1. Vertex Detector: Engineering Issues Craig Buttar University of Glasgow Cambridge GLDC meeting Sept 07

2. Design Features Outer radius ~ 6 cm Barrel length ~ 14 cm Ladder widths 1-2 cm Disks to cover forward region (GLD) (LDC) (SID) A bit larger than this

3. Optimizing Vertex Performance Close to IP –Reduce extrapolation error –Inner radius ~1.5cm Position resolution (<5 microns) –Impact parameter resolution ≤ 5µm  10µm/(p sin 3/2  ) Minimise multiple scattering –Material ~ 0.1X 0 /layer 5  m resolution or better is possible with current sensor technology –Need good alignment to exploit this Minimal mass is crucial –Constraints on mechanics –Constraints on power Cooling Power delivery –Alignment Parametric simulation assuming: 0.1% RL per layer 5 micron resolution 1.4 cm inner radius Varying each parameter ILC target

4. Material budget Service handling at ends of barrel are the problem The boring stuff is important! Breakdown for pixels cos  =0.95 ATLAS Tracker Sensors (300  m) 1.1% Ro+bump bonds1.4% Hybrid1.0% Local support+cooling5.4% Cables0.3% Global support1.5% Total for 3 layers10.7%

5. Mechanical Support 0.1% X 0 /layer  100  m of Si Need to start with thin Si, typically 20  m Thin supports –Carbon fiber-based supports, similar to D0 layer 0/CDF Layer00 –Foam-based (SiC, RVC) supports (LCFI) –Silicon picture frame (MPI) System Issues –Planarity of the sensors –Bonding to thin silicon –Thermal bowing –Connection to external cables MPI Design (University of Washington) (LCFI) (SID inside support cylinder)

6. SiC Foam Ladder 20 um thick silicon 1.5 mm thick SiC foam –8% relative density Silicone adhesive pads –1mm diameter 200 microns high on ~5mm pitch ~0.14% X 0 SiC ladder ladder block glue annulus block mm um mm um LCFI

7. RVC Foam/Silicon Sandwich Ladder 20 micron thick silicon 1.5 mm thick RVC foam –3% relative density Silicone adhesive pads –on ~5mm pitch Tension ~1.5 N ~0.08% X 0 RVC sandwiched ladder silicon spacer ladder block glue annulus block Tension um mm LCFI

8. Air Cooling Air cooling is crucial to keep mass to a minimum –Require laminar flow through available apertures –This sets total mass flow – other quantities follow –Implies a limit on power dissipation For SiD design –Use the outer support CF cylinder as manifold (15mm  r) –Maintain laminar flow (Re max = 1800). –Total disk (30W) + barrel (20W) power = 50W average For SiD ~ 131 µW/mm 2. Max  T ~ 8 deg (Cooper, SID)

9. Cooling Studies Test model of 1/4 Barrel Cold nitrogen cooling Heaters at ladder ends Parallel CFD simulations Flow 5-20 SLM –0.5  2 g/s whole detector –Laminar flow Power Extracted (W) Temperature Difference (K) LCFI

10. Alignment is critical ILC physics programme depends on identification of secondary vertices Ability to do this depends on tracking resolution Tracking resolution dependent on alignment precision Individual hit resolution may be O(5)  m –Alignment must be better, so that contribution in quadrature does not degrade hit resolution

11. Alignment – LHCb VELO Rigidity low CTE overlaps 10  m alignment Hardware DesignSoftwareMetrology Measurement machine Individual modules during assembly Complete system 10  m alignment Alignment at few  m level Iterative / non -iterative methods BEFORE / AFTER For ILC vertex detector Position of detectors on ladders to ~10  m Thin detectors  Warping (SLD) Thin ladders  not rigid Low mass beam pipe  Vertex detector will move wrt experiment

12. Design Design into system features for alignment – Rigidity, thermal and humidity expansion This is difficult at low mass –Overlaps – not just for coverage, e.g. –VELO left, right half overlap –SLD CCDs

13. Metrology - importance Starting point for alignment parameters Constrains degrees of freedom not accessible from alignment system e.g. large systematic on particle lifetimes is radius of barrel e.g. +/- 40 um on 4cm = 1% e.g. aspect ratio of vertex detector gives  systematic – important for FB asymmetries Define/understand elements: –Ladders Ideally rigid, 6 dof/ladder (372 for LCFI barrel) Ladders are not a rigid object eg detector bow, CTE –Develop models? Difficult to measure during construction need to understand effect of thermal changes eg CTE, tension due to mechanics and services? (CTE studies by LCFI) –Greater no. of degrees of freedom than ladders x 6 (ATLAS has 34,992 dof) –Requires good initial survey and understanding of changes »Difficult to do under in-situ conditions

