AHCAL Energy Resolution Katja Seidel MPI for Physics & Excellence Cluster ’Universe’ Munich, Germany for the CALICE Collaboration International Linear Collider Workshop 2010 Beijing, China 27 March 2010 K. Seidel (MPI for Physics) energy reconstruction 27 March 2010 0 / 15
Outline 1 CALICE calorimeter prototypes 2 Calibration of the AHCAL 3 Electromagnetic Showers 4 Hadronic Showers - Software Compensation Global Method Cluster Energy Density Weighting Neural Network Local Method Single Cell Energy Weighting 5 Conclusions K. Seidel (MPI for Physics) energy reconstruction 27 March 2010 1 / 15
CALICE Calorimeter Prototype Program ECAL HCAL TCMT Drift Chambers Cherenkov Detector Beam Sc2 Sc1 Sc3 Sc4 Muon Trigger Scintillators Extensive Test Beam Program DESY: 2006 CERN: 2006, 2007 FNAL: 2008, 2009 Particle Types: µ, e ± , π ± , p Particle Energies: 1 GeV to 80 GeV K. Seidel (MPI for Physics) energy reconstruction 27 March 2010 2 / 15
CALICE Calorimeter Prototype Program ECAL HCAL TCMT Drift Chambers Cherenkov Detector Beam Sc2 Sc1 Sc3 Sc4 Muon Trigger Scintillators CERN 2007 ECAL: Silicon-Tungsten Calorimeter 30 Layers; 1 × 1 cm 2 readout pads, 1.4, 2.8, 4.2 cm thick absorber plates; 30 X 0 , 1 λ 0 HCAL: Scintillator-Steel Calorimeter 38 Layers; 1.8 cm thick absorber plates, 47 X 0 4 . 5 λ 0 TCMT: Scintillator-Steel Calorimeter 8 layers: 2 cm thick absorber plates, 8 layers: 10 cm thick absorber plates, 5 × 100 cm scintillator bars; 5.8 λ 0 K. Seidel (MPI for Physics) energy reconstruction 27 March 2010 2 / 15
CALICE Analog HCAL Iron absorber structure Active layers: scintillator tiles Tile sizes: 3 × 3 cm 2 , 6 × 6 cm 2 , 12 × 12 cm 2 Light collection via wavelength shifting fiber Readout via SiPM High granularity in AHCAL center → in the shower core K. Seidel (MPI for Physics) energy reconstruction 27 March 2010 3 / 15
Calibration of the AHCAL Signal Saturation SiPM pixel number limited → only limited number of photons can be counted Auto-calibration of SiPM gain: Low-intensity LED light coupled into each detector cell Gain measurement MIP-Calibration with Muons Complete detector illuminated with high energy muons Equalization of cell response by matching the MPV position Temperature effect correction SiPM gain SiPM amplitude ⇒ All effects included into event reconstruction and Monte Carlo digitization. K. Seidel (MPI for Physics) energy reconstruction 27 March 2010 4 / 15
Calibration of the AHCAL Signal Saturation SiPM pixel number limited → only limited number of photons can be counted Auto-calibration of SiPM gain: Low-intensity LED light coupled into each detector cell Gain measurement MIP-Calibration with Muons Complete detector illuminated with high energy muons Equalization of cell response by matching the MPV position Temperature effect correction SiPM gain SiPM amplitude ⇒ All effects included into event reconstruction and Monte Carlo digitization. K. Seidel (MPI for Physics) energy reconstruction 27 March 2010 4 / 15
Calibration of the AHCAL Signal Saturation SiPM pixel number limited → only limited number of photons can be counted Auto-calibration of SiPM gain: Low-intensity LED light coupled into each detector cell Gain measurement MIP-Calibration with Muons Complete detector illuminated with high energy muons Equalization of cell response by matching the MPV position Temperature effect correction SiPM gain SiPM amplitude ⇒ All effects included into event reconstruction and Monte Carlo digitization. K. Seidel (MPI for Physics) energy reconstruction 27 March 2010 4 / 15
Electromagnetic Showers in the AHCAL Residual to linearity [%] Residual to linearity [%] Digitized MC 4 4 Positron data Positron test beam data from 10 GeV to 50 GeV systematic uncertainty 2 2 0 0 Comparison to Monte Carlo data -2 -2 Data taking without ECAL in front of -4 -4 HCAL -6 -6 Linearity of detector response of 1.5 % up CALICE preliminary to 30 GeV -8 -8 10 10 20 20 30 30 40 40 50 50 Non-Linearity at higher energies not yet Beam energy [GeV] Beam energy [GeV] reproduced in MC → Saturation handling 0.08 relative reconstructed width Positron data Digitized MC Energy Resolution Data 0.07 true MC systematic uncertainty Fit in the range from 10 GeV to 30 GeV 0.06 σ a with: E [ GeV ] = √ E [ GeV ] ⊕ b 0.05 a = 22.5 ± 0.1(stat) ± 0.4(syst) % 0.04 b = 0.0 ± 0.1(stat) ± 0.1(syst) % 0.03 CALICE preliminary 0.02 10 20 30 40 50 Beam energy [GeV] K. Seidel (MPI for Physics) energy reconstruction 27 March 2010 5 / 15
