Calibration and alignment software

1. Calibration and alignment software Marian Ivanov

2. Outlook • Impact of systematic effects on physical results • TPC calibration • TPC alignment

3. Statistical uncertainty • R-Phi and Phi resolution for perfectly aligned and calibrated TPC (at the TPC entrance) • Given by the cluster position resolution ( divided by sqrt(Npoints)) • At low momentum – influence of the multiple scattering

4. Misalignment of detectors • Linear misalignment can be detected by our algorithm • Statistic of 2000 tracks per sector (IROC+OROC) ( 72000 tracks) is big enough to be on the level below statistical uncertainty • Tested with stand-alone (fast) simulator • Following slides – precision of the alignment parameter determination for two different statistic sets

5. Track fitting • AliRieman used for track fitting • Less than 1 s for track fitting (20000 tracks) • Picture: • Pt resolution for non aligned sectors • Input misalignment • 2 mm in translation • 1 mrad rotation 1/ptrec-1/pt

6. Results –Rotation Z • Left side – 2000 track samples • Right side – 5000 track samples

7. Translation X • Left side – 2000 track samples • Right side – 5000 track samples

8. Translation Y • Left side – 2000 track samples • Right side – 5000 track samples

9. Result (Pt residuals) • Relative pt resolution (dpt/pt) • Left side before alignment • Right side after alignment

10. Alignment - ExB • ExB effect – simulated – linear dependence expected • Xshift = kx*(z-250) – kx=0.005 • Yshift = ky*(z-250) - ky=0.005 • The same in both sectors • Alignment with tracks (2000 track samples) • Systematic shifts in translation estimates (negligible in comparison with statistical error) • X – 0.02 mm, Y – 0.08 mm, Z – 0.003 mm • Systematic shift in rotation estimates • Rz – 0.05 mrad, Ry – 0.006 mrad, Rx – 0.006 mrad

11. Warning example - STAR - TPC GridLeak distortion • Dependence on field, track charge, location, luminosity consistent with ion leakage at gated grid gap • Hopefully not the case of Alice TPC

12. Alice ExB distortion (M.Kowalski) • Radial distortions at lower and outer TPC radius due to the nonuinformity of magnetic field – E field perfectly aligned with B field at central membrane • Alice - Omega tau – 0.354 (E=400V/cm, B=0.5T) • Note : • Non linear as function of z • Phi dependence

13. Alice ExB distortion (M.Kowalski) • Azimuthal distortions at lower and outer TPC radius due to the nonuinformity of magnetic field • Dy = 90cm x 0.0018 ~0.16 cm (STAR reported magnitude of correction on the level ~0.1 cm – nucl-ex/0301015) • Systematic error - 4 times bigger than statististical

14. Alice ExB distortion • Influence • Systematic effect to the DCA resolution • The distortion z and theta dependent • For the first analysis the cut on the DCA has to be adjusted • The influence on the pt resolution will be estimated • Realistic magnetic field description needed (see next slides) • Track finding efficiency in TPC should be not be affected – (ExB distortion is a smooth function) • Influence on the TPC-ITS track matching

15. L3 field components Tesla calculation (M.Losasso)currently in Aliroot I = 30 kA

16. L3 field components Measured field, I = 30 kA (from ntuples of A.Morsch) No corrections for possible probes misalignment applied

17. Drift velocity • Requirements (systematic error on the level of statistical error) • Z resolution ~ 0.01 cm • vdrift precession ~ 0.4*10^-4 • Measurements • Drift monitor – GOOFY ~ 10^-4 • Tracks crossing central membrane • STAR TPC • (Initial) drift velocities determined / monitored with lasers • Automated updating of drift velocities (and initial T0) from laser runs • Checked / fine-tuned by matching primary vertex Z position using east and west half tracks separately (Alice – algorithm tested by C.Cheskov) • Ideally determined from track-matching to SVT (perpendicular drift), but requires all other calibs to be done already! (principle has been tested)

18. Electron attachment • Electrons can be absorbed in the gas during the drift • The probability to be captured by an O2 molecule is 1% per 1 m drift per 1 ppm of O2 (NA49) • Alice – expected oxygen content (ALICE MC)~ 5 ppm • Should be achieved (Joachim) • Influence • Non systematic effect to the position resolution • Affects only statistical uncertainty by a factor sqrt(absorbtion) and dEdx measurement • Does not affect multiplicity measurement

19. Gain calibration • The chip gains vary in range of 5% • Expected cluster position variation on the level of 0.05* pad width • Expected random behavior • The gain variation due to electrostatics (for example anode wire sagita) • does not affect the cluster position – (the effect of local variation of gain is negligible as compared to cluster size) • Influence: • Small influence on the pt resolution and efficiency • dEdx affected

20. TPC calibration: Outlook • TPC calibration parameters • TPC calibration classes • MI approach: • The size of the calibration data in CDB (Condition Database) and in memory (during reconstruction) dominated by the size of data for pad by pad. Everything else negligible. •  Store all data which can be used in the reconstruction, respectively which can used to indicate problems. • Particularly the data from the sensors (voltages, currents, temperature sensors) • Offline code status

