Hadronic Shower Reconstruction in an Imaging Calorimeter Marina Chadeeva, ITEP, Moscow for the CALICE Collaboration Marina Chadeeva (ITEP) 2011 IEEE NSS, Valencia, Spain October 27, 2011 1 / 15
Imaging calorimeters for PFA-based reconstruction Imaging calorimeters for PFA-based reconstruction Detectors for a future Linear Colliders σ jet Goal for detectors at ILC: E jet ∼ 3-4% Classic calorimeter approach not sufficient Possible solution: Particle Flow Analysis ⇒ high granularity required ILD event display CAlorimeters for LInear Collider Experiments CALICE calorimeter prototypes high transversal and longitudinal granularity test of detector concepts test of PFA concept test of MC models study of 3D shower profiles Scintillator tiles assembled in one layer of hadronic calorimeter Marina Chadeeva (ITEP) 2011 IEEE NSS, Valencia, Spain October 27, 2011 2 / 15
Imaging calorimeters for PFA-based reconstruction CALICE test beam setup Test beam campaign : since 2006 up to now at DESY, CERN, FNAL Muons, electrons, hadrons in the energy range 1-180 GeV, different setup configurations CALICE test beam setup at CERN in 2007 ECAL: Si-W, ∼ 0.8 λ I (30 layers), 18x18cm 2 , ∼ 10000 cells: 1x1cm 2 HCAL: Sc-Fe, ∼ 4.5 λ I (38 layers), ∼ 1x1m 2 , 7608 tiles: 3x3, 6x6, 12x12cm 2 analogue with SiPM read-out TCMT: Sc-Fe, ∼ 5 λ I (16 layers), 90x90cm 2 , 5-cm strips with SiPM read-out Units of MIP (visible signal from Minimum Ionizing Particle) used to equalize cell-by-cell response 30 GeV pion shower in ECAL and HCAL Marina Chadeeva (ITEP) 2011 IEEE NSS, Valencia, Spain October 27, 2011 3 / 15
Imaging calorimeters for PFA-based reconstruction Advantages of high granularity Hadronic shower structure First inelastic interaction Identification of track segments and high density clusters Spatial energy density distribution 30 GeV pion event with track in ECAL and hits above 3.5 MIP shown in red Particle Flow Analysis Possibility to disentangle showers induced by charged and neutral particles Software compensation Improvement of the energy resolution by means of software compensation techniques based on the analysis of the detailed energy density spectra Marina Chadeeva (ITEP) 2011 IEEE NSS, Valencia, Spain October 27, 2011 4 / 15
Software compensation Basic idea and techniques Software compensation: basic idea and techniques Hadronic shower comprises electromagnetic and hadronic components with significant event-by-event fluctuations of electromagnetic fraction f EM Non-compensating calorimeter: different response to electrons and hadrons ⇒ Hadron energy resolution is deteriorated w.r.t. electromagnetic one Software compensation: take into account f EM fluctuations to improve resolution Global Compensation technique (GC) Local Compensation technique (LC) applying one weight calculated from the weighting of signals of individual cells energy density spectrum to energy sum depending on the cell energy density Energy density spectrum Correlation between global compensation factor and HCAL energy sum Both methods : energy dependent weights, parameters of the energy dependence extracted from test beam data, do not require a prior knowledge of particle energy Marina Chadeeva (ITEP) 2011 IEEE NSS, Valencia, Spain October 27, 2011 5 / 15
Software compensation Application to test beam data Software compensation for π − and π + test beam data Hadron energy reconstruction Hadron energy resolution E reco = E track ECAL + E HCAL + E TCMT 0.22 reco Selected events with track in ECAL ⊕ ⊕ Fit: a/ E b c/E /E 0.2 ± ± Initial: a = 57.6 0.4% b = 1.6 0.3% c = 0.18 reco E HCAL non-corrected or corrected ± ± GC: a = 45.8 0.3% b = 1.6 0.2% c = 0.18 σ 0.18 ± ± LC: a = 44.9 0.3% b = 1.6 0.2% c = 0.18 Weights for software compensation: 0.16 depend on total event energy 0.14 π - calculated from uncorrected E reco 0.12 π + 0.1 energy dependence parameters extracted from data 0.08 0.05 0.06 beam CALICE Preliminary CALICE Preliminary 0.04 )/E 0.04 beam 0.03 0 10 20 30 40 50 60 70 80 90 - E E [GeV] 0.02 beam reco 0.01 (E 0 Stochastic: from ∼ 58% ↓ to ∼ 45% -0.01 Initial: π - -0.02 Constant : 1.6% - not changed Initial: π + π GC: - -0.03 π GC: + π Noise: 0.18 GeV fixed for full setup Systematics for initial - π LC: - -0.04 π + LC: Similar improvement of relative resolution -0.05 0 10 20 30 40 50 60 70 80 90 E [GeV] for π − and π + beam Relative residuals to beam energy Marina Chadeeva (ITEP) 2011 IEEE NSS, Valencia, Spain October 27, 2011 6 / 15
