAS endcap calorimeters installation, winter-spring 2006

8. Commissioning – The Road to Physics 2005200620072008 1: Testbeams 2: Subdetector Installation, Cosmic Ray Commissioning 3: First LHC collisions 4: First Physics Sommer ’07: Global cosmic run with DAQ of ATLAS detector Final cool down ~30 k events in barrel >03/07 weekly cosmic data taking together with Endcap A ~100 k events

9. Test-beam 2002: Uniformity: 3 production modules  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 Test beam Setups

10. LAr electronic calibration F = ADC2DAC DAC2A  A2MeV  f samp Scan input current (DAC) Fit DAC vs ADC curve with a second order polynomial, outside of saturation region ADC  MeV conversion Every 8 hours 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) response to current pulse Every change of cabling pedestals and noise Cells are read with no input signal to obtain: Pedestal Noise Noise autocorrelation (OFC computation) Every 8 hours Energy Raw Samples Optimal Filtering Coefficients PedestalsADC to GeV Amplitude ( Energy) Pedestal subtracted 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).

11. 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 On the Calibration of longitudinally Segmented Sampling Calorimeter Shower Use one sampling fraction for all compartments  apply energy dependent correction

12. Sampling Fraction Correction Correction to sampling fraction in accordion: - intrinsic E-dependence of s.f. - I/E conversion - out-of-cluster (fiducial volume) correction 1%

13. Accordion Calorimeter Cryostat Walls Presampler e-e- Accordion Sampling Calorimeter –Segmentation in three longitudinal compartments Presampler (Significant) amount of dead material upstream (~2-3 X 0 ) –Cryostat wall, solenoid, … 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 test- beam. Material in front of the Accordion in ATLAS Correction for Dead Material Losses

14. Opt. Linearity Opt. Resolution A simple weight is not sufficient! DM Correction using the Presampler I Assume for a moment perfectly calibrated Lar calorimeter:

15. 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 Shower e-e-   e+e+ e-e- Presampler Accordion Dead Material Dead Material

16. DM Correction using the Presampler II Offset accounts for energy loss by particles stopping before PS - Ionisation energy loss - low-E Bremsstrahlung photons - photo-photonuclear interactions Weight accounts for energy loss (partly) traversing the DM and the PS energy dependent e-e-   e+e+ e-e- Presampler Dead Material Dead Material

17. Significant amount of inactive material (~0.5 X 0 ) –Electronics boards and cables immersed in LAr –Dependence on impact point Shower already developed (about 2-3 X 0 before Accordion) Best correlation between measured quantities and energy deposit in the gap: Empirically found e-e-   e+e+ e-e- Presampler Dead Material Dead Material DM Correction between PS and Strips

18. Shower e-e-   e+e+ e-e- PresamplerAccordion Dead Material Dead Material 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. 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 Correction to sampling fraction in accordion: - intrinsic E-dependence of s.f. - I/E conversion - out-of-cluster correction +Eleak

19. Data MC Comparison – Layer Energy Sharing Most difficult: correct description of DM material Band due to uncertainties in material estimation

20.  PS Strips Middle Back Data MC Deposited energies = f(  ) in the PS and in the 3 calorimeter compartments before applying the correction factors a,b,c,d Excellent Data / MC agreement in all samplings Data MC Comparison – Layer Energy Sharing Mean visible energy for 245 GeV e-

21. Data/MC Comparisons – Radial Extension Good description also for asymmetry 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 ?

22. 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

23. Linearity Result within 0.1% for 15-180 GeV, E=10 GeV 4 per mil too low, reason unclear…

24. Systematics..within 0.1%

25. Resolution Result Good resolution while preserving good linearity

26. Resolution is much better described in new G4 version ! Preliminary Phi-impact correction not applied Data MC comparison - Resolution G4.8 has completely revised multiple-scattering

27. Current to Energy Factor in ATLAS Barrel EM Calorimeter From calculation using field-Maps: G. Unal: ATLAS-SIM 09/05 From comparison of data and MC: Much better understanding of absolute energy scale from first principles ! (Some effects missing in simulation and calculation,e.g. recombination effect in Lar) Assuming calo is simple condensator and knowing Lar drift time: electrode Pb absorber

28. Calibration Parameter vs Eta,, E= 245 GeV e-, scan in  Internal ATLAS modul number related to 

29. Uniformity barrel results Module P13 Module P15 0,44% 0,7-0,9% 245.6 GeV245.7 GeV Resolution Uniformity TDR requirement: 0.7%

30. 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)MC describes data well 2)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

31. Accordion: 24.5 X 0 thick Upstream fraction vs E,eta Impact point:  =0.4,  =0

32. 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.

33. Beam energy accuracy Procedure works also for larger amounts of upstream matter –Linear within the beam energy accuracy Sensitivity to DM Material

34. CTB simulation Resulting error within 1% for E >50 GeV 2% for E >50 GeV Apply calibration constants derived for slightly different setup –Upstream material overestimated by 0.3 X 0 - Upstream material underestimated by 0.3 X 0 Sensitivity to DM Material

35. Longitudinal leakage Linearity: small leakage contribution, use of the average value only. Uniformity: correlation of leakage/energy in the back E 3   If no leakage parameterization, becomes a dominant effect for uniformity (0.6% contribution)  = 1

36. Understanding of the uniformity Uniformity over 300 cells < 0.5 % Over  < 0.8 region (181 cells) Correlated non-uniformity P13/P15: 0.29 % Uncorrelated non-uniformity : 0.17 % (P15) and 0.17 % (P13) SourceContribution to uniformity Mechanics: Pb + Ar gap< 0.25 % Calibration: amplitude + stability < 0.25 % Signal Reconstruction + inductance< 0.3 %  modulation + longitudinal leakage < 0.25 % 0.5 % P13 0.34% rms P15 0.34 % P13/P15 0.24% From ATLAS physics TDR Energy scale P13/P15 ~ 5 10 -4 !  x  = 0.8 x 0.15 181 cells Normalized energy 

37. Data/MC Comparisons – Layer Fractions E=50 GeV E=10 GeV Fraction of under electron peak can be estimated by looking at late showers: E 1 /(E 2 + E 3 ) 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 E 1 /(E 2 + E 3 ) is negliable

38. Correlation of passive material with Eps This difference causes the linearity problem for Indeed 1 MIP !