20/12/2011christina anna dritsa1 the model: input charge generation the charge of the cluster is...

20/12/201120/12/2011 Christina Anna DritsaChristina Anna Dritsa 11

The model: InputThe model: InputCharge generationCharge generation

The charge of the cluster is taken by random sampling of the experimental distribution for 25 pixels

0 degrees incident angle

Charge sharing

The charge distribution among the pixels in the cluster is based on a 2D Lorentz distribution (derived from the 1D)

0 degrees incident angle

Landau:Accumulated charge on 25 pixels

The simulation of inclined particles is derived by this initial parameterization.

1: Charge generation

2: Charge sharing among pixels


The model: charge distributionThe model: charge distribution

Charge provided by Landau (25 pixels)Charge provided by Landau (25 pixels)– if needed: scale to the trajectory lengthif needed: scale to the trajectory length

The trajectory is divided in segmentsThe trajectory is divided in segments A Lorentz function corresponds to each segmentA Lorentz function corresponds to each segment

Illustration for inclined track


The modelThe model

L(xk,i,yk,i)

Charge on pixel i

Sum over segments (k) x,y-coordinates

of pixel i Pixel pitch

LorentzAmplitude

Lorentzwidth

x,y-coordinatesof segment k


EvaluationEvaluationAccumulated charge plot


EvaluationEvaluationAverage pixel multiplicityAverage pixel multiplicity

Good agreement between simulation and experimental data


Evaluation (qualitative): shapeEvaluation (qualitative): shapeSimulation Experimental data

Asymmetry is not sensor feature.Reflections in readout cable=> Not simulated.

Intermediate summary CBM aims to explore the QCD phase diagram

with rare probes Requirement for high intensities and performing

vertex detector (MAPS sensors) Collision pile up and delta electrons increase

substantially the hit density Precise simulation of detector response is crucial Detector response model developed and

successfully tested with experimental data

Perform D0 → K- π+ measurement feasibility study

Open questions Evaluate open charm performance accounting

for realistic sensor response and delta electrons.

Is pile up tolerable?

What is the impact of particle identification in open charm performance?

Approach: simulation– test different assumptions on pile up– test different assumptions on particle identification

Monte Carlo Transport Code(Geant3/Geant4)

Detector response models

Event Generation

Thermal model

UrQMD

beam particles

Simulation chain

Generate D0 , T=300 MeV, σY=1

nuclear coll. Au+Au , 25 AGeV

Delta electron generation due to passage through target

Simulate interaction of particlesthrough matter, MF etc

Simulate realistic detector response

Simulation chain (reconstruction level)

Hit Finding

Track Finding(Cellular Automaton)

Track fitting (Kalman filter)

Primary and secondary vertex fitting

Analysis code

Reconstruction of particle impact point

Association of hits belonging to the same particle trajectory

Fit the particle track and provide charge sign and momentum

Provides coordinates and uncertainties of vertex position

Reject the maximum of backgroundwhile keeping the maximum of signal

Definition of pile up What is a pile up of N collisions?

– Several collisions occur within a readout cycle of the detector

In simulation: Accumulate hits from N collisions– 1 central Au+Au at 25 AGeV– N-1 minimum bias– 100xN beam ions (delta electrons)

Pile up occurs only in the MVD layers

Generation of high statistics

Generation of high statistics 1 D0→π+K- per 106 collisions: requires highly efficient background

rejection (better than 10-9)

Need high statistics to test background rejection

Use event-mixing like technique:– During the last step (analysis):– Combine opposite charge particles originating from different

nuclear collisions.– Gain CPU time ( no need for GEANT simulation, track

reconstruction)

Increase statistics by factor N (= number of collisions simulated)

Statistics reached: ~108 collisions (from ~104)

time consuming

Background rejectionBackground rejection

High statistics background is generated.

How to efficiently eliminate it?


Cut optimisationCut optimisation Criterion to define optimum cut value:

– Maximise significance S/sqrt(S+B)

Method: Use multidimensional analysis in which the significance is maximised using simultaneously all cuts+ Fast and user friendly- may converge to local maxima => careful usage


The CBM – MVD The CBM – MVD

MVDSTS

RICHTRD

TOFECAL

PSD

Detector integration: IKF, Frankfurt

Station Z (cm) Rinner [mm] Router [mm]

Mat. Budget

1 5 5.5 25 0.3% X0

2 10 5.5 50 0.5% X0

Results: cluster mergingMerged cluster (MC)

MVD station

Unambiguous clusters (UC)

