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

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

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

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

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

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

Summary of current Jet Algorithm Parameters (to be optimized according to specific analysis needs) Cone search-radius 0.7 Grid Size 0.5 Percentage of particles shared between two jets for merging-splitting: 50 % 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 : 2 Jet Shape request: > 50% of charged particles inside 0.4 cone 7

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

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

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

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

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

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

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

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

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