Pandora PFA at High Energy

1. Pandora PFA at High Energy John Marshall, University of Cambridge LCD-WG2,July 20 2010

2. Overview • Throughout development, PandoraPFA was tuned only to obtain optimal jet energy resolution. • The structure of the reconstructed events was not investigated in any detail, • With the exception of photons, particle identification was more or less neglected. • The new version of Pandora offers ability to include more sophisticated particle id. Currently available are simple/fast photon, electron and muon identification functions. • Recently began to examine performance of particle id and single particle reconstruction at high energy. • Aim to offer accurate particle id and single particle reconstruction, without harming jet energy resolution. • With help from Jean-Jacques and Erik, identified following issues, which will discuss today: • Improvements to muon clustering and association of muon clusters to ECal/HCal clusters, • Problems with muon id efficiency in the forward region, • Problems with pion id efficiency, • Incorrect merging of neutral hadron or photon ‘fragments’ with muon clusters, • Problems with reclustering of electromagnetic showers.

3. Muon Clustering • Muon clustering in Pandora consists of two distinct stages, each performed by separate algorithms: • Application of standard clustering algorithm to muon hits, using generous cone angle, etc, • Association of muon clusters with the ‘primary’ clusters in the ECal and HCal. • It was reported by Mark in previous meeting that muon clustering parameters were perhaps too generous, leading to incorrect merging of separate muons. Have now tightened these parameters. • Noted that muon clusters were only associated to primary clusters at last possible stage of reconstruction. Therefore experimented with making the associations earlier in the reconstruction to make the muon information available to subsequent algorithms. • Found that a noticeable improvement in jet energy resolution is obtained if muon clusters are merged at the earliest possible opportunity for current algorithms: • Clearly want to include this change. However, associating early means that muon association algorithm has to work with less information. It must often match to only fragments of clusters, rather than the final products. • Improvements can be made to association algorithm.Long term goal: associate muons instantly and propagate through reclustering algorithms.

4. Muon Clustering • Investigation of the muon clustering also highlighted a problem, which has yet to be fully investigated. • Whilst sections of muon clusters in the barrel are clustered perfectly, those in the EndCap tend to be fragmented, containing only a few hits at most. • The muon association algorithm then often overlooks these clusters, as there is a cut on the minimum number of hits in the cluster. • Muon EndCap hits can therefore be lost from the final reconstructed event. • The clustering algorithm is the same as that used to form the primary clusters and has been thoroughly validated. Instead, the problem is most likely due to incorrect layer/position information for the EndCap muon hits. • This issue will be investigated soon. Once fixed, should lead to small performance improvement. Each EndCap hit is separate cluster Small grouping of hits in EndCap cluster Muon EndCap

5. Muon Id Efficiency • Erik recently reported low muon id efficiency in the forward region of the detector. Examination of these events identified the problem as track quality cuts, in the MarlinPandora application, assuming TPC-only tracks. • In a Pandora client application (e.g. MarlinPandora), each track must be flagged to specify whether it reaches the surface of the ECal and whether it is of sufficient quality to be associated to clusters and used to form PFOs. • In MarlinPandora, a track must be flagged as reaching the ECal surface if it is to be used to form PFOs. • However, to be declared as reaching the ECal, the track requires a (configurable) minimum number of hits with radius greater than the TPC inner radius. • The solution is to check whether the track contains a number of hits in the FTDs, or any hits in the ETD or SET. This fixes the majority of events (88%) without harming the Pandora jet energy resolutions.

6. Muon Id Efficiency • The remaining events from Erik’s sample, for which a muon is still not reconstructed (with default parameters) are distributed as follows: • 11% of events fail the MarlinPandora cut on the number of track hits. A minimum of 5 hits (spread between any of the various tracking detectors) are required. This is user-configurable, but attempting to use tracks with so few hits in the Pandora reconstruction may be a cause for concern. • In 1% of events, the tracks are correctly flagged to allow use in PFO construction, but the muon clusters pass through an uninstrumented region and so do not appear until approximately layer 20 of the ECal. These muons can only be recovered by increasing the ‘maximum start layer’ parameter in the track-cluster association algorithm. This may not be appropriate for jets. Cluster inner layer

7. Pion Id Efficiency • The reported issue with pion id efficiencies has also been investigated and apparently resolved. • In the examined events, Pandora does successfully reconstruct the charged pion PFO, but may also output some neutral cluster fragment PFOs (in the long term, we may want to make the fragment removal algorithm more aggressive). • The user performing the analysis needs to be aware that (currently), the list of PFOs from Pandora is unsorted. • Within the Pandora library, PFOs are stored in an effectively unordered list (actually a std::set, ordered by object address, for speed and ease of modification). Users cannot assume that a charged PFO will be first in the list. • However, it would be trivial to sort the list in the MarlinPandora application before storage, if there is demand.

8. Fragment Removal • In the previous meeting, Mark highlighted the issue of incorrect merging of neutral hadron or photons with nearby muon clusters. This ‘problem’ was traced to the fragment removal algorithm. • The fragment removal algorithm successfully identifies small neutral clusters and finds nearby charged clusters with which they could be merged. However, do not often want to add neutral clusters to minimally ionising muon track. • Solution is to start using particle id information within the Pandora algorithms. The fast muon id function is used to remove likely muon clusters from the list of clusters considered during fragment removal. This addresses the problems (in the events identified by hand-scanning) without damaging the jet energy resolution. With modification Photon cluster Photon merged with muon track: Pandora reconstructs cluster as pion. Correctly reconstructs and identifies muon and photon.

9. Reclustering EM Showers • Also mentioned in previous meeting was a problem with reclustering high energy electrons. • Reclustering attempts to modify clusters, in order to ensure consistency between cluster hadronic energy and associated track momentum. • However, for electron clusters, we actually want to enforce consistency between the track momentum and the electromagnetic energy . • This tends to split electrons into a small cluster associated with the track and additional neutral fragments, typically identified as photons. • Have implemented code that will return cluster electromagnetic energy as ‘reclustering comparison energy’, if cluster is identified as an electromagnetic shower. Electromagnetic shower identification is a function used by both default fast photon and electron id. • However, modification does appear to harm high energy jet resolution (+1.2%), even if MC information is used to cheat the electromagnetic shower identification. Currently under investigation. Photon Electron Electron With modification Neutron

10. Summary • An initial examination of Pandora high energy particle identification and single particle reconstruction immediately identified a number of problems. Have now started to address these issues. • Some issues have now been addressed and are ready to be checked again: • Muon clustering and association algorithms have been improved, • Muon and pion id efficiency issues are now understood, • Incorrect merging of neutral clusters with muons satisfactorily resolved. • Others are work in progress: • Need to understand muon EndCap clustering problems, • Need to understand why use of electromagnetic energy, when reclustering electrons, degrades jet energy resolution. • Also have ideas for long term improvements: • Associate muon clusters with primary clusters immediately after production and propagate full clusters through reclustering, • Further improvements to fragment removal algorithms.