1. T2 Jet Reconstruction Studies
Results for a possible
Jet Algorithm
SW Working Group Meeting (TOTEM Collaboration Meeting) CERN, 4/12/2007
Mirko Berretti - Giuseppe Latino

2. We want to study the possibility of developing a Jet algorithm by using only charged particles
reconstructed with T2
To do this, we have to exchange the (usual) PT information with the particle density information in the h-f plane
First step: the algorithm has been developed/tested by using Pythia DiJets Events where both partons are required to fall in T2 region (5.3 < h < 6.5). 2

3. include the neighbouring
We have developed a cone algorithm with some improvement, based on MidPoint algorithm
Build eta –phi grid
Fill the cells with the
number of particles inside them
Construction of
ordered list
(in particle number)
of seed avoiding to
include the neighbouring
cells of the major seeds
multiplicity
and shape
cuts
Final
Jet
List
From the seeds
start the
search of stable
cones
(1st Jet list)
2nd
Jet
List
Merging -
Splitting
Mid
Point 3

4. Mid - Point
For every pair of jets with distance < 2R , assign a new seed in the midpoint.
This method could reduce “infrared sensitivity”
of our algorithm due to missing p0s in the core
of particle jet and “collinear sensitivity” due to
the choice on grid size/position.
Infrared sensitivity
With Mid-Point we could merge two different jets when soft radiation is present between them
Collinear sensitivity
Algorithm start from central most populared cell and it will probably find 3 Jet or only 1 Jet (if the Jet are too close in the preclustering).
Without Mid-Point, algorithm starts from the right-most cell and it will find only 2 Jets.With Mid Point it will find 3 jet o 1 jet 4

5. To use a reasonable grid size and search radius we have looked at the distribution of charged particles around the outgoing parton 5

6. We need some jet “quality” cuts
Multiplicity
(minimum number of particles forming a jet) >= 2
Shape
# charged particles inside cone 0.4
Jet charged shape:
# charged particles inside cone 0.7
> 0,5 6

7. Summary of current Jet Algorithm Parameters
(to be optimized according to specific analysis needs)
Cone search-radius
Grid Size
Percentage of particles shared between
two jets for merging-splitting: %
of the less populated jet
If the number of particles shared are less than half multiplicity of the less populated jets, the two jets are split otherwise are merged.
Jet Charged Multiplicity cut :
Jet Shape request: > 50% of charged particles inside 0.4 cone 7

8. Cuts Effect Event Jet Number No Shape – No Multiplicity cuts
No Jet Shape cut
Jet Shape and Multiplicity cuts 8

9. Cuts Effect DJet Event Reconstruction No Shape – No Multiplicity cuts
No Shape Multiplicity cut
Shape and Multiplicity cuts 9

10. Benefits of Splitting and Merging Procedure
Event Jet Number
No Mid point
No Split-Merging
No Mid Point
Split-Merging
Mid Point and 10

11. Benefits of Splitting and Merging Procedure
Event DJet Reconstruction
No Mid point
No Split-Merging
No Mid Point
Split-Merging
Mid Point and 11

12. Benefits of Split and Merging Procedure
Resolution in the reconstruction of leading Jet center
No Mid Point
No Split and Merging
Only Split and Merging
Mid Point and
Split and Merging 12

13. Other features Find the best cell size Check the algorithm
stability shifting the grid
Angular resolution
(all cuts activated) 13

14. DJets vs Single Diffractive events
Selection of particles in the T2 Region
Single Diffraction
in T2
DJets
Events
in T2 14

15. DJets vs Single Diffractive events
Selection of particles in the T2 Region
Single Diffraction
in T2
DJets
Events
in T2
Cell= 0.4 x 0.4 ; R =0.8 15

16. WORKING PLAN
FURTHER INVESTIGATION OF SOME ALGORITHM FEATURES WITH IMPROVED MC STATISTICS
START TO WORK ON THE SIMULATION OF TRAKS IN T2 DETECTOR
OTHER ALGORITHM OPTIMIZATIONS WILL BE MADE DEPENDING ON THE RESULTS OF THE PARTICLES TRACKING.
COMPARISON OF OUR ALGORITHM WITH A KT- LIKE JET-ALGORITM.
Acknowledgment:
Fabrizio
Jan
Hubert 16