1. 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

2. 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

3. 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   of 10 GeV E resolution is 16 % while for P T is 1.3%. The well measured particle momentum substitutes random fluctuation of energy in the calorimeter  improvement in resolution in jet and E T miss 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:  p T /p T  0.036%p T  1.3% Cal:  E/E  50%/  E  Advantages/Disadvantages

4. 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 resolution HAD Cal resolution Si Detect resolution (  and electrons) (hadrons :   and k  ) (track of e ,   and   ) Effects as overlap of particles inside 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 0.0005(1+  10/7000)Pt  0.012 0.245/  Pt  0.007 at  <1.4 0.306((2.4-  )+0.228) /  Pt  0.007 at  >1.4 0.5/  Pt  0.03 at  <3.2 1.0/  Pt  0.07 at  >3.2

5.  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) 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 with Atlfast 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)  Jet Reconstruction from particles

6. 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 had and 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 Charged hadNeutral hadPhotons per jet(%)per jet(%)per jet(%) 40-8022.661.24.612.59.225.2 80-16040.361.37.811.816.925.6 160-32069.161.413.111.928.925.7 Charged hadNeutral hadPhotons per jet(%)per jet(%)per jet(%) 24.1561.14.8812.49.225.2 42.6261.38.1911.811.725.7 73.5061.413.9811.730.725.7 Total in jet Charged had Neutral hadPhotons per jet(%)per jet(%)per jet(%) 40-80 13.26.246.60.97.16.045.5 80-160 17.28.247.11.16.47.945.7 160-320 20.910.047.31.36.19.645.7 Total in jet Charged had Neutral hadPhotons per jet(%)per jet(%)per jet(%) 13.46.446.60.97.06.045.5 17.78.447.11.16.38.245.7 21.710.347.31.36.19.945.7 R=0.4 R=0.7 R=0.4

7. 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 BUT  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

8.  Number of Cells Most of Mixed Cells are in DR<0.1  overlap dominate the central cell Et jet (GeV) Charged CellsNeutral CellsMixed Cells per jet(%)per jet(%)per jet(%) 40-8035.5016.345.86.718.912.635.3 80-16065.9421.833.88.713.435.354.6 160-32094.2023.725.29.610.260.764.4  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  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

9. Applying Energy Flow : 0.5/  Pt  0.03 at  <3.2 resolution of charged hadrons ~13% resolution of charged hadrons ~1 % 0.0005(1+  10/7000 )Pt  0.012 at  <2.5  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  ) :

10. Improvement in ET of the jet (Range 40-80GeV and DR=0.4) Aplying HAD Cal smearing: 0.5/  Pt  0.03 at  <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%

11. 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 RMS HAD RMS INNER (%) 40-80 0.0810.045 44.0 80-160 0.0620.042 31.0 160-320 0.0510.039 23.6 320-640 0.0410.034 16.9 640-1280 0.0320.029 9.6 R=0.4 RMS HAD RMS INNER (%) 40-80 0.0760.04935.7 80-160 0.0620.04330.7 160-320 0.0490.03920.4 320-640 0.0390.03316.6 640-1280 0.0310.0299.5 R=0.7