Alberto Annovi for the Fast Tracker collaboration Istituto Nazionale di Fisica Nucleare
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Transcript of Alberto Annovi for the Fast Tracker collaboration Istituto Nazionale di Fisica Nucleare
Alberto Annovifor the Fast Tracker collaboration
Istituto Nazionale di Fisica NucleareLaboratori Nazionali di Frascati
A hardware track finder for the ATLAS trigger.
Fast Tracker
11th ICATPP Conference onAstroparticle, Particle, Space Physics,
Detectors and Medical Physics Applications
11th ICATPP, October 7th, 2009 Alberto Annovi 2
Outline
• The Fast Tracker for ATLAS level 2
• Physics motivations
• Fast Tracker internals
• Fast Tracker performances
• A pixel clustering algorithm for FTK
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107
105
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10
10-1
10-3
σ Even
ts/s
w
ith L
= 1
034 c
m-2
s-1
TRIGGER @ HADRON COLLIDERS
Hard Life!
Pile-up:
CDF: ~7 events @3 1032 cm-2
396 ns interbunch
LHC:~25 events @1034 cm-2s-1
25 ns interbunchA few hundreds events at SLHC
p
event + underlying event + pile-up
p
S1/2 (TeV)RARE
EVENTS
B charmless decays
B0->K
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30 minimum bias events + H->ZZ->4
Tracks with Pt>2 GeV
Where is the Higgs?
FTK
30 minimum bias events + H->ZZ->4
Tracks with Pt>2 GeV
Where is the Higgs?
Help!
What does FTK? Find tracks pT>1 or 2 GeV
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SVT
Fast Track (FTK)
LHC
Builds on the Silicon Vertex Trigger experience
For SVT see G. Punzi plenary talk (Monday)
Fast tracking in pixel and SCT det.
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Total # of readout channels:PIXELS: 80 millionsSCT: 6 millions
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Where could we insert FTK?
PIPELINE
LVL1LVL1
CALO MUON TRACKERCALO MUON TRACKER
BufferMemory
ROD
BufferMemory
FEFE
Raw dataROBs
2nd output
1st output
Fast Track(Road Finder+GigaFitter)
Fast Track(Road Finder+GigaFitter)
Track dataROB
Track dataROB
high-qualitytracks:Pt>1 GeV
Event rate up to 100 kHz
Very low impact on DAQ
No changeto LVL2
Fast network connectionFast network connection
CPU FARM (LVL2 Algorithms)CPU FARM (LVL2 Algorithms)
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• R&D Proposal to work on TDR: approved Feb 2008• Preparing the TDR (2009) to be approved 2010 for SLHC Phase I
• Expected inst. luminosity: 1034 – 3*1034 (and 1035 later on) • staging is being considered to take data with FTK also before the Phase I shutdown:
•it is very important to learn at lower luminosities before going up to SLHC •early impact on physics : lepton isolation, b-tagging and tau-tagging studies
(Zbb; Ztau tau)
Argonne National Lab : J. Proudfoot and J. ZhangUniv. of Chicago : A. Boveia, E. Brubaker, F. Canelli, M. Dunford, A. Kapliy, Y.K. Kim, C. Melachrinos, M. Shochet, and J. TuggleUniv. and INFN Ferrara : L. TripiccioneINFN Frascati : A. Annovi, M. Beretta, and P. LaurelliUniv. of Illinois at Urbana-Champaign : H. DeBerg, A. McCarn, M. Neubauer, and S. WolinHarvard Univ. : M. Franklin, C. Mills, and M. MoriiUniv. and INFN of Pisa : E. Bossini, V. Cavasinni, F. Crescioli, M. Dell’Orso, P. Giannetti, M. Piendibene, G. Punzi, F. Sarri, I. Vivarelli, G. Volpi, and L. SartoriWaseda University : N. Kimura and K. Yorita
FTK collaboration & schedule
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Physics motivations
B-tagging: FTK vs offline
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Measuring the Z0 bbbar for:• b-jet calibration• improve top mass resolution• bbbar resonances (e.g. Higgs)
Z0 bbbar for b-jet calibration
I.Vivarelli et al.
