FTK the online Fast Tracker for the ATLAS upgrade

Transcription

1 FTK the online Fast Tracker for the ATLAS upgrade Kostas Kordas Aristotle University of Thessaloniki Annual EESFYE mtg, University of the Aegean, Chios, April 2013

2 Overview ATLAS Trigger and DAQ FTK : the need and the idea FTK Components Timeline and strategy Performance figures of merit & the H τ τ case Symmary 2

3 ATLAS and its Trigger & DAQ (TDAQ) system 3

4 ATLAS at the LHC - pp collisions p p 4

5 ATLAS at the energy frontier interested in rare processes ~80 mb μb nb pb Design s=14 TeV >10 orders of magnitude! (>12 orders when including branching ratios to leptons, e.g. H Z Z μ+ μ- μ+ μ- )!!! Like the merchant in Alladin, the movie, says: I see you are (only) interested in the exceptionally rare 5

6 ATLAS at the energy frontier interested in rare processes ~80 mb Design : s=14 TeV GHz MHz μb khz In order to have reasonable number of observed events, we need high luminosity nb Hz 34 pb mhz >10 orders of magnitude! (>12 orders when including branching ratios to leptons, e.g. H Z Z μ+ μ- μ+ μ- )!!! 2 1 Design : L =10 cm s For 25ns bunch spacing: <μ> ~ 25 pp interactions/bunch crossing And then L = 3-5 x more 6

7 Looking at many & complex events every 25ns look at a superposition of ~25 pp collisions ~90 M channels in ATLAS, recording signals from these products 1.5 MB of info per event, i.e., every time I read this info * The Trigger and Data Acquisition system, has to watch ~1 billion pp collisions per second (40M proton bunch crossings / sec), * select online the most interesting O(400) events/sec, * and log them for offline use with a resolution of a ~90 Mpixel camera. 7

8 Triggering is the art of what you can achieve within the time and bandwidth limitations 1. The most abundant channel may be the hardest* to trigger on (e.g., H b bbar ) * Keep the interesting, while rejecting the background 2. The easiest to trigger on, may not be so popular... e.g., H Z Z μ+ μ- μ+ μ3. So, we trigger mostly on leptons and high-pt γ / jets 8

9 The job of the TDAQ Select events and get the data from the underground cavern, to the surface and then to the CERN Tier0 center for the first processing and then to Tier1 centers around the world. CASTOR: Permanent storage High Level Triggers (L2 & EF): PC farms LVL1: hardware 9

10 LVL1: decide with calo and muon dets Latency/ <proc. T> CASTOR Rates ~75 khz LVL1 2.5 s R/O Drivers Muon & Calo data p p Pipeline Memories Muon Calo Inner 40 MHz 10

11 @LVL1 accept: data to buffers, ROI list to L2 Latency/ <proc. T> CASTOR Rates L2 ReadOut System ROI list ~75 khz... LVL1 2.5 s Data fragments R/O Drivers Muon & Calo data p p R/O Buffers Pipeline Memories Muon Calo Inner 40 MHz 11

12 L2: selective asking start from ROIs Latency/ <proc. T> CASTOR Rates ~3-5 khz L2 ~40 ms ReadOut System ROI list ~75 khz ROI data... LVL1 2.5 s Data fragments R/O Drivers Muon & Calo data p p R/O Buffers Pipeline Memories Muon Calo Inner 40 MHz 12

13 @L2 accept: full event to EF... storage Latency/ <proc. T> CASTOR Data-logger Rates EF ~ Hz ~4 s Full events Event Builder ~3-5 khz L2 ~40 ms ReadOut System ROI list ~75 khz... LVL1 2.5 s Data fragments ROI data Data fragments R/O Drivers Muon & Calo data p p R/O Buffers Pipeline Memories Muon Calo Inner 40 MHz 13

