Calibration and alignment software Marian Ivanov

Outlook Impact of systematic effects on physical results Impact of systematic effects on physical results TPC calibration TPC calibration TPC alignment TPC alignment

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

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

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

Results –Rotation Z Left side – 2000 track samples Left side – 2000 track samples Right side – 5000 track samples Right side – 5000 track samples

Left side – 2000 track samples Left side – 2000 track samples Right side – 5000 track samples Right side – 5000 track samples Translation X

Left side – 2000 track samples Left side – 2000 track samples Right side – 5000 track samples Right side – 5000 track samples Translation Y

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

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

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

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 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 – (E=400V/cm, B=0.5T) Note : Note : Non linear as function of z Phi dependence

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

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

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

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

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 STAR TPC (Initial) drift velocities determined / monitored with lasers (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)

Electron attachment Electrons can be absorbed in the gas during the drift 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) 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 Alice – expected oxygen content (ALICE MC)~ 5 ppm Should be achieved (Joachim) Should be achieved (Joachim) Influence Influence Non systematic effect to the position resolution Non systematic effect to the position resolution Affects only statistical uncertainty by a factor sqrt(absorbtion) and dEdx measurement Affects only statistical uncertainty by a factor sqrt(absorbtion) and dEdx measurement Does not affect multiplicity measurement Does not affect multiplicity measurement

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

TPC calibration: Outlook TPC calibration parameters TPC calibration parameters TPC calibration classes TPC calibration classes MI approach: 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. 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.  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) Particularly the data from the sensors (voltages, currents, temperature sensors) Offline code status Offline code status

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 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 Interface AliTPCCalROC(Int_t sector) AliTPCCalROC(Int_t sector) SetValue(padrow, pad, value) SetValue(padrow, pad, value) GetValue(padrow, pad) GetValue(padrow, pad) Memory consumption Memory consumption Npads x sizeof(value) Npads x sizeof(value) 0.5 million channels * sizeof(value) 0.5 million channels * sizeof(value) 1D array for each sector 1D array for each sector Mapping index – (padrow- row) using external map array class AliTPCRoc (1 per outer sector, 1 per inner sector) Mapping index – (padrow- row) using external map array class AliTPCRoc (1 per outer sector, 1 per inner sector)

TPC calibration parameters –per pad Parameter N. of channels UnitSource Update frequency Gain factor RelativeOffline/HLTRare Time Relative ? Offline/HLTRare Preamp-shaper width Relative ? Offline/HLTRare Noise Relative (sigma) ?Rare The difference between relative and absolute is in the data volume The difference between relative and absolute is in the data volume ~ 2MBy relative ~ 8 MBy absolute Current implementation in AliRoot – use floats

TPC conditions – per set of sensors Parameter N. of channels InformationSource Update frequency Temperature probes ~4500 sensors on FEC, snesors on space frame? ?? Interface to DCS Array of : ID, position, samples (temparature) in time DCS and ? Per run High voltage ? Array of : ID, samples (voltage and current) in time DCS Per run Drift voltage (VHV) ? Array of : ID, samples (voltage and current) in time DCS Per run Gating voltages ? Array of : ID, voltage DCS Per run Laser parameters Array of : ID, position, angles ? Per surveyer measurement The format should be defined as soon as possible The format should be defined as soon as possible Avoid problems with versioning Define queries Data volume depends on the sampling frequency Data volume depends on the sampling frequency Can be reduced by fitting The data format and functionality – Not TPC specific The data format and functionality – Not TPC specific Common class should be defined Request for offline group presented (Hopefully someone will implement it)

TPC calibration parameters – per TPC Parameter N. of channels InformationSource Update frequency Oxygen content 1 Samples in time DCS Per run Drift velocity monitor (Goofy) 2 Samples in time DCS? Per run

Altro setup Parameter Data volume Source Update frequency Altro frequncy 0 Altro acquisition window 0 Moving average (on/off) 0 Zerro suppresion (on/off) 0 Tail cancelation (on/off) 0

TPC calibration parameters – per TPC Parameter Data volume Source Update frequency Drift velocity map (parameterization) ?OffflineRare Space charge map ?OfflineRare ExB correction map ?Offline Per change of magnetic field The above result in the distortion map The above result in the distortion map The data volume depends on the grid size The data volume depends on the grid size

TPC parameters for reconstruction Parameter Data volume Source Update frequency Signal shape parameterization (diffusion parameter) 0OffflineRare Local error parameterization () 0OfflineRare

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

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

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

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

Framework Overview 1/2 ESD file with track space-points ESD file with track space-points ESD file with track space-points Tree with Selected Space points Build tree index Alignment procedures Local file Reconstruction Phase I Distributed Local Phase II Phase III Phase IV

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

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

Framework Overview 2/2 CDB Iterations loop (user-defined) Loop over volumes (user-defined) Update alignment objects Align volume(s) CDB Fit tracks Minimize residuals File

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

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

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

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