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My work. PAST WORKS: 1) (Madrid) Data Analysis in L3, LEP: - Measurement of the Mass, Width and Cross Section of the W boson production at LEP, 1999   - Study of the e+e-  W+W-qqe  process at LEP, 2000 2) (Barcelona)

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My work

My work

PAST WORKS:

1) (Madrid)

Data Analysis in L3, LEP:

- Measurement of the Mass, Width and Cross Section of the W boson production at LEP, 1999  

- Study of the e+e- W+W-qqe process at LEP, 2000

2) (Barcelona)

Quality Control of Extender Barrel of the Hadronic Calorimeter TiCal of ATLAS:

- RMS left- right side of module, Energy resolution of tile ,RMS vs Temperature…

- Stability of the velocity of the LED diffuser inside Calorimeter module

- Performance of the cut cells of the calorimeter (Tical-Week2002,CERN)

TestBeam Analysis of TiCal Detector in CERN, ATLAS

- Analysis with pion TBdata 2000-01.

- TestBeam MonteCarlo Simulation

Software Analysis Offline

- Energy Flow Algorithm in Atlfast (Fast Simulation in ATLAS) (SW-Week2003,CERN)

PRESENT WORK:

3) (Valencia)

Software Analysis Offline

- Energy Flow in Full Simulation: Study the overlap and gain resolution with single particle

FUTURE WORK:

Validate Energy Flow in Combined TestBeam 2004: Study the overlap in clusters

-Simulation-Reconstruction CTB with single Particle (RecTB)

-Data CTB: low pt pion and low pt electron as aprox of the behavior of photon shower


Energy flow in atlfast potencial gain in energy resolution of the jet

Energy Flow in ATLFAST: Potencial gain in Energy resolution of the Jet

Carmen Iglesias

IFIC-Intituto de Fisica Corpuscular (TileCal Group, Valencia)

(collaborating with IFAE,Barcelona)

RTN WorkShop Barcelona 03


Energy flow concept

Energy Flow Concept

EFLOW: Combine calorimeter, tracking and particle ID information to improve energy

resolution for jet

For low Pt charged particles, tracking error is much smaller than calorimetric energy error.

In the Barrel we can approximate (=0):

We can see, for one p of 10 GeV E resolution is 16 %

while for PT is 1.3%.

The well measured particle momentum substitutes random fluctuation of energy in the

calorimeter improvement in resolution in jet and ETmiss energy

HOWEVER

The use of track to improve the resolution only works if cluster is isolated. If track shares cluster

with neutrals then gain in resolution from track by loss of resolution from remaining cluster.

Efficiency of algorithm is limited by the overlap between neutral and Charged particles in the

cell of the calorimeter

We need to know more about this effect and its influence in the analysis

Track: pT/pT 0.036%pT1.3%

Cal: E/E  50%/E3%

Advantages/Disadvantages


Resolution in atlfast

Resolution in Atlfast

ATHENA: Framework of ‘offline’ Software in ATLAS

ATHENA-Atlfast: C++ Object Oriented implementation which provides a fast particle-level simulation of the detector response and its later reconstruction, and allow:

define the 4-momentum of the particles

reconstruct clusters and jets inside the calorimeters

characterize the tracks

In Atlfast no detailed simulation of particle shower

neither of the trakcs in Si detector

only a parametrisation of calorimeter E resolution

and a simulation of efficiency and Pt resolution in Si detector.

Parametrisations were derived from Full Simulation studies:

EM Cal resolutionHAD Cal resolutionSi Detect resolution

(  and electrons) (hadrons :p and k) (track of e ,  andp)

Effects as overlap of particlesinside the cell can be studied byAtlfast,

HOWEVER when the influence of the shower is relevant  Full Simulation.

