particle flow calorimetry: experimental status and ...indico.ihep.ac.cn/event/910/session/17/... ·...

Particle Flow Calorimetry:Experimental Status and Technical

DevelopmentsFelix Sefkow

Friday, May 14, 2010

MC

Particle Flow Calorimetry: Experimental Felix Sefkow CALOR 2010, Beijing, May 10-14, 2010

What we learnt from CALICE:

• Introduction: – testing PFLOW calorimetry

• In real terms:– Validate simulation– test algorithms– test technologies and establish

feasibility

2


MC


Understand particle flow performance

• Particle flow is always better– even at high jet energies

• HCAL resolution does matter– also for confusion term

• Leakage plays a role, too

3

ARTICLE IN PRESS

neutral hadrons being lost within charged hadron showers. For alljet energies considered, fragments from charged hadrons, whichtend to be relatively low in energy, do not contribute significantlyto the jet energy resolution.

The numbers in Table 5 can be used to obtain an semi-empirical parameterisation of the jet energy resolution:

rms90E

!21!!!E

p " 0:7" 0:004E" 2:1E

100

" #0:3

%

where E is the jet energy in GeV. The four terms in the expression,respectively, represent: the intrinsic calorimetric resolution;imperfect tracking; leakage and confusion. This functional formis shown in Fig. 10. It is worth noting that the predicted jet energyresolutions for 375 and 500GeV jets are in good agreement withthose found for MC events (see Table 3); these data were not usedin the determination of the parameterisation of the jet energyresolution.

For a significant range of the jet energies relevant for the ILC,high granularity PFlow results in a jet energy resolution which isroughly a factor two better than the best achieved at LEP(sE=E! 6:8% at

!!s

p!MZ). The ILC jet energy goal of sE=Eo3:8%

is reached in the jet energy range 40–420GeV.Fig. 10 also shows a parameterisation of the jet energy

resolution #rms90$ obtained from a simple sum of the total

calorimetric energy deposited in the ILD detector concept. Thedegradation in energy resolution for high energy jets is due tonon-containment of hadronic showers. It is worth noting thateven for the highest energies jets considered, PFlow reconstruc-tion significantly improves the resolution compared to the purelycalorimetric approach. The performance of PFlow calorimetry alsois compared to 50%=

!!!!!!!!!!!!!!!E#GeV$

p" 3:0% which is intended to give an

indication of the resolution which might be achieved using atraditional calorimetric approach. This parameterisation effec-tively assumes an infinitely deep HCAL as it does not correctlyaccount for the effect of leakage (which is why it deviatessignificantly from the ILD Calorimetric only curve at highenergies).

8. Dependence on hadron shower modelling

The results of the above studies rely on the accuracy of the MCsimulation in describing EM and hadronic showers. The Geant4MC provides a good description of EM showers as has beendemonstrated in a series of test-beam experiments [27] using aSilicon–Tungsten ECAL of the type assumed for the ILD detector

Table 5The PFlow jet energy resolution obtained with PandoraPFA broken down into contributions from: intrinsic calorimeter resolution, imperfect tracking, leakage andconfusion.

Contribution Jet Energy Resolution rms90#Ej$=Ej

Ej ! 45GeV Ej ! 100GeV Ej ! 180GeV Ej ! 250GeV

Total (%) 3.7 2.9 3.0 3.1Resolution (%) 3.0 2.0 1.6 1.3Tracking (%) 1.2 0.7 0.8 0.8Leakage (%) 0.1 0.5 0.8 1.0Other (%) 0.6 0.5 0.9 1.0Confusion (%) 1.7 1.8 2.1 2.3(i) Confusion (photons) (%) 0.8 1.0 1.1 1.3(ii) Confusion (neutral hadrons) (%) 0.9 1.3 1.7 1.8(iii) Confusion (charged hadrons) (%) 1.2 0.7 0.5 0.2

The different confusion terms correspond to: (i) hits from photons which are lost in charged hadrons; (ii) hits from neutral hadrons that are lost in charged hadron clusters;and (iii) hits from charged hadrons that are reconstructed as a neutral hadron cluster.

EJET/GeV

rms 9

0/Eje

t [%

]

0

1

2

3

4 TotalResolutionConfusion

OtherLeakage

0 50 100 150 200 250

Fig. 9. The contributions to the PFlow jet energy resolution obtained withPandoraPFA as a function of energy. The total is (approximately) the quadraturesum of the components.