14. Power delivery High currents to drive CCD clock pulses Minimise voltage drop on power cables –Low resistance  more conductor mass (Cu) –0.5V drop at 6cm ~ 0.5%X 0 Use serial powering –Power at higher voltage, locally regulate at detector –Reduces conductor mass –0.5V drop at 6cm ~ 0.04X o Issues –Failure in string –Coherent noise –Increase complexity of interconnects UK-ATLAS activity for sLHC upgrade

15. UK Experience ATLAS barrel and endcap silicon tracker, LHCb VELO –Sensors (strips) –Readout electronics –Module construction –Engineering –Cooling – liquid based –Alignment LCFI SLD CCD based vertex detector ALEPH, DELPHI, OPAL strip-based vertex detectors CDF Layer-00 strip-based vertex detector

16. Summary/conclusions Low mass critical to achieve required IP –Challenging eg ATLAS is ~ 10.7%X 0 for 3 pixel layers –Dominated by support and cooling Target layer thickness 0.1%X0 (100  m Si) –Thin sensors –New support materials –Air cooling  limits power to ~O(10W) –Also implications for services  serial powering Need to consider alignment in hardware –Design: overlaps in system (increase material) –Metrology during assembly –Warping of thin detectors and ladders –Report of LHC alignment workshop: CERN yellow report 2007-004 Thanks to: Mark Thomson, Tim Greenshaw, Joel Goldstein, Chris Parkes, Val O’Shea, Richard Bates

17. Barrel Layout Beam pipeLadder (detector element) Foam cryostat Beryllium support shell Spring Ladder block Annulus block Fixed endSliding end Readout and drive chips Substrate Silicon sensor Beryllium support shell Annulus and ladder blocks

18. Barrel Layout Layer no No of Ladders Radius (mm) Active length (mm) Active width (mm) Tilt angle Overlap (mm) 1815(19)1001300 2826(28.5)2502200.42 3123725022151.3 4154825022150.86 5196025022151.2 Looking at: –the radius of the layers –width of elements –tilt angle

19. Metrology - Equipment Smartscope Small scale items – not full system –High precision O(2)  m XY O(10)  m Z –Optical head –Automatic pattern recognition –Excellent for measuring sensor curvature –Individual sensors not double sided modules – no alignment to reverse side

20.  Residuals are function of the detector resolution and the misalignments From this…  The geometry we are looking for is the one which minimizes the tracks residuals … to that  Alignment principle : Software Alignment Each individual ‘unit’ has six degrees of freedom Need to apply global transformation constraints

21. All software alignment procedures follow one of these two forms: conclusion : both methods can be made to work well. Misaligned detector Geometry not corrected Plot and fit the residuals distributions Best mean and  values ? Detector aligned YES NO Fit the tracks Modify the geometry ITERATIVE Misaligned detector Geometry not corrected Detector aligned YES NO Fit the tracks & the residuals Outliers rejected ? Non- linearities corrected ? NON-ITERATIVE Iterative / Not Iterative Iterative: fit biased tracks then fit alignment constants, iterate to reduce bias Non-Iterative: fit tracks and alignment constants simultaneously

22. ŒEstablish linear expression of residuals as a function of mis-alignments. Fit the tracks simultaneously with the alignment constants  Get all track parameters and all misalignment constants simultaneously  1 single system to solve.  But this system is huge ! (N tracks ∙N local +N global equations) BUT… x clus = x track +  x Parameters  i of the tracks (different for each track) x clus = ∑  i ∙  i +  x LOCAL PART x clus = ∑  i ∙  i + ∑ a j ∙  j Residuals expressed as function of misalignments  i GLOBAL PART Global Alignment Method – H1, LHCb, ATLAS r clus = (x clus - x) ∂  2 ∂ ∆ i ∂  2 ∂  i = = 0 Alignment  minimise  2 res = ∑ ∑w clus ∙r 2 clus

23.  The matrix to invert has a very special structure:  Inversion in section (implemented in the code MILLEPEDE V.Blobel - NIM. A 566), The problem becomes only N global x N global  If N global  100, the problem can be solved in seconds  k C k global HkHk HkTHkT  kk = 0 0 C k local 0 0 0 0 … … …… … … kwkxkkwkxk kwkkkwkk …… …… N global N local x N traces Matrix Inversion

24. Other Interesting Techniques Kalman Filter Alignment – CMS –Iterative –Updates alignment constants immediately after each track SLD –Residuals as a function of misalignments –Fit residuals as a function of position –Determine alignment constant from matrix inversion