14 FTK: prepare ID tracks for L2 beforehand Latency/ <proc. T> CASTOR Data-logger Rates EF ~ Hz ~4 s Full events Event Builder ~3-5 khz L2 ~40 ms... LVL1 2.5 s p R/O Buffers Tracks Data fragments FTK R/O Drivers Muon & Calo data p Data fragments ReadOut System ROI list ~75 khz ROI data +FTK tracks Pipeline Memories Muon Calo Inner 40 MHz 14

15 FTK: the need and the principle 15

16 FTK: works on Silicon Inner Detectors 16

17 Tracking is a combinatorics problem - pileup Occupancy vs Luminosity P0 Y intercept: Hard scattering! negligible compared to pileup # of ROD Hits P1 P2 SCT Layers 17

18 Tracking is a combinatorics problem - pileup Timing WH L1 jet Threshold = 20 αϖε ραγ ε οφ40 ϕε τσ GeV 3 x 1034 FTK TrigSiTrack πε ρε ϖε ντ Global Tracking PT>1 GeV 25 μsec Time per RoI 25 ms per jet x1034 Y intercept: Hard scattering! negligible compared to pileup (ω/ο ηιγ η λυµ ι οπτιµ ιζατιον) Occupancy vs Luminosity P0 P1 P Execution time ms # of ROD Hits Execution time µ σ 34 SCT Layers 18

19 Tracking is a combinatorics problem - pileup Timing L1 jet Threshold = 20 GeV WH αϖε ραγ ε οφ40 ϕε τσ 3 x 1034 FTK TrigSiTrack πε ρε ϖε ντ Global Tracking PT>1 GeV 25 Microsec Time per RoI 25 ms per jet (ω/ο ηιγ η λυµ ι οπτιµ ιζατιον) 1034 Tracking separates pileup events x10 Very helpful tool at high pileup: # of ROD Hits b and tagging Track based isolation Execution time µ σ Execution time ms MET corrections Jet vertex fraction Primary vertex reconstruction Y intercept: Hard scattering! negligible compared to pileup 34 Occupancy vs Luminosity P0 P1 P2 120 SCT Layers 19

20 FTK: the idea tracking in 2 steps 1. Find low resolution track candidates called roads. Solve most of the combinatorial problem. Roads Pattern recognition w/ Associative Memory Originally: M. Dell Orso, L. Ristori, NIM A 278, 436 (1989) 2. Then track fitting inside the roads. Thanks to 1st step, this is much easier. Excellent results with linear approximation! %7Eorso/ftk/IEEECNF2007_2 115.pdf 20

21 FTK: the idea tracking in 2 steps 1. Find low resolution Roads track candidates called roads. Solve most of the combinatorial problem. Critical parameter:pattern roadrecognition width w/ Associative Memory 2. Originally: Affects: Dell Orso, L. Ristori, NIM A 278, 436 (1989) - Number of patternsm.for given effi ciency cost - Number of fake roads workload for next step Then track fitting inside the roads. Thanks to 1st step, this is much easier. Excellent results with linear approximation! %7Eorso/ftk/IEEECNF2007_2 115.pdf 21

22 st 1 step: find the roads Find track candidates with enough Si hits O(109) prestored patterns simultaneously see the silicon hits leaving the detector at full speed. Based on the Associative Memory chip (content-addressable memory) initially developed for the CDF Silicon Vertex Trigger (SVT). 22

23 Associative Memory chips: hits fow in a prestored pattern matches, or not Pre-computed patterns stored in the Associative Memory > Flag if a pattern is matching Hits coming from the detector Sophistication added: - don't care bits (to be fexible on dead channels) - these bits are used to allow variable-width patterns narrower or wider patterns according to the conditions 23

24 st Track fitting in FPGAs: 1 stage, 8 layers Track fitting done in FPGAs. Linear approximation (instead of full helix) with pre-computed constants very fast in modern FPGAs : approx. 1 Gfits/FPGA 24