Ejet/GeV

rms 9

0/Eje

t [%

]

0

2

4

6

8

10Particle Flow (ILD+PandoraPFA)Particle Flow (confusion term)Calorimeter Only (ILD)

E(GeV) ! 3.0 %50 % /

0 100 200 300 400 500

Fig. 10. The empirical functional form of the jet energy resolution obtained fromPFlow calorimetry (PandoraPFA and the ILD concept). The estimated contributionfrom the confusion term only is shown (dotted). The dot-dashed curve shows aparameterisation of the jet energy resolution obtained from the total calorimetricenergy deposition in the ILD detector. In addition, the dashed curve,50%=

!!!!!!!!!!!!!!!E#GeV$

p" 3:0%, is shown to give an indication of the resolution achievable

using a traditional calorimetric approach.

M.A. Thomson / Nuclear Instruments and Methods in Physics Research A 611 (2009) 25–4034

ARTICLE IN PRESS



rms90E

!21!!!E

p " 0:7" 0:004E" 2:1E

100

" #0:3

%



!!s





!!!!!!!!!!!!!!!E#GeV$








Total (%) 3.7 2.9 3.0 3.1Resolution (%) 3.0 2.0 1.6 1.3Tracking (%) 1.2 0.7 0.8 0.8Leakage (%) 0.1 0.5 0.8 1.0Other (%) 0.6 0.5 0.9 1.0Confusion (%) 1.7 1.8 2.1 2.3

(i) Confusion (photons) (%) 0.8 1.0 1.1 1.3(ii) Confusion (neutral hadrons) (%) 0.9 1.3 1.7 1.8(iii) Confusion (charged hadrons) (%) 1.2 0.7 0.5 0.2


EJET/GeV

rms 9

0/Eje

t [%

]

0

1

2

3


OtherLeakage

0 50 100 150 200 250


Ejet/GeV

rms 9

0/Eje

t [%

]

0

2

4

6

8


E(GeV) ! 3.0 %50 % /

0 100 200 300 400 500


!!!!!!!!!!!!!!!E#GeV$




Resolution Tracking Leakage Confusion


MC


How to test it experimentally?

• “Jets” from thin targets?– Would require magnet

spectroscopy and large acceptance ECAL + HCAL

• Simulation study

– Multi-million $ experiment– and still inconclusive

• need to control target losses and acceptance losses at 1-2% level

• model dependence

4

20 GeV pion, 0.8 T

• Factorize the problem: check the ingredients– simulation– algorithms– technical performance


MC


Critical questions

• Are the basic detector performance predictions confirmed?

• Are the shower parameters well enough simulated to predict PFLOW?

• Is the substructure actually there and well modeled?• Can one realize the potential of software

compensation for gain and linearity?• Can we verify the "double track resolution" of a

tracking calorimeter?• Are detector effects under control?• Can we calibrate millions of cells and control

stability?• Can we build the detector without spoiling it by dead

material everywhere?• What are the relative merits of different

technologies for PFLOW?

5

The PandoraPFA Algorithm M

ark Thomson

12

! High granularity Pflow

reconstruction is highly non-trivial !

PandoraPFA consists of a many com

plex steps (not all shown)

Clustering

Topological Association

30 GeV

12 GeV

18 G

eV

Iterative Reclustering

9 GeV

9 GeV

6 GeV

Photon ID Fragm

ent ID

Calor 2010, Beijing, 11/5/2010

MT, N

IM 611 (2009) 24-40


MC


Technology tree

6

• mostly ILD, SiD• ILC, CLIC


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Overall status

• Major test beam campaigns at DESY, CERN and Fermilab• 1st generation “physics” prototypes• Mostly combined set-ups ECAL-HCAL

• Si W ECAL 2005-08• Scint W ECAL 2007-09• Scint Fe HCAL 2006-09• RPC Fe HCAL to start end 2010

• 2nd generation “technical” prototypes: construction and commissioning ongoing, single or few layers

• Complete detectors to start with RPC-Fe HCAL 2011• ECAL, Scint Fe HCAL later

7


Validation of the simulationsdetector performance

shower models

8


MC


ECAL options

• W Si or Sci: common mechanics, similar electronics

9

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! %1/437$5/#$0/1"0B0,56$4,$561#/,51#$652#/7:FG$&@