Here the goal is to estimate the potencial gain in resolution of the Energy Flow Algorithm and the degree of overlap of particle inside the jets

  • 0.5/Pt 0.03at <3.2

  • 1.0/Pt 0.07at >3.2

  • 0.245/Pt 0.007at <1.4

  • 0.306((2.4- )+0.228) /Pt 0.007at >1.4

0.0005(1+ 10/7000)Pt 0.012


Generation with pythia 6 2

Generation with PYTHIA 6.2

Generate 1000 events of QCD jets, applying in Pythia the next conditions:

generate jets with differents range of PT(GeV):

20-40, 40-80 , 80-160, 160-320, 320-640 and 640-1280

Neither Underlying Events nor Minimum Bias effects are included

ISR and FSR are taken into account

|parton| < 5.0 (calorimeter coverage)

Jet Reconstruction with Atlfast

Release 6.2.0 is used for the reconstruction of QCD jets:

- Cone algoritm is used with different values of radius R=0.4 and 0.7

- |jet| < 2.0 , inside Inner coverage to ensure the completed containment within the cone jet.

- Pt min of the jet  different values depending on R (multiplicity of jets still significant)

Ptmin=20GeV if R=0.7 Ptmin=15GeV if R=0.4

Jet Reconstruction from particles

To reconstruct jet from particle energy into the cone select:

- only stables particles deposited in Calorimeter

mainly charged hadrons ( ± and k ± ) and photons (from 0)

neutral hadrons (kLO & n) and very few leptons (e ± ,± and )

- ET>0.5GeV for charged particles

- |partc| < 2.5, only particles inside INNER (calo+track info used)


Selected particles

Selected Particles

Multiplicity

mainly charged hadrons and photons, leptons are negligible (<0.5%)

Number of particle increase as the E is bigger and slightly bigger at R=0.7

Similar contribution from charged hadand photonsCharacteristc of the fragmentation

Et deposited by particles

ET deposited by particles increase as the ET of jet is bigger

most of ET from charged had (2/3 parts), more than twice that from photons

Et per jet in R=0.7 is bigger than 0.4

R=0.4

R=0.7

R=0.4

R=0.7


Analysis by cells

Analysis by Cells

1) The number of charged hadrons is ~ 47% of the total particles

2) The ET deposited by charged hadrons is ~ 61% of the total energy

We are going to apply the Energy Flow to the charged hadrons, BUT not to all only to the

charged hadrons which fell down in cell without sharing with neutral particles, SO we need:

a)define the calorimeter CELL that the particles hits

Grid of 81 cells with 0.1 x 0.1 granularity in - plane around deposition point of jet

b)classification of the cell based on the type of particle

(charged or neutral) that fell in it

CHARGED CELLS: only charged partic ( ± and k ±)

NEUTRAL CELLS: only photons

MIXED CELLS: mixed charged and neutral particles

in this last case it’s analyzed the overlap between

charged had and photons or neutral had


Number of cells

Number of Cells

Most of Mixed Cells are in DR<0.1

 overlap dominate the central cell

ET deposited in cells

Up to 45% of total ET, in the best case, come from charged had in Charged cells. For this ET a gain in resolution will be done by E-Flow

this proportion decrease quickly as the ET of jets is bigger

ET in Mixed Cell increase with E Overlap will be bigger

 the gain will be worse with E


Applying energy flow

Applying Energy Flow

:

0.5/Pt 0.03at <3.2

resolution of charged hadrons

~13%

resolution of charged hadrons

~1 %

  • Apply Energy Flow only over CHARGED cells to avoid loss of resolution from

    neutral particles. For charged hadrones in these cells:

    sustitute HAD Cal Resolution:

by INNER Detect resolution

(if we include dependance on ) :

0.0005(1+ 10/7000)Pt 0.012 at <2.5


Improvement in et of the jet

Improvement in ET of the jet

(Range 40-80GeV and DR=0.4)

Aplying HAD Cal smearing:

0.5/Pt 0.03at <3.2

resolution of ET del jet

~8%

Aplying INNER smearing

resolution of ET del jet

~4.5%

much better result than with HAD Cal

0.0005(1+ 10/7000)Pt 0.012 at <2.5

Resolution of the Energy of the jet have been improved in ~44%


Variation of gain in resolution

Variation of gain in resolution

CONCLUSIONS

Very optimistic result high gain in resolution using Energy Flow at low Pt. ~40 %

The improvement decrease with E.

 At a few 100 GeV the overlap of particles gets higher and the gain in resolution is marginal

R=0.4

R=0.7


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