(No-pile-up)
ATL-PHYS-PUB-2006-006
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Higher efficiency for bbH/A 4 b-jets
(not
90%
eff
ic. f
or t
rue
Pt)
“w/o FTK”: assumes that level-2 execution time limits level-1 jet rate to a few hundred Hz., e.g. only jet threshold sharpening at level 2 & no b-tagging.
w/ FTK
w/o FTK
4-Jet Trigger Rate @2x104-Jet Trigger Rate @2x103333
Careful study of the 4-jet L1 trigger cross sections:
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– At lower mass, the signal is badly sculpted by the non-FTK jet threshold
– Larger discovery region with FTK
Signal dijet mass distribution at the trigger
Higher efficiency for bbH/A 4 b-jets
e/ isolation @ high luminosity
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Lepton identification: primary vertices fast identification Isolation with tracks of Pt>Threshold and from right vertex
z
Calorimetric isolation suffers from high event multiplicity!
Isolation with tracks has little sensitivity to pile-up events it becomes easy with FTK
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Tracking in 2 steps
Roads1. Find low resolution
track candidates called “roads”. Solve most of the combinatorial problem.
2. Then track fitting inside roads.Thanks to 1st step it is much easier.Excellent results with linear approximation!
Pattern recognition w/ Associative Memory
http://www.pi.infn.it/%7Eorso/ftk/IEEECNF2007_2115.pdf
IEEE Trans. On Nucl. Sci., vol. 53, pp. 2428-2433 (2006)
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The Event
...
The Pattern Bank
Pattern matching
Associative Memory (AM)see L. Sartori talk Tuesday
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1/2
AM
1/2
AM
Divide into sectors
6 buses 40MHz/bus (to be increased)
ATLAS Pixels + SCT
Feeding FTK @ 100kHz event rate
Pixel barrel SCT barrel Pixel disks
11 Logical Layers: full coverage
• 8 regions each with• 6 sub-regions ( towers)
• ~25o, ~1.7 • bandwidth for up to 3*10E34 cm-2s-1
Allow a small overlapfor full efficiency
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Track dataROB
Track dataROB
Raw dataROBs
~Offline quality Track parameters
…
HITS
Inside Fast-TrackPixels & SCT
DataFormatter
(DF)50~100 KHzevent rate
RODsRODscluster findingsplit by layer
cluster findingsplit by layer
overlap regions
overlap regions
Remove duplicate trks
S-links
Alberto Annovi
DataOrganizer
AM brd
Track Fitter
DataOrganizer
AM brd
Track Fitter
6x towers
The pattern bank for pattern matching
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Pattern bank size strongly depends on superstrip size.It is a compromise between coarser superstrips = fewer patterns but many more fits
Sing
le m
uon
effici
ency
>100M patterns/region with AMchip + tree search processor (TSP)
50M patterns/region with future AMchip
90%
88%
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Bank size (M patterns/region)
4M patterns/region use current AMchip
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1. Two smaller FTKs in cascade (a` la CDF): 8 SCT layers fitted first, then 3 pixels layer + SCT fitted segment
2. Two Half-SS shifted banks used in parallel to improve SS resolution
1. New algorithm inserted between the AM and the TF: The Tree Search Processor (TSP) - NIM A287 (1990) 436-438
http://www.pi.infn.it/~paola/Tree_search_algorithm.pdf
SS lsb-bit=0SS lsb-bit=1
Same SS #
Blue is SSYellow is SS-1
Blue is SS+1, Yellow is SS.