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!"#$%&'()#*#(%)+#"#$%,'(! "#$%&'()*%*+),%')%$-+./+*$%*#$%012345%6$(&7(8+91$%&7(%$.$1*(79):! "#$%$.$1*(79;*/9$<%=$+8%179*+'9)%)78$%!; >%"; 179*+8'9+*'79):! 3$($9,7-%17/9*$(%)'?9+.)%#+-$%=$$9%/)$<%&7(%*#$%@4A%*('??$(B#7C$-$(%)*'..%7&&.'9$%$-$9*%)$.$1*'79%')%9$1$))+(D%*7%6/('&D%*#$$.$1*(79%)+86.$:! 2-$9*%)$.$1*'79%')%<79$%=+)$<%79%E; 579?'*/</9+.%>%.+*$(+.%)#7C$(%)#+6$; !; >%"; -$*7%=D%*#$%F345%)'?9+.%.71+*$<%+*%<7C9)*($+8%

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/$=*-$:(63-$8*>?@A7*<(B !@ 2(/.;

! C")*"(64$%3"#63-4$*45*39(*8($("/3(=*!! D-39*E6-$3-,,/34"@FG1H;

!!!!! "#$""""

!! =(3(63-4$*%#66(%%5#,I


MC


Pions in the SiW ECAL

• test Geant 4 predictions with 1 cm2 granularity

• sensitive to shower decomposition• favor recent G4 physics lists• certainly not perfect - certainly not bad

either!

10

Shower Components:

- electrons/positrons knock-on, ionisation, etc.- protons from nuclear fragmentation- mesons- others- sum


MC


Fe Scint tile HCAL

• Present-day simulation quality requires good detector understanding to discriminate

• Fluctuations also well reproduced

11

Frank Simon ([email protected])Particle Showers in a Highly Granular HCALCALOR2010, Beijing, China

Imaging of Hadronic Showers

• Highly granular calorimetry motivated by the Particle Flow Concept:

Separation of particle showers within hadronic jets

2

• Physics prototypes in test beams provide unprecedented 3D information of the

structure of hadronic showers

! Excellent possibilities for the validation and the further development of

hadronic shower models in simulation codes (GEANT4)!


Shower Start & Shower Profiles

• Identification of the shower start point:

Increase of activity in the detector

7

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• Full profile of showers with high

resolution:

Perfect for detailed studies and

MC comparisons

10 GeV !-


Mean Shower Radius

• Mean radius, energy weighted

9

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2D profile from starting point


MC


Shower fine structure

• Could have the same global parameters with “clouds” or “trees”• Powerful tool to check models• Surprisingly good agreement already

12


Digging Deeper: 3D Substructure - Particle Tracks

11

Beam25 GeV !-

ECAL upstream

identified tracks

• Imaging capability of detector

allows the identification of

individual MIP-like tracks

within hadronic showers


Track Segments in Hadronic Showers: Length

• Track length and slope well described by all models:

• Beam composition well modeled, satisfactory inclusion of detector noise

• High energy cross sections well described

12

) [HCal Layer])!cos(

lengthReal Track Length (

5 10 15 20 25 30 35

MC

/Da

ta

0.8

1

1.2

1.4

1.65 10 15 20 25 30 35

En

trie

s (n

orm

aliz

ed

to

no

. E

ven

ts)

-210

-110

data_rec_v0408-"

LHEP-"

QGS_BIC-"

QGSP_BERT_TRV-"

QGSP_BERT-"

FTF_BIC-"

FTFP_BERT-"

CALICE preliminary

Energy [GeV]10 20 30 40 50 60 70 80

MC

/Da

ta

0.96

0.97

0.98

0.99

1

1.01

1.02 10 20 30 40 50 60 70 80

Me

an

Re

al T

rack

Le

ng

th [

hca

l la

yers

]

10.5

11

11.5

12

12.5

data_rec_v0408-!

LHEP-!

QGS_BIC-!

QGSP_BERT_TRV-!

QGSP_BERT-!

FTF_BIC-!

FTFP_BERT-!

CALICE preliminaryµ component in beam

20 GeV


Track Distributions: Angles & Multiplicities

• Large discrepancy between different models

• Best agreement with QGSP_BERT

• LHEP, QGS_BIC have too small angles and too small multiplicity: Insufficient

production of high-energy secondaries at large angles

13

]°Track Angle [0 10 20 30 40 50 60

MC

/Da

ta

0.5

0.6

0.7

0.8

0.9

1

1.1

1.20 10 20 30 40 50 60

En

trie

s (n

orm

aliz

ed

to

In

teg

ral =

1)

0

0.05

0.1

0.15

0.2

0.25

0.3

data_rec_v0408-!

