LNF, 26 gennaio 2004 1

Test Beam 2003

Data Analysis and MonteCarlo Studies

M. Barone

Software and Analysis Meeting

ATLAS/Frascati

LNF, 26 gennaio 2004 M. Barone 2

Outline

H8 Test Beam 2003 setup data analysis Results

MonteCarlo simulation Garfield Results

Work in progress Conclusions

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2003 H8 setup

BIL2BIL2

BIL1BIL1

BOL1BOL1BML1BML1

BOL2BOL2BML2BML2

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H8: data sample 1 month of data taken with half “ATLAS like” gas fluxes Runs collected in the period 19/7-22/8 (35 days – 830 hours) have been analyzed: corresponding to 14 Runs of ~100300 K events

each. Trigger Hodoscope Runs taken under stable operating conditions:

Gas: Ar (93%) , CO2 (7%) at 3 bar absolute Gas flow: 60 bar l/h BIL (~0.9 changes/day) 120 bar l/h BML (~1.2 changes/day) 180 bar l/h BOL (~1.0 changes/day) HV : 3080 V

BML1 has the multilayer 2 with complete parallel gas distribution Software: ATHENA version 6.6.0 used to get the MDT digits PAW ntuples and ROOT trees available on lxcalc (and lxplus)

/scratch/nfs/data/athen/rootdata

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H8: analysis method – max tdrift MDT spectra fitted with a double

Fermi-Dirac function + constant to extract t0 and tmax

Drift time computed for the six barrel chamber (12 multilayers):

tdrift = tmax – t0

Typical statistical errors for runs with larger statistic (~300k evts):

error on t0 < 0.2 ns error on tmax ~ 1 ns error on tdrift ~ 1 ns

t0 = P5

tmax = P6

)0.1()0.1(

)0.1()(

8675

45

/)(/)(

/)(32

1 PPtPtP

PtP

ee

ePPPtf

)0.1()0.1(

)0.1()(

8675

45

/)(/)(

/)(32

1 PPtPtP

PtP

ee

ePPPtf

max tdrift

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Tubes grouped on the basis of their postion in the gas series (the gas flows from tube 1 to tube 3)

Tubes grouped on the basis of their postion in the gas series (the gas flows from tube 1 to tube 3)

H8: analysis method

Temperature correction The drift times have been

corrected to take into account changes in temperature T. The values of T were registered by temperature sensors

tdrift/ T = -2.4 ns/K (ATLAS 2003-001)

Temperature correction The drift times have been

corrected to take into account changes in temperature T. The values of T were registered by temperature sensors

tdrift/ T = -2.4 ns/K (ATLAS 2003-001)

Time spectra from tubes in different layers have been added together -> statistical uncertainty reduced

Time spectra from tubes in different layers have been added together -> statistical uncertainty reduced

Only tubes with SIGNAL/NOISE >

15 have been considered

Only tubes with SIGNAL/NOISE >

15 have been considered

1 2 3

Statistical errors on tdrift much larger for chambers of type 1 with respect to chambers of type 2 because of the different beam illumination

Statistical errors on tdrift much larger for chambers of type 1 with respect to chambers of type 2 because of the different beam illumination

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Exp. Results: Long Term Stability - BIL

BIL1BIL1 BIL2BIL2

The values of the drift times for the 6 barrel chambers have been analyzed as a function of the data-taking time and fitted with a 1th order polynomial

The values of the drift times for the 6 barrel chambers have been analyzed as a function of the data-taking time and fitted with a 1th order polynomial

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Exp. Results: Long Term Stability - BML

BML1BML1 BML2BML2

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Exp. Results: Long Term Stability - BOL

BOL1BOL1 BOL2BOL2

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Exp. Results: Drift Time and Serial Effect

The average tdrift and RMS have been computed for each tube type, multilayer and chamber

The average tdrift and RMS have been computed for each tube type, multilayer and chamber

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Experimental results

Uniform response - in terms of drift time - from chamber to chamber within ±2÷3 ns

Uniform response - in terms of drift time - from chamber to chamber within ±2÷3 ns

Dependence of the drift time on the tube series position clearly visible for all the multilayers, with the exception of multilayer 2 of the BML1 (parallel system): average drift time differences from 2 to 3.2 ns

Dependence of the drift time on the tube series position clearly visible for all the multilayers, with the exception of multilayer 2 of the BML1 (parallel system): average drift time differences from 2 to 3.2 ns

Drift properties of the MDTs stable at the level of 0.04 ns/day on long term base and at the level of 1÷2 ns level on short term time base

Drift properties of the MDTs stable at the level of 0.04 ns/day on long term base and at the level of 1÷2 ns level on short term time base

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Explanation of the Serial Effect

The “serial effect” can be explained with a water contamination due to the NORYL end-plug permeability: water vapor accumulates in the gas mixture during its flow along the series.

The estimated equivalent water flux is: EHP(bar l/day)/EP 0.0002 for all the chambers. The value is in good agreement with an approx. estimate based on NORYL-GFN3 characteristics: WF(bar·l/day)/EP = 0.000227

The impact of the “serial effect” on the single tube space resolution is negligible

The “serial effect” can be explained with a water contamination due to the NORYL end-plug permeability: water vapor accumulates in the gas mixture during its flow along the series.