Hadronic Showers Detector Response 1200 Total HCAL Energy in Cells > 4.5 MIP/cell [MIP] ◮ CALICE: non-compensating sampling (a) CALICE Preliminary calorimeter 1000 20 GeV pions, no weighting ◮ Calorimeter response to hadrons is smaller 1 = ) l a t o than to electrons of the same energy t 800 ( E / ) d l o h ◮ CALICE AHCAL e s π ∼ 1 . 2 e r h t > 600 ( E 400 Software Compensation ⇒ Identification of electromagnetic and . 3 0 = a l ) o t ( t / E 200 d ) o l s h hadronic shower component fractions r e t h ( > E ⇒ Improve energy resolution 0 0 200 400 600 800 1000 1200 ⇒ Improve linearity of detector response Total HCAL Energy [MIP] Method: Electromagnetic showers tend to be denser than purely hadronic ones Correlations between reconstructed energy and energy in high density shower regions ⇒ Test Local and Global Techniques K. Seidel (MPI for Physics) energy reconstruction 27 March 2010 6 / 15
Cluster-Based Software Compensation Two global methods based on cluster as a whole - no subcluster analysis Look at global cluster properties 1 Shower reconstruction in AHCAL and TCMT ECAL HCAL TCMT Showers are required to start in the AHCAL 2 Determination of shower variables from test beam and simulated data Sc2 3 Analyses developed on Monte Carlo data FTF BIC Muon Trigger Energy density weighting technique Neural Network from TMVA 4 Application of weight or trained neural network on test beam data K. Seidel (MPI for Physics) energy reconstruction 27 March 2010 7 / 15
Cluster Energy Density Weighting Technique Hadronic Showers with high energy density ρ ⇒ Higher electromagnetic content ⇒ Higher reconstructed energy E rec [ GeV ] = E rec [ MIP ] · ω ( ρ, E ) K. Seidel (MPI for Physics) energy reconstruction 27 March 2010 8 / 15
Cluster Energy Density Weighting Technique entries entries 3 3 10 10 Hadronic Showers with high energy density ρ ⇒ Higher electromagnetic content ⇒ Higher reconstructed energy 2 2 10 10 E rec [ GeV ] = E rec [ MIP ] · ω ( ρ, E ) 10 10 Individual weights with minimization of function 1 1 CALICE Preliminary χ 2 = E rec · ω − E beam 0 0 0.05 0.05 0.1 0.1 0.15 0.15 0.2 0.2 0.25 0.25 0.3 0.3 0.35 0.35 0.4 0.4 Cluster Energy Density [MIP/volume] Cluster Energy Density [MIP/volume] K. Seidel (MPI for Physics) energy reconstruction 27 March 2010 8 / 15
Cluster Energy Density Weighting Technique entries entries 3 3 10 10 Hadronic Showers with high energy density ρ ⇒ Higher electromagnetic content ⇒ Higher reconstructed energy 2 2 10 10 E rec [ GeV ] = E rec [ MIP ] · ω ( ρ, E ) 10 10 Individual weights with minimization of function 1 1 CALICE Preliminary χ 2 = E rec · ω − E beam 0 0 0.05 0.05 0.1 0.1 0.15 0.15 0.2 0.2 0.25 0.25 0.3 0.3 0.35 0.35 0.4 0.4 Cluster Energy Density [MIP/volume] Cluster Energy Density [MIP/volume] Parameterization of the individual weights via ω = a ( E ) · ρ + b ( E ) K. Seidel (MPI for Physics) energy reconstruction 27 March 2010 8 / 15
Cluster Energy Density Weighting Technique fit parameter a Hadronic Showers with high energy density ρ -0.02 ⇒ Higher electromagnetic content -0.03 ⇒ Higher reconstructed energy -0.04 E rec [ GeV ] = E rec [ MIP ] · ω ( ρ, E ) -0.05 -0.06 Individual weights with minimization of function -0.07 χ 2 = E rec · ω − E beam 10 20 30 40 50 60 70 80 beam energy [GeV] fit parameter b 0.0345 Parameterization of the individual weights 0.034 via ω = a ( E ) · ρ + b ( E ) 0.0335 0.033 0.0325 Parameterization of energy dependence with 0.032 function for a ( E ) und b ( E ) , E = E rec 0.0315 0.031 0.0305 0.03 ⇒ Determination of weights independent of beam energy! 10 20 30 40 50 60 70 80 beam energy [GeV] K. Seidel (MPI for Physics) energy reconstruction 27 March 2010 8 / 15
Cluster Energy Density Weighting - Results Energy Resolution: 0.25 1.05 E/E single CALICE Preliminary CALICE Preliminary ∆ σ 1 / Energy resolution - FTF_BIC weights weight 0.2 test beam data: 0.95 σ constant cluster weight 0.9 energy dependent parametrization 0.15 0.85 0.8 0.1 0.75 Ratio of energy resolutions - FTF_BIC weights 0.7 0.05 Fit: a/ E ⊕ b ⊕ c GeV/E test beam data a = 64.8 0.2% b = 0.00 0.80% c = 0.000 0.208 [GeV] ± ± ± 0.65 energy dependent parametrization / constant cluster weight a = 53.5 ± 0.9% b = 2.21 ± 0.37% c = 0.488 ± 0.118 [GeV] 0 0.6 0 10 20 30 40 50 60 70 80 90 0 10 20 30 40 50 60 70 80 90 beam Energy [GeV] beam Energy [GeV] Weight parametrization from Monte Carlo derived Weights applied on test beam data Energy resolution improvement: → approx. 15 % K. Seidel (MPI for Physics) energy reconstruction 27 March 2010 9 / 15
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