Michela Biglietti (Universita’ di Napoli-Federico II) Gabriella Cataldi (INFN Lecce)

1. and the HLT Michela Biglietti (Universita’ di Napoli-Federico II) Gabriella Cataldi (INFN Lecce)

2. Standard Moore flow Each step is driven by an Athena top-algorithm Transient objects are passed via TDS/StoreGate Independent algorithms, the only coupling is through the transient objects MooAlgs RPC/TGC digits MooMakePhiSegments PhiSegments MooMakeRZSegments MDT digits MooMakeRoads CrudeRZSegments MooRoads MooMakeiPatTracks MooiPatTracks MooStatistics MooMakeNtuples Ntuples

3. Reconstruction Strategy • Searches for  regions of activity • From the RPC/TGC  measurements “- Segments” are created • Searches for R-Z regions of activity • For each “-Segment”, the associated MDTs is found and a “crude” RZ Segments is built (essentially collections of z hits) .

4. Reconstruction Strategy • Pattern recognition and outer Roads • Inside MDTs the drift distance is calculated from the drift time, by applying various corrections on it (TOF, second coordinate, propagation along the wire, Lorenz effect). From the 4 tangential lines the best one is found. • All the “MDT segments” of the O station are combined with those of the M layer. The MDT hits of each combination are added to the phi-hits of the “Phi Segment”, forming “outer” track candidates. All the successfully fitted candidates are kept for further processing. MDT mutilayer • Final tracks • associate inner station MDT hits. A “final” track is defined • The successful “outer” track is subsequently used to • as a successfully fitted collection of trigger hits and of MDT hits from at least two layers.

5. run-time plug in differents algos in order to build the reconstruction chain. Athena algorithms with different features/goals Moore is a container Moore structure MooLVL2Algs MooAlgs • MooAlgs contains the standard sequence of Algorithms: MooMakePhiSegments MooMakeRZSegments MooMakeRoads MooMakeTracks • MooLVL2Algs contain a possible seeding approach MooMakePhiSegmentLVL2 MooMakeRZSegmentLVL2 MooStatistics MooAlgs_n MooEvent Events for reconstruction

6. MooLVL2Algs strategy run atrig trigout10GeV.txt trigout20GeV.txt ….. ….. ….. only on barrel ASCI files Set-up the logic link: TestRelease/… /run/Persint.out … /… /trigoutxxx.txt run athena with the appropriate jobOptions.txt RPC digits MooMakePhiSegmentLVL2 PhiSegment MooMakeRZSegmentLVL2 MDT digits RZSegmentMap How to run Moore and MooLVL2Algs Documentation in: http://people.na.infn.it/~bigliett/moore/MooreDoc/MooreDoc/

7. MooLVL2Algs outputs are: PhiSegments RZSegments In order to perform a fit the standard Algorithms MooAlgs must be plugged in the sequence MooMakePhiSegmentLVL2MooMakeRZSegmentLVL2MooMakeRoadsMooMakeTracks

8. in HLT MooAlgs in HLT assuming that the sequence is always valid, and accessing to all the event, i.e Standard Moore running. No use of TriggerElements. First need: • If we want to have the seeded Moore in the HLT environment we need: • MooAlgs • MooLVL2Algs First step:

9. First step: MooAlgs in HLT MooTopAlg.h • Creation of a new subpackage • In MuonSpectrometer/Moore ( Is this the right place ?) • MooEFAlgs ... private: // Pointers to sub-algoritms MooMakePhiSegments * m_MooMakePhiSegmentsAlgs; MooMakeCrudeRZSegments * m_MooMakeCrudeRZSegmentsAlgs; MooMakeRoads * m_MooMakeRoadsAlgs; MooMakeiPatTracks * m_MooMakeiPatTracksAlgs; // initialize and execute methods for subalgs template<class T> StatusCode initializeAlgs(std::string, T* ); template<class T> void executeAlgs(T*); ... In MooEFAlgs the selection/seeding algorithms will also be included MooTopAlg.cxx bool MooTopAlg::execHLTAlgorithm(TriggerElement* TEout){ executeAlgs(&m_MooMakePhiSegmentsAlgs); executeAlgs(&m_MooMakeCrudeRZSegmentsAlgs); executeAlgs(&m_MooMakeRoadsAlgs); executeAlgs(&m_MooMakeiPatTracksAlgs); //Let's suppose Moore always validates the sequence... log << MSG::INFO << "Trigger Element is active : " << TEout->label() << endreq; TEout->SetActive(true); return true; }

10. How to proceed ? • Our understanding is that NOW we can access the Offline Identifiers of RPC selected by LVL1 Trigger. • Keeping in mind that in future we will use LVL2 output, can we access RoI in terms of Offline Identifiers from the LVL1 ? If yes, how?

11. How to seed MooLVL2Algs. • As it is now • MooLVL2Algs are seeded with the list of HitsOnTrack from LVL2. • MooLVL2Algs work with Old EDM layout M geometry. • In the future (personal opinion) • MooLVL2Algs for HLT should use as input the RoI (eta, phi) and the pt from LVL2 (in order to confirm or reject their estimate), opening a Region of Interest around the track, and performing the PhiSegment, RZSegment search, as for standard Moore, in a limited region. • MooLVL2Algs seeded with list of HitsOnTrack will be useful for comparison of results. RPC hits MDT hits

12. MooLVL2Algs 10GeVm- 10GeVm- counts counts MooAlgs MooLVL2Algs MooAlgs N associated (RPC hits) N associated (MDT hits) Hits comparison • Same dataset used for MooAlgs MooLVL2Algs • The 2 different sets of algos are chained together • Fit results from the same sample of events are directly compared Plans for testing a more general hit addition procedure

13. 1TeVm- counts MooLVL2Algs 10GeVm- counts MooLVL2Algs c2 MooAlgs c2 c2

14. 10GeVm- counts 10GeVm- counts MooLVL2Algs MooAlgs 1/pT pull 1/pT pull 1/pT pull

15. 10GeVm- counts MooLVL2Algs 10GeVm- counts MooAlgs pT resolution pT resolution pT resolution