LHEP-!

QGS_BIC-!

QGSP_BERT_TRV-!

QGSP_BERT-!

FTF_BIC-!

FTFP_BERT-!

CALICE preliminary

Track Multiplicity / event0 1 2 3 4 5 6 7 8

MC

/Da

ta

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6 0 1 2 3 4 5 6 7 8

En

trie

s (n

orm

aliz

ed

to

In

teg

ral =

1)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4 data_rec_v0408-!

LHEP-!

QGS_BIC-!

QGSP_BERT_TRV-!

QGSP_BERT-!

FTF_BIC-!

FTFP_BERT-!

CALICE preliminary

20 GeV


MC


Summary on validation:

• The particle flow detectors perform as expected– support predictions for full-scale detector

• Geant 4 simulations not perfect, but also not as far off as feared a few years ago– fruitful close cooperation with model builders ongoing

• Predicted shower sub-structure is seen– detailed checks possible, benefits for all calorimeters

13


Test the algorithms with real data

14


MC


Software compensation

• Poor man’s dream• Significantly improved resolution AND linearity• High granularity - many possibilities

15

beam Energy [GeV]

0 10 20 30 40 50 60 70 80 90

E/E

!

0

0.05

0.1

0.15

0.2

0.25

c GeV/E " b " EFit: a/

0.353 [GeV]±0.73% c = 0.172±0.4% b = 0.00±a = 64.3

0.055 [GeV]±0.39% c = 0.966±1.0% b = 1.97±a = 45.3

test beam data:

constant cluster weight

neural network

Energy resolution - FTF_BIC trainingCALICE Preliminary


Software Compensation: Global Method

• Cluster finding in HCAL and TCMT to determine properties of the shower:

total energy, volume, longitudinal structure...

! Used as input for a neural net, training of the NN with simulations (quasi-

continuous energy)

! No prior knowledge of the beam energy needed for application of method

16

HCAL+TCMTNN trained with FTF_BIC

Resolution improved by ~25%(~15% at 10 GeV, ~20% at 15 GeV)

Resolution given by Gaussian sigma / mean of a fit to the distribution within 1.5" of peak

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Energy Reconstruction & Software Compensation

• The CALICE HCAL is non-compensating: e/! ~ 1.3 (energy dependent)

• High granularity provides detailed information for software compensation:

• Electromagnetic energy deposits tend to be denser than hadronic ones

" Improvement studied on the cell (local) and on the cluster (global) level

14

Local method: apply weight to cells according to their energy, lower weight for cells with

higher energy content, weights are determined with a minimization technique

weighting

10 20 30 40 50 60 70 80 90

reco

nst

ruct

ed

En

erg

y [G

eV

]

10

20

30

40

50

60

70

80

90

CALICE Preliminary

test beam data

constant cluster weight

neural network

Linearity of detector response - FTF_BIC training

beam Energy [GeV]10 20 30 40 50 60 70 80 90

beam

)/E

beam

- E

rec

(E -0.1

0

0.1


Software Compensation: Linearity of Response

• Unweighted reconstruction

shows typical non-linear

behavior: Increased response at

high energies

• Software compensation

recovers linearity within < 2%

from 10 to 80 GeV

17


MC


Two-particle separation

• The “double-track resolution” of an imaging calorimeter • Small occupancy: use of event mixing technique possible• Important: agreement data - simulation

– sharing the same limitations

16

!"

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!"#$!%&&'()*+,+-.(/ !"#$!%&&'()*+,+-.(/

"

to be done with photons, too


MC


Leakage estimation

• Infer leakage from seen part of shower topology and energy• multivariate techniqes; striking potential• implications for detector optimization: implement in Pandora

17

!

!"#$%&'()*+&,-

!!"#!$%&!!'#!$%&!!(#!$%&!!)#!$%&!!*#!$%&

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Simulation, to be confirmed

ongoing thesis work I.Marchesini


MC


Summary on algorithms

• Granularity is extremely powerful

• Energy resolution and imaging capabilities verified with data at sub-structure level– the main drivers of PFLOW

performance

• Leakage estimation and software compensation not yet implemented in present Pandora

