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Alignment, Closing Strategy

Alignment, Closing Strategy. Silvia Borghi. Outline. Alignment Silvia Borghi, Marco Gersabeck, Stefano De Capua (PVSS use of resolver) Perform alignment on TED data Motion System position stored in database and used to update alignment constants

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Alignment, Closing Strategy

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  1. Alignment, Closing Strategy Silvia Borghi

  2. Outline • Alignment Silvia Borghi, Marco Gersabeck, Stefano De Capua (PVSS use of resolver) • Perform alignment on TED data • Motion System position stored in database and used to update alignment constants • Implement alignment monitoring , add Green/Red light from alignment for data processing • Improve primary vertex alignment method for relative alignment of the two VELO halves • Alignment run in FEST • Test/tune HLT alleys for halo tracks (lower priority) • Closing Malcolm John, Stefano De Capua (PVSS) • Test HLT vertex reconstruction and display in online monitoring with FEST data • Test PVSS closing procedure • Setup online presenter pages for beam position monitoring Silvia Borghi

  3. Alignment Silvia Borghi, Marco Gersabeck, Stefano De Capua (PVSS use of resolver)

  4. Perform Alignment for TED data • Module alignment constants evaluated by different methods using Aug. and Sept. data samples • Millepede considering X & Y trans and Z rot, • Kalman considering X & Y trans. • The detector displacement from metrology is less than 10 m • Module alignment precision is 5 m for X and Y translation and 200 rad for Z rotation Diff. of alignment const. obtained using Aug. and Sept. data samples for X and Y translations Silvia Borghi

  5. Motion System position • Resolver position from the motion system for x and y position of each VELO half • automatically updated into online CondDDB at beginning of run if it is changed from the previous one • used by the HLT • automatically updated into a private CondDDB each time it changes • VELO position used in the reconstruction in LHCb reference system is determined by a function that combines the Alignment constants and the resolver position (transparent for the user). Silvia Borghi

  6. Align Const in Offline CondDB ResolPos in Online CondDB Align. const. and resolv. pos. in CondDB Closing procedure Read Module & Sensor Align. Const 2 halves Align const. not used! Read Velo ResolPos from PVSS Write Read Read Physics Data Taking Read Read Run Alignment Changed? No Yes Write Offline Reconstruction Read Read Silvia Borghi

  7. Implement alignment monitoring: Sensor • Online monitoring: • Residual distribution for each sensor • Overview: Residuals versus sensor number • Histogram of Mean and RMS of residual distribution for each sensor • Offline monitoring: • Measure size of misalignment from residual vs Φ distribution • Analyse the plots by fitting sinusoidal functions using python scripts • Script has two levels: warnings and alarms • Warnings are counted • Alarms are counted and printed for each module that causes an alarm • Thresholds depend on fit values (≈ misalignments) and their significances • Produce overview plots of both Silvia Borghi

  8. Implement alignment monitoring: Sensor • Traces of a ‘Gauss bug’ in only 98 events Misalignment Significance Without Metrology = full effect of bug With Metrology = less affected by bug Silvia Borghi

  9. VELO alignment monitoring • Monitoring of the VELO half alignment • Monitor VELO half alignment through primary vertices by Offline PV Tool • Reconstruct PV position with tracks of only one half at a time • Plot A and C side PV position • Plot A-C side 2D difference in PV position • Script for warnings and alarms should be implemented Silvia Borghi

  10. Results on the FEST data week 2nd -6th March 2009 Only sensible conclusion from an alignment point of view: PV left x – PV right x VELO halves are 8 μm apart Beam pos. (x, y) = (15, 30) is nothing that can be used as an alignment constant ambiguous interpretation not at all indication of problems VELO alignment monitoring Mean 8 m PV left-right x [mm] Mean 11m Mean 19 m Mean 30m PV right x [mm] PV left x [mm] PV Y [mm] Silvia Borghi

  11. VELO half alignment Velo half alignment via MILLEPEDE [minimisation done with single matrix inversion]: • Overlaps tracks: • Measurements: point coordinate (x,y,z) • Local variable: track slope and intercept (a, b, c, d) • Global variable: half misalignment parameters (x, y, z,, ,) • PV method: • Measurements: track slope and intercept (a, b, c, d) • Local variable: vertex coordinate (vx,vy,vz) • Global variable: half misalignment parameters (x, y, z,, ) Require a PR in the global frame to determine overlaps also in the case the VELO not completely closed Silvia Borghi

  12. PV method Improvement of PV method for the VELO half alignment, based on Millepede: • Selection of event with only one PV found using only the tracks reconstructed in each half • PV offline tool used to determine the tracks coming from the same PV • Track slope and intercept are evaluated by a linear fit based on least square method • PV coordinates are the local variables evaluated in Millepede (the PV position evaluated by the PV offline tool is not used) • Performance of the method is under study. Silvia Borghi

  13. Alignment with FEST data • Update of software-options for both methods • Changes will be available in the new release of Escher • FEST data on 2nd-6th March 2009 week • Input misalignment 10 micron misalignment along x between the two halves • Kalman results 11-12 m • Millepede result 8 m • It should be tested to be able to run on calibration farm Not yet final... Should be re-evaluated... Silvia Borghi