Software compensation Application to test beam data Software compensation: improvement of resolution Energy distributions before and after compensation ( χ 2 NDF < 2 for Gaussian fits) 6000 5000 7000 Events / 0.5 GeV Events / 1.0 GeV Events / 1.0 GeV CALICE Preliminary CALICE Preliminary CALICE Preliminary 4500 π π 6000 π - + - 5000 (a) 10 GeV (b) 40 GeV (c) 80 GeV 4000 Initial Initial Initial 5000 3500 LC LC LC 4000 GC 3000 GC GC 4000 3000 2500 3000 2000 2000 1500 2000 1000 1000 1000 500 0 0 0 2 4 6 8 10 12 14 16 18 25 30 35 40 45 50 55 60 60 65 70 75 80 85 90 95 100 Reconstructed energy, GeV Reconstructed energy, GeV Reconstructed energy, GeV initial 1 CALICE Preliminary σ Relative improvement of absolute resolution / SC 0.95 σ 12% < σ SC /σ initial < 25% 0.9 0.85 Similar improvement for π − and π + 0.8 0.75 Local approach gives 3% better improvement 0.7 in the energy range 25-60 GeV than the global one π GC: - 0.65 π GC: + π LC: - 0.6 Global uses twice as less parameters as the local π + LC: 0 10 20 30 40 50 60 70 80 90 E [GeV] beam Marina Chadeeva (ITEP) 2011 IEEE NSS, Valencia, Spain October 27, 2011 7 / 15
Software compensation Comparison with MC Software compensation: simulated samples and data Local compensation Global compensation 0.22 0.22 reco reco ⊕ ⊕ Fit: a/ E ⊕ b ⊕ c/E /E Fit: a/ E b c/E /E 0.2 0.2 ± ± ± ± Data LC: a = 44.9 0.3% b = 1.6 0.2% c = 0.18 Data GC: a = 45.8 0.3% b = 1.6 0.2% c = 0.18 GEANT 4.9.4 reco reco QGSP_BERT LC: a = 41.7 ± 0.2% b = 2.3 ± 0.1% c = 0.18 QGSP_BERT GC: a = 42.9 ± 0.1% b = 0.0 ± 0.3% c = 0.18 σ 0.18 ± ± σ 0.18 ± ± FTF_BIC LC: a = 40.2 0.3% b = 3.2 0.1% c = 0.18 FTF_BIC GC: a = 42.4 0.3% b = 1.8 0.2% c = 0.18 QGSP BERT 0.16 0.16 FTF BIC 0.14 0.14 π - π - Data LC: Data GC: parameters 0.12 Data LC: π + 0.12 Data GC: π + π π QGSP_BERT LC: - QGSP_BERT GC: - QGSP_BERT LC: π + QGSP_BERT GC: π + 0.1 0.1 for compensation FTF_BIC LC: π - FTF_BIC GC: π - π π FTF_BIC LC: + FTF_BIC GC: + 0.08 0.08 extracted 0.06 0.06 from data CALICE Preliminary CALICE Preliminary 0.04 0.04 0 10 20 30 40 50 60 70 80 90 0 10 20 30 40 50 60 70 80 90 E [GeV] E [GeV] beam beam 1 1 initial initial CALICE Preliminary CALICE Preliminary 0.95 0.95 Local: MC σ σ / / SC SC 0.9 0.9 σ σ follows data 0.85 0.85 0.8 0.8 Global: MC 0.75 0.75 0.7 0.7 predicts further 0.65 0.65 Data LC: π - Data GC: π - improvement Data LC: π + Data GC: π + 0.6 π 0.6 π QGSP_BERT LC: - QGSP_BERT GC: - π + π QGSP_BERT LC: QGSP_BERT GC: + above 40 GeV π π 0.55 FTF_BIC LC: - 0.55 FTF_BIC GC: - π + π FTF_BIC LC: FTF_BIC GC: + 0.5 0.5 0 10 20 30 40 50 60 70 80 90 0 10 20 30 40 50 60 70 80 90 E [GeV] E [GeV] beam beam Marina Chadeeva (ITEP) 2011 IEEE NSS, Valencia, Spain October 27, 2011 8 / 15
PFA test using test beam data PFA test using test beam data Pairs of single particle events from the CALICE prototype are superimposed and mapped into ILD geometry PandoraPFA was used as a reconstruction tool Goal is to compare disentangling efficiency: test beam data vs. GEANT 4.9.2 simulations 10 GeV and 30 GeV pion showers at 12 cm Estimated confusion term: RMS deviation of a neutral cluster reconstructed energy from its measured energy in the vicinity of a charged cluster 10 GeV and 30 GeV pion showers at 24 cm Confusion term agrees for QGSP BERT and data Marina Chadeeva (ITEP) 2011 IEEE NSS, Valencia, Spain October 27, 2011 9 / 15
PFA test using test beam data Summary Hadron energy resolution of the CALICE scintillator-steel analogue HCAL was estimated for π − and π + test beam data samples in the range from 10 to 80 GeV 57 . 5% 0 . 18 intrinsic resolution: √ E / GeV ⊕ 1 . 6% ⊕ E / GeV , linearity of response within ± 2% Local and global software compensation techniques were developed for the CALICE AHCAL and applied to test beam data 45% √ contribution from stochastic term reduced down to ∼ E / GeV PFA performance was compared for CALICE test beam data and GEANT 4.9.2 simulated samples similar performance observed for QGSP BERT model and data ⇒ extrapolation to jets in the complete detector is reliable Marina Chadeeva (ITEP) 2011 IEEE NSS, Valencia, Spain October 27, 2011 10 / 15
Backup slides Backup slides Marina Chadeeva (ITEP) 2011 IEEE NSS, Valencia, Spain October 27, 2011 11 / 15
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