The estimated equivalent water flux is: EHP(bar l/day)/EP 0.0002 for all the chambers. The value is in good agreement with an approx. estimate based on NORYL-GFN3 characteristics: WF(bar·l/day)/EP = 0.000227

The impact of the “serial effect” on the single tube space resolution is negligible

Chamber type <tdrift> 1,2-2,3 (ns) <ppmH2O> 1,2-2,3 EHF(bar·l/day)/EP

BIL 3.2 49.1 0.000209

BML 2.0 30.2 0.000192

BOL 1.9 29.3 0.000211

1) use the GARFIELD simulation to predict the impact of water vapor contamination on the MDT drift properties: tdrift/ H2O= 6.5ns/100ppm

2) translate the measured water content into an equivalent water flux per end-plug (EHF/EP)

3) estimate the impact of the serial effect on single tube space resolution

1) use the GARFIELD simulation to predict the impact of water vapor contamination on the MDT drift properties: tdrift/ H2O= 6.5ns/100ppm

2) translate the measured water content into an equivalent water flux per end-plug (EHF/EP)

3) estimate the impact of the serial effect on single tube space resolution

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What’s next?

The study on the stability and uniformity of the system is well documented in:M.Antonelli , M.Barone, F.Cerutti, M.Curatolo, B.Esposito, “Long term stability and uniformity studies of MDT chambers in the H8 2003 system test”, ATL-COM-MUON-2003-35, December 2003

The study on the stability and uniformity of the system is well documented in:M.Antonelli , M.Barone, F.Cerutti, M.Curatolo, B.Esposito, “Long term stability and uniformity studies of MDT chambers in the H8 2003 system test”, ATL-COM-MUON-2003-35, December 2003

What about the shape of the spectrum? Are we able to reproduce the whole spectrum?

What about the shape of the spectrum? Are we able to reproduce the whole spectrum?

Width of the TDC spectrum

MC simulation

GARFIELD

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Garfield: parameters

pressure 3 bar

temperature 300 K

Ar 93.0 %

CO2 7.0 %

GAS

high voltage 3080 V

n_electrons 25

gain 20000

muon energy 180 GeV

transfer function (t/)2 e- t/

6 ns

electronic noise ENC = 4200

SIGNAL

Garfield version 7.10 Magboltz: simulation of the electron transport properties in a given gas mixture Heed: simulation of the ionization of gas molecules by particles crossing the detector signal calculation and processing Magboltz: Magboltz:

geometry tube

radius 1.46 cm

CELL

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Garfield: drift velocity

Computed from Magboltz 100 points in the E (or E/p) range

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Garfield: track and signal simulation

14600 tracks uniformely

distribuited in the cell (from r=0 to r=1.46cm)

For each track, the drift time of the first electron crossing the threshold is recorded

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Garfield: time spectrum

Comparison between real data and simulated data (Ar-CO2 93-7% + H2O xppm; 14600 tracks uniformely distributed)

black: Run 1559, BML2, ml2black: Run 1559, BML2, ml2

red: H20 0ppm

green: H20 50ppm

blu: H20 100ppm

purple: H20 200ppm

red: H20 0ppm

green: H20 50ppm

blu: H20 100ppm

purple: H20 200ppm

In the tail :

blue - black 20 ns

- 83 ns +1%Ar

(Braccini, dec 2002)t = 0 is given by the primary muon

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Garfield: different gas mixture

Different gas mixture (Ar-CO2 93.25-6.75% + H2O 100ppm; 14600 tracks uniformely distributed)

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Garfield: work in progress

Magboltz: COLL = “number of collisions in multiplies of 960000, to be used to compute the transport properties. [...] The statistical accuracy of the drift velocity calculation improves with the square root of this parameter”

Default: 10 x 960,000 collisions

20 coll 0.25%

statistical error on vdrift

80 coll 0.15%

statistical error on vdrift

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Garfield: work in progress

-rays shorter drift time expected Inefficiencies expected (for r Rtube)

What happens near the wire or for a track hitting the wire?

…

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Goal

MC description matches

the experimental data

MC description matches

the experimental data

simulated spectra can give hints on chamber’s behavior, allowing to improve performance and to have a better understanding of the detector r(t) relations can be automatically derived

one relation for each tube (instead of one per chamber) no need of any hypothesis “ad hoc” (the effects of -rays, cluster position fluctuations, … , are already taken into account by the simulation)

simulated spectra can give hints on chamber’s behavior, allowing to improve performance and to have a better understanding of the detector r(t) relations can be automatically derived

one relation for each tube (instead of one per chamber) no need of any hypothesis “ad hoc” (the effects of -rays, cluster position fluctuations, … , are already taken into account by the simulation)

appropriate corrections will be extracted from the data by means of proper algorithms

appropriate corrections will be extracted from the data by means of proper algorithms

YN

Tune the MC simulation to obtain simulated spectra well reproducing the experimental data

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Conclusions

We performed a systematic study of the drift behavior of the 6 barrel MDT chambers, using the H8 2003 test beam data.

The response of the chambers appears to be uniform and stable in time. We deeply investigated the “serial effect”, that can be quantitatively

explained in terms of water contamination.

We performed a systematic study of the drift behavior of the 6 barrel MDT chambers, using the H8 2003 test beam data.

The response of the chambers appears to be uniform and stable in time. We deeply investigated the “serial effect”, that can be quantitatively

explained in terms of water contamination.

We are planning to tune the GARFIELD simulation in order to obtain simulated time spectra as similar as possible to real data.

The following steps will depend on the success of the previous item. r(t) relations tracking

We are planning to tune the GARFIELD simulation in order to obtain simulated time spectra as similar as possible to real data.

The following steps will depend on the success of the previous item. r(t) relations tracking