a luminosity detector for the future linear collider ronen ingbir prague workshop hep tel aviv...

A Luminosity Detector for the Future Linear Collider

A LuminosityDetector for the Future Linear

ColliderRonen Ingbir

Prague Workshop

HEP Tel Aviv University

Approach : Going back to ‘pure electron’

simulation

A. Analyses of detector + background MCB. Cross checks in Geant 4C. Energy and Angular resolution improvement. Tel Aviv University High Energy Physics Experimental Group

Next Steps..…

BhabhaBeamstrahlungBeam spread

R&D progress report

HEP Tel Aviv UniversityA Luminosity Detector for the Future Linear Collider

Detector Design

0.34 cm Tungsten

0.31 cm Silicon

Cell Size1.3cm*2cm>1.3cm*6cm<

~1 Radiation length

~1 Radius Moliere


15 cylinders * 24 sectors * 30 rings = 10800 cells

RL

8 cm

28 cm

6.10 m


Reconstruction Algorithm

)]}ln([,0max{T

ii E

EconstW

i

ii

E

EXX

i

ii

W

WXX

Events Num.

)(radgenrec

)(radgenrec

)(radgen

We explored two reconstruction algorithms:

The log. weight fun. was designed to reduce steps in a granulated detector :

1. Selection of significant cells.

2. Log. smoothing.

Log. weight.

E weight.


Logarithmic Constant

Constant value

Constant value

)]}ln([,0max{T

ii E

EconstW

After selecting:

We explored a more systematic approach.

The first step is finding the best constant to use under two criteria:

1. Best resolution.

2. Minimum bias.

400 GeV

))(_( radmean genrec

))(( rad


Energy dependent constant

)]}ln()([,0max{T

ibeami E

EEconstW

The goal is to find a global weight function.

Is the the log. weight constant really a constant ?

Constant value


Shower reconstruction

Nu

m. o

f C

ells

Nu

m. o

f Se

ctor

s

Nu

m. o

f C

ylin

de r

s

En

ergy

por

tion

(%

)

En>90%

)(rad )(rad

)(rad)(rad

What happens when we select the best log. weight constant ?

Shower size

Log. Weight selection

Most of the information is in the selected cells.


Detector optimization

))(( rad

Num. rings

24 sectors 48 sectors

0.63 0.46

0.00013 0.00013))(( rad

)(deg)(

Comparing new results :

Improvement without changing the detectors granularity.


Detector optimization


Beam Energy (GeV)

))(( rad

Angular resolution

Results using ‘pure electron’ simulation

Can we maintain same detector properties using a more ‘real’ MC ?


Azimuthal resolution

)]}ln([,0max{T

ii E

EconstW

Events Num.E weight.

Log. weight.

(deg)genrec


Log. constant

Constant value

Constant value

)(deg)(

)(deg)_( genrecmean

400 GeVBest constant value for is bigger then the one.

Meaning: in this case best results are obtained by using ‘all the detector information’.

Fixed non zero bias

under investigation.


Log. weight Const function

100%

33%)]}ln()([,0max{

T

ibeami E

EEconstW

)(deg)(

)(deg)(

)(rad

)(rad

E weight.

Fixed constant

dependent const

Different cell size requires different weight function.

Low angle small cell size

smaller constant value

(like in rec)


Nu

m. o

f C

ells

Nu

m. o

f Se

ctor

s

Nu

m. o

f C

ylin

de r

s

En

ergy

por

tion

(%

)

En>98%

Shower reconstruction

)(rad )(rad

)(rad)(rad

What happens when we select the best log. weight constant ?

Shower size

Log. Weight selection

Cell size determines selection mechanism


Azimuthal resolution

)(deg)(

Beam Energy (GeV)



Energy resolution

Beam Energy (GeV)

EE /EE 29.0

Energy Resolution )( GeV

)(rad

Acceptance cut



Bhabha scatteringBHWIDE MC Simulation

Barbie -Geant 3.21 which includes detector description for Tesla detector

The MC of physical process is the output of bhabha scattering

The Barbie program was given to us by Leszek Suszycki Electron energy (GeV)

)(rad

Bhabha

Pure electrons

Pure electrons

Bhabha


250GeV

26 mrad

Events selection - old approach

Gaussian fit and energy

calibration based tail cut

Acceptance cut was based

on the leakage

rec

Detector signal


33 mrad

EE %40

New Acceptance

More systematic analyses results with new acceptance cut and improved resolution.

Energy Resolution )( GeV

)(rad


Energy balance

RL

Simulation distribution

Distribution after acceptance and energy balance event selection

Right side detector signal

Lef

t si

de

det

ecto

r si

gnal

Right signal - Left signal

New approach in this study :

Selecting events using information from both sides of the detector.


Angular-azimuthal symmetry

New approach :

Selecting events which are back to back.

The band cut must be wider than the relevant resolution.

1

2

(deg)21

)(21 rad


Events selection

Acceptance cut

Energy balance cut

)(rad

)(rad

)(rad

Det

ecto

r si

gnal

Det

ecto

r si

gnal

Det

ecto

r si

gnal


Energy resolution - Bhabha

EE %24


Angular resolution - Bhabha


Background studieswe included in the

BHWIDE a background

simulation routine called CIRCE. This

was suggested to us by K. Moenig

Beamspread

Boogert & Miller note (0.05%)

(hep-ex/0211021)

Moenig talk in Zeuthen (0.1%)

Barbie

Detector simulation


Background studies


Selecting events in background MC

)(33)(80 mradmrad

)(2.621 GeVEE )(7.3 GeVE

)(01.021 rad )(001.0 rad

(deg)518021 (deg)5.0~

Cut Value Resolution Relative to Resolution

Acceptance

Energy balance ~85%

Angular symmetry

~14%

Azimuthal symmetry

<20%


Energy resolution

Ntuple

No

cuts

With

cuts

Pure electrons

31% 29%

Bhabha

42% 24%

Bhabha + Beamstrahlung

45% 24%

Bhabha + Beamstrahlung + Beam spread (0.05%)

46% 25%

Bhabha + Beamstrahlung +Beam spread (0.5%)

49% 29%


Angular resolution


intLR Event rate:

yx

nnfL

421Luminosity:

*2

L

LR&D approach:

410 L

LFuture linear collider precision goal:

x

y

1n 2n

Measurement approach : taking a known process – Bhabha scattering.

Bhabha

RL

e e

Luminosity

R&D status : 310

Working with both sides of the detector and looking at the difference between the reconstructed properties:

(In real life we don’t have generated properties)


Conceptual experiment &real life algorithm

In real life we have a MC to help us understand our measurements.

We want to improve by a factor of 10, but maybe a 10% disagreement between data and MC is exactly the 10th factor we need.

The question is how well does our MC will describe the future data ?

2new


)(33.5)(2)(2)(30.8 '' GeVEEGev MCDATA

)(183.0)(2)(2)(165.0 '' mradmrad MCDATA

How far are we?

How well will our MC

describe the future DATA ?

11%

35%

1.’DATA’ = Reality MC

2. Real life algorithm )(21 GeVEE

)(21 rad


Summary and future goals

Using a more systematic approach results with improved reconstruction algorithms and improved resolutions.

Our next step will be to explore further the ‘real life’ approach.

Final detector design recommendation.


http://tesla.desy.de/new_pages/TDR_CD/PartII/accel.html

a luminosity detector for the future linear collider ronen ingbir prague workshop hep tel aviv...

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