25 Track fitting in FPGAs: 2 nd stage, 12 layers Done on other FPGAs 25

26 FTK: the system components 26

27 The FTK system components & relation to rest of TDAQ Full scan Tracks pt>1gev 27

28 Data in FTK from Si RODs in dual-output card Sends SCT & pixel data to DAQ & FTK. Full scan Tracks pt>1gev 28

29 4 FTK_IM/DF do cluster finding. ATCA DF distributes clusters to 64 FTK towers. Sends SCT & pixel data to DAQ & FTK. Full scan Tracks pt>1gev 29

30 4 FTK_IM/DF do cluster finding. ATCA DF distributes clusters to 64 FTK towers. 128 PUs do pattern matching and the 1st stage track fitting. (a) AMBoard AUX: DO+TF +HW Sends SCT & pixel data to DAQ & FTK. Full scan Tracks pt>1gev 30

31 4 FTK_IM/DF do cluster finding. ATCA DF distributes clusters to 64 FTK towers. 128 PUs do pattern matching and the 1st stage track fitting. (a) AMBoard Sends SCT & pixel data to DAQ & FTK. AUX: DO+TF +HW Do full 12 layer fit Full scan Tracks pt>1gev 31

32 4 FTK_IM/DF do cluster finding. ATCA DF distributes clusters to 64 FTK towers. 128 PUs do pattern matching and the 1st stage track fitting. (a) AMBoard Sends SCT & pixel data to DAQ & FTK. FLIC sends tracks to ROS s. ATCA for global function TBD AUX: DO+TF +HW Do full 12 layer fit Full scan Tracks pt>1gev 32

33 Timeline 33

34 Timeline staged production & installation 2014: Global integration and production First half 2015: fi nal Amchip (AM06) small production 34

35 Timeline staged production & installation 2015: system will have limited coverage 2016: production and installation barrel and endcap 2017: double number of processing units for 3E34 pileup 2014: Global integration and production First half 2015: fi nal Amchip (AM06) small production 35

36 Tracking strategy- goal: ~ofine quality tracks out of the FTK into the L2 For the TDR (now) optimized HW and datafl ow within FTK Benchmark 3E34 inst. lumi, 80 pileup events Datafl ow requirements guide tracking choices Pattern recognition 7/8 layers (1-miss) Effi ciency and fake roads tuned against HW size 8-layers track fi t (1-miss) Early rejection of fake roads Only few tracks reach extrapolation step 12-layers track fi t (2-miss) Extrapolation based on 8-layer tracks Allow additional miss (2 missing max) Nearly offl ine quality tracks 36

37 Performance 37

38 Important: Performance figures Effi ciency: % of offl ine tracks found by the FTK Fakes: Resolution of FTK tracks % of FTK tracks not found by offl ine Offl ine FTK Single track efficiency from FTK TP ~5-10% lower than ofine EDMS:ATL-D-ES

39 Important: Performance figures Effi ciency: % of offl ine tracks found by the FTK Fakes: Resolution of FTK tracks Much closer to ofine! % of FTK tracks not found by offl ine FTK effi ciency (TDRpreliminary) Offl ine FTK Single track efficiency from FTK TP ~5-10% lower than ofine EDMS:ATL-D-ES

40 Single-track parameters (ofine, FTK) FTK produces ~ofine-quality tracks in 10's of microsecs! BL 036 I no S-0 P T D-E K FT TL om S:A r F M ED 40 40

41 H τ τ benchmark channel Require 1 (2 or 3) tracks in the signal cone for 1 (3) prong s and no tracks with PT >1.5 GeV/c in the isolation cone. offl ine FTK 1-prong offl ine FTK 3-prong FTK & offl ine have ~10-3 jet fake probability. 41

42 Summary FTK performs full scan at L1 output rate (~100 khz) pt>1gev, 10/12 silicon layers Schedule 2014: Global integration 2015: Data taking with limited coverage 2016: coverage extended to full barrel and endcap Tracking performance close to offl ine FTK TDR being fi nalized. Will include: Single track and physics objects performance H τ τ physics case (more in Sep. '13 TDAQ TDR) 42

43 Thank you! 43

44 44