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ILC.ElAlgoritmoParticleFlowEnILDr1.2 - 22 Dec 2008 - 12:35 - Main.iglesiastopic end

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4. *Algoritmo Particle Flow *

4.b. El algoritmo Particle Flow en ILD

4.b.i. Pandora PFA * 4. Algoritmo Particle Flow en la zona hacia delante

Pandora PFA is a C++ implementation of a PFA algorithm running in the MARLIN Framework. It was developed and optimised using events generated with the MOKKA program, which provides a GEANT4 simulation of the detector.


Pandora PFA performs both calorimeter clustering and particle flow in a single stage. using tracking information to help ECAL/HCAL clustering

It can be applicable to multiple detector concepts

The algorithm has seven main stages:

1- Tracking: Charged particle tracks are reconstructed in the tracking detectors. The track parameters are then extracted using the helical fit. For optimal performance it is important to identify :

  • neutral vertices, e.g. from KS +- decays and photon conversions: Neutral particle decays resulting in two charged particle tracks (V0s) are identified by
searching for tracks which do not originate from the interaction point and that are consistent with coming from a single point in space.
  • and kinks from bremstrahlung charged particle decays: Kinked tracks from charged particle decays to a single charged particle and a number of neutrals are also identified. When a kink is identified the “parent” track is effectively removed and is not used in the reconstruction of PFOs.

The projections of tracks onto the front face of the ECAL barrel/endcap are calculated using helical fits (without taking into account energy loss along the track) and are stored.

2- ECAL/HCAL Clustering: The clustering algorithm Start at inner layers and work outward. In this manner, hits are added to clusters or are used to seed new clusters. Throughout the clustering algorithm, clusters are assigned a direction (or directions) in which they are growing. Tracks can be used to “seed”clusters. The initial direction of a track-seeded cluster is obtained from the track direction. A Simple cone based algorithm is used

3- Topological Cluster Association:

4- Associate track to clusters

5- Iterative Reclustering¨: This step using different estrategies:

Cluster splitting we reapply entire clustering algorithm to hits in “dubious” cluster. And iteratively we reduce cone angle until cluster splits to give acceptable energy match to track. Therefore, if it will be necessary, plug in alternative clustering algorithm

Cluster merging with splitting We look for clusters to add to a track to get sensible energy association. If necessary iteratively we split up clusters to get good match.

Track association ambigueties In dense environment we may have multiple tracks matched to same cluster. We Apply above techniques to get ok energy match. Nuclear option If none of above works –we kill track and rely on clusters alone

6- Photon identification/Recovery

7- Fragment Removal Clustering “fragments”from charged tracks, looking for “evidence” that a cluster is associated with another, as: *Distance of closest approach *Layers in close contact *Distance to track extrapolated *Fraction of energy in cone

-- Main.iglesias - 22 Dec 2008

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