G. BrunoOffline week - February 20051 Comparison between test- beam data and the SPD simulations in...

G. Bruno Offline week - February 2005 1

Comparison between test-Comparison between test-beam data and the SPD beam data and the SPD simulations in Aliroot simulations in Aliroot

G. Bruno, R. Santoro

Outline:

strategy of the MC simulation

comparison with real data

conclusions


Minibus 2

Minibus 3

Test chip

425 m

50 m

256 rows

32 columns

Beam test in 2003

Test detector: 300 m sensor

Tracking precision: (x) = (y) 10 m

Full scan of threshold and tilt-angle

y

x


Minibus 0

Minibus 1

Test chip

425 m

50 m

256 rows

32 columns

Beam test in 2002

Test detector: 200 m sensor

Tracking precision: (y) 6 m

threshold and tilt-angle scan

y

x


• Kinematics: - , p= 120/350 GeV, gaussian beam profile (x =y=0.2 cm)– Beam focusing tuned to reproduce the real data– 1 track per event ; 50K events for each setup (threshold, tilt-angle, etc)

• Geometry: – Starting point: AliITSvSPD02 (setup for 2002 by J. Conrad e B. Nielsen)– Our developments (actually a minor work):

• setup for 2003• geometry with test-plane tilted (for both 2002 and 2003)

• thin sensor (200m) with thick chips (750m) for 2002 setup

• SPD response-function & simulation: – AliITSresponseSPD, AliITSsimulationSPD

(i.e. the Ba/Sa model without diffusion)

Strategy of the MC simulation

Ba/Sa


• Kinematics• Geometry• SPD response-function & simulation (cont.)

– AliITSresponseSPD, AliITSsimulationSPDdubna (i.e. the Dubna model with the diffusion)

• Clustering, tracking, efficiency and precision studies are done with the same codes used for test-beam real data (see talk by D. Elia)– Immediate comparison– No bias from different algorithm

Strategy of the MC simulation

Dubna


Tracking precison: setup 2003

track(x) track(y) 8 m

For real data:track = 10 m


Tracking precison: setup 2002

track(y) 5 m

For real data:track(y)=6 m


Comparison Ba/Sa MC vs. data

• Setup 2003 – Pc=Pr =0. (no coupling)

Pc=Pr =0.1 (suggested coupling)

Real

MCP=0.1

MCP=0

Coupling has to be introduced !


Comparison Ba/Sa MC vs. data

Ba/Sa MC

Real data

One might playwith Pr and Pc (let’s say Pr=0.2 Pc=0.03 )

Pc=Pr =0.1

... but this would mask the real physics ongoin inthe detector !


Comparison Ba/Sa MC vs. data• Setup 2003 • Pc=Pr =0.1 (in ALICE notes)

Ba/Sa MC Real data

3

2

1


Comparison Ba/Sa MC vs. data• Setup 2003 • Pc=Pr =0.1 (in ALICE notes)

Ba/Sa MC Real data

3

1

Even if cluster type distribution can be reproduced, it will not be related with track impact on the pixels


Comparison dubna MC vs. data

• Setup 2003 – Pc=Pr =0. (no coupling)

– standard conditions for diffusion

– Eth = 3220 elec/holes

Real

MC

Coupling can help with the fine details



dubna MC

Real data



dubna MC

Real data

In log scale


Comparison dubna MC vs. data• Setup 2003

dubna MC Real data

3

2

1


Comparison dubna MC vs. data• Setup 2003

dubna MC Real data

• MC distribution is narrower than real data: not enough diffusion in the model !

3

1



dubna MC Real data

Efficiency versus threshold parameters

Is there a relation between DAC and MC th ?



dubna MC Real data


gaussian fit

no linearity

• Real data: threshold linear over the full range (see talk by Domenico) !