2 roads found by blue AM
1 road found by yellow AM
Comparing the 2 outputs andrejecting the roads not found byboth AM banks, cleaner output
Different SS definitions on each layer
FTK #1(as XFT @CDF)Find SCT segments
FTK # 2(as SVT @CDF)links pixel hits
To SCT segments
,pt,z,d ofSCT segments
CDF
XFT in COT
SVX
Studying different architectures: optimize AM useRedundancy for high luminosity
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Algorithm: NIM A287 (1990) 436-438 http://www.pi.infn.it/~paola/Tree_search_algorithm.pdfTree Search Processor: NIM A 287, 431 (1990), http://www.pi.infn.it/~orso/ftk/NIMA287_431.pdf
IEEE Toronto, Canada, November 8-14 1998 http://www.pi.infn.it/~paola/TSP_v14.pdf
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3 4
THIN ROAD
FAT ROADFound by AM (default SS for example or even larger)
1 2 3 4
5 6 7 8
Depth 0
Depth 1
Depth 2
PATTERN
BLOCK
PARENTPATTERN
Advantages: pattern bank saved in dense RAMs high degree of parallelism
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How much workload for the GF?
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Full simulation: WH events @1034 cm-1 s-1
Million of fits
<fits/event> ~ 200kBarrel onlyRegion 0
http:
//w
ww
.pi.i
nfn.
it/%
7Eor
so/ft
k/IE
EECN
F200
7_21
15.p
dfGF upgrade for SVT: 1 fit/nswith a Xilinx Virtex 5 FPGA(XC5VSX95T)
Doable with a few FPGAs/region
AMchip : 3.8M patterns/region+ TSP : 108M patterns/region
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A hardware architecture able to digest WH lnubb + 1034 pileup @ 75kHz event rate
SUPERBins
DO + TF
HIT
s
ROADS
12 L
SUPERBins
DO + TF
HIT
s
ROADS
12 L
….….……..
Tower 0 Tower 5
TRACKS TRACKS
TRACKS MERGING + HW
Tracks to Level 2
1000 hit/ev/layer corrected for overlaps between region→ 1000/3=330/ev. Dividing in 6 towers (with 100% contingency for tower overlap)→ 330 * 75 kHz = 25 MHz OK even for current AMchip!
~6500 <Roads>/ev → 3600 for RW reduction → 600/ev if divided in 6 engines→ 600 * 75 kHz = 45 MHz →1 Road each 22 ns
400 k <Fits>/ev. → 66 k <Fits>/ev. in 6 engines→ 66 k * 75 kHz → 5 G<fits>/s → 5 fit/ns
1 FTK -region : 6 towers
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CUSTOMBACKPLANE
Hit
Warr
ior
FTK INPUT
12 AM boards
Today technology:3.8 106 Patterns today
Amchip100M patterns TSP
For 2014(?) installation:O(50 106 ) Patterns for 90 nm ??Giga patterns future TSP
DO
+TF
-1
AM
2+
TSP
AM
3+
TSP
DO
+TF
-2
AM
4+
TSP
AM
5+
TSP
DO
+TF
-3
AM
6+
TSP
AM
7+
TSP
DO
+TF
-4
AM
8+
TSP
AM
9+
TSP
DO
+TF
-0
AM
0+
TSP
AM
1+
TSP
DO
+TF
-5
AM
10+
TS
P
AM
11+
TS
PC
PU
vm
eFTK
Outputtracks
All found tracks
8x core crate layout (TSP option)
1 tower
Alberto Annovi
Tracking quality on single muon events
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• We compare FTK-reconstructed tracks with an offline algorithm (IPAT)
• Resolutions are wrt all truth tracks with pt > 1 GeV and |η| < 2.5
• Performances are comparable
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November 2008: nopile-up – full coverage raw hits
Single muons
0 2 4 6 8 10 12 14 16 PT [GeV]
FTK proposal: no pile-up barrel only – space points
WH 10**34 pile-up
Alberto Annovi
σFTK = σoffline 30μm⊕
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RO
RI
RSle
adin
g P T
tr
ack
Jet a
xis
|| < 0.8 for jet1033 Lumi
Fakes as a function of jet Fakes as a function of jet Pt
iPat Tracks
FTK Sim Tracks
-jet efficiency rejection
SingleProng (1,0)