21. Calibration classes • AliTPCCalDet • Calibration parameters specific to each sector: • One array of 72 floats • AliTPCCalPad • Parameters specific to single Pad: • GainFactor, T0, Pad Response Function Width, Noise • Used to pattern local variations of detector parameters • One array of 72 AliTPCCalROC objects • AliTPCCalROC • Actual container of single ROC specific data • One array of [Nchannels] floats • Nchannels depends on the type of sector in stack (inner, outer) • Interface • AliTPCCalROC(Int_t sector) • SetValue(padrow, pad, value) • GetValue(padrow, pad) • Memory consumption • Npads x sizeof(value) • 0.5 million channels * sizeof(value) • 1D array for each sector • Mapping index – (padrow- row) using external map array class AliTPCRoc (1 per outer sector, 1 per inner sector)

22. TPC calibration parameters –per pad • The difference between relative and absolute is in the data volume • ~ 2MBy relative • ~ 8 MBy absolute • Current implementation in AliRoot – use floats

23. TPC conditions – per set of sensors • The format should be defined as soon as possible • Avoid problems with versioning • Define queries • Data volume depends on the sampling frequency • Can be reduced by fitting • The data format and functionality – Not TPC specific • Common class should be defined • Request for offline group presented (Hopefully someone will implement it)

24. TPC calibration parameters – per TPC

25. Altro setup

26. TPC calibration parameters – per TPC • The above result in the distortion map • The data volume depends on the grid size

27. TPC parameters for reconstruction

28. Shuttle Schema • AliShuttle – The Shuttle program manager. Organizes conditions data retrieval, preprocessing and storing it to CDB. • AliShuttleConfig – Interface to the configuration stored into LDAP server • AliDCSClient – Provides DCS API. Communicates with DCS AMANDAserver over TCP/IP • AliShuttleTrigger – Interface to DAQ LogBook and client to DAQ “End of Run” notification service

29. Offline calibration - Status • Calibration classes for pad parameters implemented • Default parameters stored in the database • Pad gain variation (+- 5%) • Used in simulation and reconstruction • Noise, T0, and Preamp shaper width - will be implemented soon in the simulation • Typical variation of parameters needed as input

30. Alignment - Outlook • Toy model results presented in previous slides • Short overview of reconstruction framework (Cvetan Cheskov) • Current development • Implement alignment algorithms inside of AliRoot alignment framework

31. Alignment framework • Space-points extraction and processing (filtering) • Track fitting • Track extrapolation points • Residuals minimization

32. Framework Overview 1/2 Phase III Phase IV

33. Space-points retrieval (Phase I) • During the reconstruction, in between backward propagation and refitting: • Loop over ESD tracks and sub-detectors (ITS,TPC,TRD,TOF,RICH): • Get cluster indexes • Call trackers to get the space points • Store the points inside the ESD track • The storage of space-points is controlled by AliReconstruction::SetWriteAlignmentData() • Unified AliESDtrack method of getting #clusters and their indexes: • GetNcls(Int_t iDet) & GetClusters(Int_t iDet, UInt_t*) • Abstract method of AliTracker: • GetTrackPoint(Int_t index, AliTrackPoint &p) • Method implemented for ITS,TPC,TRD,TOF

34. Space points filtering (Phase II) • Filtering: • Take the ESD trees in a TChain • Select on ESD track parameters • Store selected space point arrays into tree (in local file) for further analysis • So far a simple (local analysis case) ESD processing is implemented • A TSelector prototype is being implemented (distributed analysis case)

35. Framework Overview 2/2

36. Alignment of volume(s) Load space-points arrays with >=1 point in volume(s) A Apply accumulated alignment info (AliAlignObj) for all space-points in volume(s) A and B • Base method for aligning volumes: AliAlignmentTracks::AlignVolumes() • What does it do? • It aligns a volume A (set of volumes) w.r.t to another volume B (set of volumes) • The input is: two arrays (A&B) of ints (volume unique IDs) • The output is: updated alignment info for the volume(s) A • Note: volume sets A and B can (partially) overlap • Several predefined methods to align single volumes, layers are implemented Fit space-point arrays (tracks) in volume(s) B and extrapolate them to volume(s) A Arrays with track extrapol. points in volume(s) A Arrays with all space-points in volume(s) A Calculate and minimize residuals in volume(s) A Update alignment info (AliAlignObj)

37. Track fitters • Base class for track fitters – AliTrackFitter: • Interface to space-point array being fitted • Interface for getting the two space-points arrays (residuals) • Abstract Fit() method: • Fits the track within user-defined volume(s) • Prepare the arrays with residuals • To do: all fitters share some part of Fit() method  move Fit() to the base class and define some methods inside as abstract • Getters for fit quality information • Current status • AliTrackRiemanFitter implemented • Ongoing development (MI and Cvetan) • Interface to the ROOT TLinearFitter (Possibility to use “Robust” fitter) • Linear fit, parabolic fit, Rieman fit with tilting angles ( for TRD), parabolic fit with tilting angles • Interface to the Kalman fitter (AliExternalTrackParam)

38. Track Residuals minimization • Base class for residuals minimization – AliTrackResiduals: • Two classes implemented: • Minuit based (AliTrackResidualsChi2) • Fast linear minimization (AliTrackResidualsFast): • Assume small mis-alignment rotation angles:  linear transformation • Sufficient precision assuming angles ~mrad • Interface to the TLinearFitter to be implemented • Possibility of fixing parameters • Robust fit

39. Alignment - status • The misalignment implemented in the simulation • The correction for the misalignment implemented in the reconstruction • Test with misalignment on the level +-1.5 mm and angular misalignment 0.6 degree made • The performance of tracking with perfect alignment parameters – almost the same as with ideal geometry • First attempts to use alignment framework (“real MC” data) – work in progress