1. Fermi-LAT: A Retrospective on Design, Construction, and Operation and a Look Towards the Future Bill Atwood Dec 6, 2011 HSTD-8 1

2. Plastic Scintillation counters to veto entering charged particles  e+e+ e–e– Tungsten Conversion Foils Position Measuring Detectors Measured Track co-ordinates Total Absorption Calorimeter to measure gamma ray energy Z e+ e-  Energy loss mechanisms Pair Cross-Section saturates at E  > 1 GeV Gamma Ray Pair Conversion Z=74 Tungsten e+ e- High Electric Field High Energy Gamma Ray Pair QED Process Splitting Function E e+/ E  E   = 10 MeV) Opening Angle Energy From Rossi, High Energy Particles, 1952 At 100 MeV  Open ~ 1 o 2

3. Previous Satellite Detectors 1967-1968, OSO-3 Detected Milky Way as an extended  -ray source 621  -rays 1972-1973, SAS-2, ~8,000  -rays 1975-1982, COS-B orbit resulted in a large and variable background of charged particles ~200,000  -rays 1991-2000, EGRET Large effective area, good PSF, long mission life, excellent background rejection >1.4 × 10 6  -rays SAS-2 COS-B EGRET OSO-3 SAS-2 COS-B EGRET 3

4. Conception GLAST was the amalgamation of many ideas and concepts from the Experimental Particle Physics in the 1980’s and early 1990’s MACRO – Grand Saso Modularity ALEPH SSD Detector Xtal Calorimeter Hodoscopic Design P. Persson – P. Carlsen EGRET onboard CGRO For Space Instruments: Solid State Detectors Silicon Strip Detector: SSD 4

5. Evolution of GLAST April, 1991 CGRO (with EGRET on board) Shuttle Launch May, 1992 NASA SR & T Proposal Cycle 3. Pick the Rocket Delta II (launch of GP-B) 4. Fill-it-up 4. Fill-it-up ! Rocket Payload Fairing Diameter sets transverse size Lift capacity to LEO sets depth of Calorimeter 2. Make it Modular Select the Technologies 1. Select the Technologies Large area SSD systems and CsI Calorimeters resulted from SSC R&D Another lesson learned in the 1980's: monolithic detectors are inferior to Segmented detectors Cheap, reliable Communication satellite launch vehicle Original GISMO 1 Event Displays from the first GLAST simulations 5

6. First Oral Presentation of GLAST: HSTD-1 6 May, 1993

7. At this point there were just 10 collaborators! and the Conference Proceedings… 7 Now over 400 and from 7 countries

8. 8 Overview of GLAST- LAT e+e+ e–e–  TrackerTracker 18 XY tracking planes with interleaved W conversion foils. Single-sided silicon strip detectors (228 μm pitch) Measure the photon direction; gamma ID. CalorimeterCalorimeter 1536 CsI(Tl) crystals in 8 layers; PIN photodiode readouts. Hodoscopic: Measure the photon energy; image the shower. Anticoincidence Detector (ACD)Anticoincidence Detector (ACD) 89 plastic scintillator tiles. Reject background of charged cosmic rays; segmentation removes self-veto effects at high energy. Calorimeter Tracker ACD Electronics SystemElectronics System Includes flexible, robust hardware trigger and software filters.

9. And… After 2 Years 9

10. Silicon Detectors: the choice that keeps on giving!  Thin detectors leads to optimal arrangement of radiators  Near 100% efficiency leads to new tracking paradigms and more  Extremely low noise results in few false triggers and low confusion in tracking  Long term stability promotes optimal recon strategies  Fine granularity allows for a precision snap shot of the conversion and resulting tracks What follows are several examples capitalizing on these properties, which were not anticipated from the outset. 10

11. Detector Layout and the PSF Multiple Scattering limits tje resolution over much of the High Energy Band MS in a single Converter: Y3Y3 Y2Y2 Y1Y1 X3X3 X2X2 X1X1 High Energy Gamma Ray X 1 is not usable By Y 1 MS = Error Y 1 – Y 2 : Y Error by Y 2 : X Error by X 2 : Error Box Area: X 1, Y 1 X 2, Y 2 X 3, Y 3 Ratio: Distrib./ Discrete = 1.9 (if X 1 usable: Ratio =1.05) The elimination of lever-arms between radiators and detectors minimizes radiators and detectors minimizes MS effects! MS effects! Distributed Detector Discrete Detector 11

12. Tracking: Tracker Design and Analysis Basics Pair Conversion Telescope Layout Multiple Scattering Trim Radiator tiles to match active SSD area Close spacing of Radiators to SSDs minimizes multiple scattering effects Plane-to-plane spacing and SSD strip pitch sets meas. precision limit Tungsten Radiator Si Strip Detector  converts ½ through radiator d Angular Resolution Parameters Data Analysis Techniques for High Energy Physics, R. Fruhwirth et al., (Cambridge U. Press, 2000, 2 nd Edition) Track parameters (position, angles, error matrix) at a plane Propagation of parameters Multiple Scattering -- depends on energy! Propagation of parameters Predicted parameters at next plane Measurement with error New parameters at next plane Kalman Tracking/Fitting Trade-off Between A eff & PSF Source Sensitivity Photon Density Doesn't depend on  Rad ! on  Rad ! 2-Source Separation pushes for thin radiators Transient sensitivity pushes for thick radiators 12

13. Low Noise & High Efficiency Enables Trigger *using ACD veto in hardware trigger Hardware Trigger Instrument Total Rate: * First Light 3-in-a-Row Rate: ~ 8 kHz TKR trigger uses fast OR’d signals of all strips in a single plane. Coincidence formed with pair plane (x y pairs) 3 x y pairs form the Tkr Trigger  X Y X Y X Y Tungsten Conversion Foils Conversion Point SSD Planes x y pairs 3-In-A-Row 13 ~ 2-3 noise hit per readout!