What is the fraction of unambiguous clusters as a function of the hit density (collision pile up) ?

f=UC/all


Invariant mass distributionsInvariant mass distributions

open cuts open cuts

Background Signal

final cuts final cuts


S and B calculationS and B calculation

max

min

)(~ m

m

mGaussS max

min

)(~ m

m

mExpoB

S

signalNorm bgNorm

B


ResultsResults S/B efficiency


SummarySummary Feasibility of open charm Feasibility of open charm

measurements was investigated measurements was investigated based on newly developed detector based on newly developed detector response model for MAPSresponse model for MAPS

Delta electrons were accounted forDelta electrons were accounted for Different assumptions on event pile Different assumptions on event pile

up and PID capabilities were madeup and PID capabilities were made

ConclusionConclusion The digitiser reproduces the response of

MAPS sensors within 10% in terms of… For an event pile up above 5 substantial

cluster merging is observed Additional counting statistics for a moderate

pile up is cancelled out by reduced sensitivity.

CBM remains sensitive to open charm with S/B between on ~0.1 and ~3 depending on pile up and particle identification assumptions.

Expect better results for collisions at 35 AGeV

Outlook Improve cluster finding algorithms

Adding a 3rd MVD station might improve sensitivity of CBM (master thesis C.Trageser, IKF)

Digitiser describes also partially depleted and irradiated MAPS (master thesis M.Domachowski, IKF)

Expect improvement on sensor development: new CMOS processes allow approaching ~µm


Additional slidesAdditional slides

Simulation setup CBMROOT simulation framework

– root based framework

Detectors:– MVD for vertex reconstruction– STS for track and momentum reconstruction – Thickness of silicon detectors : 300 µm– TOF is modelled with ideal proton identification.

Tracking-vertexing– Cellular automaton and Kalman filter

Realistic detector response (MVD , STS)


Strategy of D0 reconstructionStrategy of D0 reconstruction Reconstruct the invariant mass by

combination of all opposite charge pairs

Apply selection cuts– Define cuts– Optimise

Evaluate performance (S/B, significance…)– Estimate Signal, Background– Use scaling factors where needed


MVD detector geometryMVD detector geometry Thickness of sensors – GeometryThickness of sensors – Geometry

– 11stst MAPS at 5 cm is 300 MAPS at 5 cm is 300 µµm thickm thick– 22ndnd MAPS at 10 cm is 500 MAPS at 10 cm is 500 µµm thickm thick

Station Z (cm) Rinner [mm] Router [mm]

Mat. Budget

1 5 5.5 25 0.3% X0

2 10 5.5 50 0.5% X0


Assumptions on collision rateAssumptions on collision rate CBM year: CBM year: 55··10106 6 ss ≈ 2 months ≈ 2 months Assumed sensor time resolution: Assumed sensor time resolution: ttintint = 30 = 30 µµss

Collision rateCollision rate(interactions/s)(interactions/s)

Collisions/yearCollisions/year(mbias)(mbias)

No pile upNo pile up 3 3 ··10 10 44 1.51.5··10101111

Pile up NPile up N N x 3 N x 3 ··10 10 4 4 N x 1.5N x 1.5··10101111

Pile up 5Pile up 5 1.5 1.5 ··10 10 55 7.57.5··10101111

Event generators Nuclear collisions: UrQMD used for the final state phase

space distributions of hadrons for Au+Au collisions at 25 AGeV.

Delta electrons are generated with GEANT by the passage of beam particles through the target.

D0 signal generated with thermal model and embedded in Au+Au collisions. Production multiplicity taken from:– HSD : 2 x 10-4 – SHM : 3.7 x 10-5

Due to low production multiplicities, event mixing-like technique is used to generate high statistics for background.

Data Processing and Data Levels

Event generatorUrQMD, HSD, user defined, ...

Transport (VMC)GEANT3, GEANT4, FLUKA, ...

Detector Response

Reconstruction

Analysis

CB

MR

OO

T

Sim

ulat

ion

(MC

)

GEN

MC

RAW

ESD

Data Level

Experiment DAQ

CBM Software Workshop, Ebernburg, 8 November 2011 30Volker Friese


Background rejectionBackground rejection

π+

K-

D0

PVSV

Cuts: e.g. impact parameter, vertex position, quality of vertex…

Cut effect

Explain pile up

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Explain pile up

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Detector geometry MVD for vertex reconstruction STS for track and momentum

reconstruction PID assumptions…

20/12/2011christina anna dritsa1 the model: input charge generation the charge of the cluster is...

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