• MC: at very hard threshold linearity is lost !

gaussian fit



dubna MC Real data



dubna MC

• This naive method can give a good estimate !


no MC linearity


Comparison dubna MC vs. datadubna MC Real data

• Precision of the tracking is a bit better in the MC– it is better to compare the intrinsic resolutions


Comparison dubna MC vs. datadubna MC Real data

• 200 m steeper than 300 m both in data and in MC

• MC@200 m: there is a maximum as observed in real data

• MC@300 m: the minimum cannot be reached:

one has to introduce more diffusions in the model !!!!

nominal precision



dubna MC Real data300mThreshold (e-)

With more diffusion in the model the cl2 curve is expected to go up !



dubna MC Real data200m

Threshold (e-)

Again, with more diffusion the cl2 curve should go up (but less than at 300 m)


Definition of cluster types

1 32 4 57

8

VTH = 200


tilted angle 0°300 m Real:

@190 DAC

@3220 e-h

300 m MC


data(DAC 190)

MC (3220 e-)

*

For a given threshold DAC one can already get a good matching by playing only with Eth


tilted angle 0°300 m Real:

@190 DAC

@3220 e-h

300 m MC


0O


300 m Real:

@190 DAC

@3220 e-h

300 m MC

tilted angle 10°



300 m Real:

@190 DAC

@3220 e-h

300 m MC

tilted angle 20°



cpu consumptions in the two models

hits sdigits hits sdigits hits sdigits

Ba/Sa

cp time 31.0 17.4 986.5 466.0 22493 12905

real time 0:00:38 0:00:18 0:17:44 0:08:03 7:03:37 3:46:20

Dubna

cp time 30.8 240.2 987.5 2753 22529 22899

real time 0:00:38 0:04:02 0:17:43 0:46:15 6:55:49 6:37:31

1K events 10K events 50K events

The Ba/Sa code is much faster for small size file (the model is simpler)but both become slow when managing large files


Conclusions

• As it is, the Ba/Sa model is not suited for studies such as charm and beauty production (displacement of the secondary vertices)

• The dubna model reproduces the test beam details much better

• In term of cpu, dubna slower than ba/sa • Test beam data suggest that more diffusion has to be

introduced in the model


What next

• Fine-tuning• Optimization of the algorithm in term of cpu


A reminder of the Ba/Sa modelThe energy deposited in the sensitive material during the transport (at the moment GEANT) is distributed among the pixels according to two mechanisms:

• Charge sharing – Energy in each pixel proportional to the

track path in that pixel

• Capacitive coupling between adiacent pixels– Pc (Pr) is the probability to fire an

adiacent pixel along the column (row)

– If fired, it gets the same energy E of the parent pixel

– Default: Pc=Pr =0.1 (in Aliroot set to 0)

not fired

not fired Fired, E coupl, E

coupl, E


• references: R. Caliandro, R. Dinapoli, R. A. Fini, T. Virgili, Simulation of the response of a silicon pixel

detector, Nucl. Instrum. Meth. A 482 (2002) 619-628R. Caliandro, R. Dinapoli, R.A. Fini and T. Virgili, A model for the simulation of the

response of pixel detectors, ALICE INT-2000-23R. Caliandro, R. Dinapoli, R.A. Fini and T. Virgili, Simulation of the response of the ALICE

silicon pixel detectors, ALICE INT-2001-05R. Barbera, R. Caliandro, B.V. Batyunya, A.G. Fedounov, R. A. Fini, B.S. Nilsen, T. Virgili,

Status of the simulation for the silicon pixel detector in ALICE, ALICE-INT-2001-48

A reminder of the Ba/Sa model• At the initial stage, noise is added to all

the pixels according to a gaussian (default: sigma = 280 elec-hole pairs)

• From Sdigit (analog) to Digit (digital) – A pixel gives a digit if the energy is

larger than a threshold Eth (default: Eth=2000 elec.-hole pairs)

•Actually the model was thought with a parametrization of the diffusion–never implemented in Aliroot


A reminder of the Dubna model

• Charge sharing: diffusion – The electrons/holes produced along the track are let to diffuse (T,V,).– Slower than Ba/Sa (we will quantify later)– A better physical description: essential to match the observed (real data)

improvements in the intrinsic resolution due to cluster 2,3

• Capacitive coupling between adiacent pixels – the same as Ba/Sa– By default switched off: Pc=Pr =0.0

• noise:– “electronics” = “baseline” + “noise” (i.e. const + gaussian)– Default: “electronics”=0.+0.

After work by B.Nielsen and J. Conrad for merging features of the two models ...

3

2

G. BrunoOffline week - February 20051 Comparison between test- beam data and the SPD simulations in...

Documents

Transcript of G. BrunoOffline week - February 20051 Comparison between test- beam data and the SPD simulations in...