14. Low Noise & High Efficiency Determines Track Length  “Missing Hits” on tracks not easily excused.  Check closeness to gaps  Check on presences of dead strips  No excuse – Track terminated at last hit false  Allows testing of track hypothesis – improves track finding accuracy by eliminating false solutions 14

15. 1/2 -1/2 Gaussian Equivalent  for a Square Distribution -1/2 1/2 Hence Actual “hits” on tracks are in general Clusters of Strips. Naively expect SSD Measurement Errors Dependence on cos(  ) 1 GeV Muons  2 Depends on Angle Large angles too narrow!! …Suspect Meas. Errors 15

16. SSD Measurement Errors SSD Layer Can move track left-right by at most 1 strip pitch! Fitted Track But… ! Suggests (!) Success!  2 Distributions – Near Text-Book! -1 < cos(  ) < 0 cos(  ) = -1 Notice the Binning Effects? = 36 = 1.05 = 22 = 1.06 16

17. There’s More: Slope Dependent Hit Errors credit: Leon Rochester You might expect delta to be uniformly distributed in each strip, so that the error on each measurement would δ Slope is in units of stripPitch/siliconHeight (Both slopes and deltas are folded around zero.) and SSD Magniified Slope  Distribution of  vs Track Slope 17

18. What’s happening? There are magic slopes… We know exactly where these tracks went. 1 2 34 5 etc. This is the factor by which the error is less than 18 Coupling of Slope error to position error finite – but - small

19. Backup to the ACD: SSD Veto  The Anti-Coincidence Detector for the LAT is a wave- shifting fiber based scintillation system. Science requirement was for ~ 10 4 : 1 rejection of entering charged particles.  Vulnerability is the accuracy of Track Finding  Near 100% efficiency of the SSDs can be used to verify the neutrality of the incoming particle  Invoking the tightest cuts on the ACD only when there were 2 - or less - “veto” SSD planes preserves efficiency 19

20. 105 MeV Gamma Count number of planes with hits inside cone Tile Energy (MeV) Dist. from Tile Edge (mm) Background Gamma Rays 01&23&4 5&6 No. of SSD Veto Planes Using SSD Vetos  Extend Track solution backwards towards ACD  For each SSD plane crossed search of hits within expanding cone  Count No. of (SSD Veto) Planes – reset counter to ZERO when hit plane encountered  Allows for looser ACD Cuts  Preserves Efficiency (~ 95%) of ACD cuts for Gamma Rays  Results in > 10 4 : 1 rejection of entering charged particles PINK: events rejected BLUE: events kept 20

21. Background Rejection via  -Rays  Electrons (positrons) produce more and more energetic “knockon” electrons (  -rays) then Cosmic Rays (protons)  This is just a result of “billiard-ball kinematics” and dE/dX’s relativistic rise  Granularity of SSDs allows the observance of excess hits around track  Adds considerably to Background Rejection 1 GeV e +  -Ray Extra SSD Hits Cosmic Rays (protons) Gamma Rays 21

22. Details for Counting  -Ray Hits Energy Dependence Counts Distributions 5 mm 10 mm 20 mm   The distribution of  - Ray counts depends on energy. Core-Hit counts becomes very useful for  -rays above ~ 300 MeV  Counts saturates at ~ 10 mm from track Cosmic Rays Gamma Rays 22 Excess Hits/Track Hits Excess Hits

23. Z=74 Tungsten e+ e- High Electric Field High Energy Gamma Ray Pair If the incident Gamma Ray is linearly polarized, the plane of the e+,e- pair shows a modulation in azimuth, , about the direction of the Gamma Ray.. Details of the LAT Conversion Telescope SUPPORT TRAY SILICON STRIP DETECTORS TUNGSTEN RADIATOR Tungsten Conversion Silicon Conversions  e+e+ e-e-  Polarization Recoil Nucleus First 12 LAT Bi-Planes Radiator = 2.8% (68% Convert Here) Silicon = 2 x.4% (20% Convert Here) Trays =.5% (12% Convert Here)  OP TOP Conversions BOTTOM Conversions Detailed Vertex Topology: Polarization? 23

24. Separation of Silicon Conversions Monte Carlo location of conversions Overall Tray Level Reconstruction The reconstruction places the start of the track in the middle of the lower SSD measuring plane or in the middle of the tungsten radiator above the upper SSD measuring plane. THIN THICK TOP BOTTOM 24

25. Separation Analysis Bottom CT (easy) TOP CT (HARD) These include the Tungsten Conversions 25 Use Classification Trees to do separation!

26. E  = 100 MeV  op =.017   MS in mrad, E  in GeV,  in rad. Len.  MS = 21.1 mrad for Silicon Conversions! (34.5 mrad for Tungsten Conversions) (E  = 100 MeV) Other Angles in the Problem All ConversionsTop Silicon Conversions 26

27. Putting It All Together … =.76 Efficiency: 294 events / 5076 events = 5.8% (Max possible: 21%) Analysis Efficiency = 27% This is probably a bit high as the average X o is >.4% … As a Polarimeter, LAT has an “analyzing power” of ~ 3% And so it will take to measure A GammaRays ( assumed = 1.0 ) to 1  27

28. Summary & Conclusions  Thin detectors optimizes PSF by minimizing multiple scattering level arms.  Low noise and near 100% efficiency  Main Instrument Trigger  SSD Vetoes  Track Validation  Fine granularity allows for a precision snapshot of the conversion and resulting tracks  Background Rejection via  -ray identification  Detailed vertex topology and hit structure leads to silicon conversion separation – Polarization?  In-flight Performance: see talk by Luca Baldini 28