18

ARTICLE IN PRESS



rms90E

!21!!!E

p " 0:7" 0:004E" 2:1E

100

" #0:3

%



!!s





!!!!!!!!!!!!!!!E#GeV$








Total (%) 3.7 2.9 3.0 3.1Resolution (%) 3.0 2.0 1.6 1.3Tracking (%) 1.2 0.7 0.8 0.8Leakage (%) 0.1 0.5 0.8 1.0Other (%) 0.6 0.5 0.9 1.0Confusion (%) 1.7 1.8 2.1 2.3

(i) Confusion (photons) (%) 0.8 1.0 1.1 1.3(ii) Confusion (neutral hadrons) (%) 0.9 1.3 1.7 1.8(iii) Confusion (charged hadrons) (%) 1.2 0.7 0.5 0.2


EJET/GeV

rms 9

0/Eje

t [%

]

0

1

2

3


OtherLeakage

0 50 100 150 200 250


Ejet/GeVrm

s 90/E

jet [

%]

0

2

4

6

8


E(GeV) ! 3.0 %50 % /

0 100 200 300 400 500


!!!!!!!!!!!!!!!E#GeV$





Test the technologies and establish feasibility

19


MC


Calibration

• Study triggered by review of LC detector LOI• Can you calibrate millions of channels and maintain stability?

– not really a worry for Si, but could be an issue for scintillator

• 1. Simulate impact of statistic (uncorrelated) and systematic (correlated) calibration errors, find ∫L for in-situ calibration– PFLOW performance VERY robust w.r.t. channel-to-channel variations;

coherent effects easy to control

20

• 2. Exercise in-situ methods (SiPM auto-calib, track segments) with test beam data from CERN and FNAL - transport calibration

across the ocean and restore performance


MC


Integration

• Sensor technology, precision mechanics

• Next: system engineering

• Industrialized ASIC development using common building blocks

• New operational challenges– power pulsing– on-detector zero suppression– real-time threshold monitoring– time measurement

21

spin-off

SDHCAL Scint E/HCAL

Si ECAL


MC


Digital calorimetry

• MAPS DECAL: Tera-pixel– 1st sensor tests in e

showers

• Digital and semi-digital hadron calorimeter– even higher granularity– suppress dE/dx fluct.– reduced n sensitivity– limited at high E?

• Small RPC proto successful• Full-size RPC based

prototypes underway

• Promising tests of GEM and MicroMEGAS based read-out modules

22

Frank Simon ([email protected])CALICE

68. DESY PRC

Pushing the Limits of Granularity: A Digital ECAL

• Extreme resolution needed to resolve every single particle within an

electromagnetic shower: Densities of up to 100 particles / mm2 expected

! Readout granularity of 50 x 50 µm2 required

28

go from here...

to there...


68. DESY PRC

DECAL Technology

• A challenging idea: A complete ILC detector would be a Tera-pixel Calorimeter!

• The technology: MAPS - A standard CMOS product

• Potentially significant price advantage over high resistivity Si diodes

• Tests of sensor prototypes at CERN in 2009: 8.4 x 8.4 mm2 sensitive area

29

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!"#$%&'(#)%"

! !"#$%&'()*+*+,(-

" *)")+,-./01+

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! C)3DD-)'E';'2436+3@@5+*)2

" 2+*3+4

! =11'%2342

! !5>-'+)3DD-)36D

'2,2+->

17

Square meter plane with Readout boards


68. DESY PRC

A Digital HCAL Physics Prototype

17

• The concept: Active layers of glass RPCs

with 1 cm2 pads, one bit readout per channel

• Proof of principle measurement at Fermilab:

• small prototype: 20 x 20 cm2 active area,

8 layers (6 read out)

• 1.2 X0 Steel/Cu absorber per layer

positron shower in the prototype


MC


High energy

• Particle flow also a promising option for CLIC energies

• Leakage expected to limit PFLOW performance– need 1 λ ECAL + 7 λ HCAL

• Tungsten absorber cost-competitive with larger coil - and less risky

• Test beam validation with scintillator and gas detetctors

• More neutrons:– different model systematics– timing measurements

23

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MC


Summary on technologies

• a leap in several orders of magnitude in channel count

• new sensor technologies, new integration concepts– the latter is part of the feasibility demonstration

• progress towards realism:– realistic designs– realistic simulations – realistic cost– realistic proposal

• Digital calorimetry ready for exploration

24


MC


Conclusion

• Particle flow calorimetry does not solve the inherent problems of hadron calorimeters

• But it holds the promise of providing a highly performant work-around

• Substantiated by test beam data• Can be built

25


particle flow calorimetry: experimental status and ...indico.ihep.ac.cn/event/910/session/17/... ·...

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