  14. HLT Alley: An alignment-friendly PatRec Silvia Borghi • The need for tracks parallel to the beam axis has been emphasised many times • No suitable pattern recognition exists to easily do this and to be used at HLT level • A new fast, efficient, and pure pattern recognition was developed • The idea of the new pattern recognition: • Look for a certain number (small range) of space-points in r-phi projection of all modules (both halves) • Check that this number is not produced by combinatorics on a single module • Ensure that the number of space-points is a local maximum • Optional: Check whether track candidate has a minimum number of space-points in both halves to detect overlap tracks • Don’t fit the track candidate as this is thought to be a filter only, to select useful events for alignment

  15. HLT Alley: An alignment-friendly PatRec Silvia Borghi • The status: • Filter implemented as PatVeloAlignTrackFilter in Tf/PatVelo • Two ‘alleys’ for generic parallel tracks and for overlap tracks specifically • Can pre-scale differently and hence enhance overlap sample • Outputs of these ‘alleys’ should end up in calibration stream • Future plan • Tune the parameters on Montecarlo minimum bias events and ‘halo’ events

  16. work work work work ongoing ongoing ongoing ongoing Conclusion on Alignment • Perform alignment on TED data • Motion System position stored in database and used to update alignment constants • Implement alignment monitoring add Green/Red light from alignment for data processing Green/Red light should be tuned. The procedure should be integrated in the general alignment and data quality procedure • Improve primary vertex alignment method for relative alignment of the two VELO halves First results are promising but performance should be evaluated • Alignment run in FEST Missing test to run it on the calibration farm • Test/tune HLT alleys for halo tracks (lower priority) Partially done before 17 April Silvia Borghi

  17. VELO closing Malcolm John, Stefano de Capua with Paula Collins, Eddy Jans, Kurt Rinnert

  18. What the VELO needs • Safety— Detector integrity is paramount • The strategy is to use all information available from the detector and the machine to make a self-consistent decision • Speed— Don’t waste any more stable-beam time than necessary • motion takes 150 sec from OPEN to CLOSE. • Aim that monitoring, validation and decision-making < 30 seconds (total) • Want quasi-real-time feedback on the beam position (~2s/measurement) • Strategy— Use the HLT for the CPU intensive 3D reconstruction • Leaves the monitoring job “just” filling histograms. NB: In very early running, with lower luminosity, refresh rates will be slower Silvia Borghi

  19. HLT-resident, 3D vertex reconstruction DecodeLiteCluster, PatVeloSpace, PatVeloGeneral, PatPV3D, HLTVeloClosingDecision Two independant lines for A-side and C-side. Vertex measure w.r.t. each half The resolver measurement of the opening is NOT broadcast to the HLT - too slow. Vertex stored in a RawBank HLTVelo-accepted event Histogramming in the online monitoring Online monitoring job that uses a selective trigger mask (0x00 0x08 0x00 0x00) Unpacks HltVertexReport from the RawEvent and histograms vertex x, y, z & nTrks Every 1000 events, the vertex distribution is fitted by a Gaussian and then cleared. The mean, RMS, mu and sigma are written to a PVSS data-point DIM data-point Luminous region position PVSS-based closing manager The core of the closing logic. Takes all available information, makes a judgement on its consistency and suggests the next move... Instruction sent to motion control DIM data-point Silvia Borghi

  20. Screenshot: ‘live’ VELO-open FEST data Latest 1000 events Accumulation Luminous region x-position w.r.t. the A-side = -30 mm Screenshot taken after ~250000 FEST minimum bias events (i.e. 1/40th of a second of nominal running) DIM data point monitoring N(evts): ~21700 triggered in HLT VELO lines ~12300 had a vertex found by A-side LOTS! But only requiring >4trks/vertex here, In reality, will require 10 to improve resolution. N(trks)/vertex Silvia Borghi

  21. PVSS closing manager Real-time vertex monitoring • Performs consistency checks between motion and measurements as well as monitoring the key hardware. • Its output can be: • Retract and park! • Request human! • Move to new position! Resolver measurement LHC beam position information at IP8 Silicon bias current BCM relative flux Silvia Borghi

  22. SCREEN-SHOT Silvia Borghi

  23. 1st version graphical page for closing manager • Graph for A and C sides • X resolver position • X vertex position • X sigma of vertex position • Graph • Beam position • A side: X Res. Pos. – X Vert. Pos. • C side: X Res. Pos. – X Vert. Pos. Res – PV for A side 14 mm – (-13 mm) =27 mm Beam position = 1 mm Res – PV for C side Silvia Borghi

  24. work work work ongoing ongoing ongoing Conclusion for Closing • Test HLT vertex reconstruction and display in online monitoring with FEST data Online monitoring task keeps up with 100% of the HLT output Demonstrates the selective trigger mask and use of HltVertexReports Velo HLT lines work as expected on FEST data • PVSS closing manager extensively tested • Setup online presenter pages for beam position monitoring • Add graphical front page to closing manager to ease human digestion Plans • Revisit the closing strategy documentation based on recent updates. Write Twiki. • Liaise with LHC to confirm the provision of their beam-position measurement and understand what information the wish from us • Add new information - from tracks traversing both VELO halves. Investigate a new PR algorithm that forms space-points from overlapping phi-sensors when the VELO is <1.7mm open. should be done before 17 April Silvia Borghi

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