Alberto Annovi
Efficiency vs. Efficiency vs. pT
end end
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d
phi
d
SVT: Online beamline fit & correction
Subtracted
Raw
x
y
d
<d> = Ybeamcos – Xbeamsin
L. Ristori et al.
Also used to monitor beam profile.Useful information for accelerator people! htt
p://
ww
w-c
df.fn
al.g
ov/c
dfno
tes/
cdf7
208_
bw_o
nlin
e.ps
Nuc
l.Ins
trum
.Met
h.A5
18:5
32-5
36,2
004
end end
Pixel clustering for the Fast TracKer• Pixel clustering device for the ATLAS FastTracKer processor
– 1st application & design motivationhttp://twiki.cern.ch/twiki/bin/view/Atlas/FastTracker
• Main challenge: input rate 160Gibts– 132 S-link fibers from all pixel RODs
• Running at 1.2 Gbits (total 160Gbits) • 32bit words at 40MHz, 1 hit/word
– Use hits at 40MHz as benchmark• Focus on clustering quality for level-2
• Illustrate a general clustering strategyLevel-2
Event buffers
Fast TracKerinput stage
PixelDetector
clusteringdevice
50~100 kHzevent rate
Detectorinterface
Detectorinterface
132S-links FTK reconstructs
tracks for level 2
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The problem
• Clustering is a 2D problem1. Associate hits from same cluster
– Loop over hit list– Time increases with occoupancy &
instantneous luminosty– Non linear execution time
2. Calculate cluster properties – e.g. center, size, shape …
• Goal: execution time linear with number of hits– Not a limiting factor even at
highest inst. Luminosity
3 9 7
1 13 15
4 8 6 11
12
2 5 10 14
11 22 33 44 55 66 77 88 99 1010
11th ICATPP, October 7th, 2009 Alberto Annovi
The algorithm working principleFPGA replica of pixel matrix
Eta direction -->
Pixel module is a 328x144 matrix.Replicate it in a hardware matrix.The matrix identifies hits in the same
cluster (local connections).
Load allmodule hits
Loop
ove
r eve
nts
and
pix
el m
odul
es
select left most
top most hit
propagate selection
through cluster
read out cluster
Loop
ove
r clu
ster
s in
a m
odul
e
Averagecalculator
out
Core logic:Hit associated into clusters
high level cluster analysis
high level cluster analysis
2nd pipeline stage
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end end
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Clustering by 328x8 slices?Module data
Eta direction -->
Shift of hits comes for free (no extra time)! Just use the slice as a circular bufferin the eta direction. Then hits are shifted by redefining the first column.
SLIDING WINDOW: with one xc5vlx155 process one S-LinkImplement 2 processing matrixes. Process hits at 40MHz rate.
11th ICATPP, October 7th, 2009 Alberto Annovi
Fill 328x8 slicelike this
Read out 1st cluster
Read out 2nd cluster
And so on
Fill 328x8 slicelike thisModule data
Eta direction -->
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Two priority logic chains
hitpixelcell
select
hitpixelcell
select
hit
hit
X 328 pixels in a column and 144 columns
This logic selects the top most pixel.Similar logic to select the left most column with a hit.
select
a 1st priority logic- needed to select first hit
a 2nd priority logic- needed to readout selected hits (cluster)- position from address bus
11th ICATPP, October 7th, 2009 Alberto Annovi
hit
sel
sel
hitControllogic
Controllogic
The elementary cell
clk
clk
ROW SEL
COLUMN SEL
AND
Combinatorial logic
Combinatorial logic
8
1st neighborhoodIS_SELECTED
WRITE
IS_HIT
IS_SELECTED
SEL HIT
SEL FORREADOUT
3 STATES (2 FLIP-FLOPS):IS_EMPTYIS_HITIS_SELECTED
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Cluster definition:Contiguous hits along side or cornerFlexibility to redefine it
Conclusions• FTK performs global track reconstruction at Level-1 trigger rate
• Using massively parallel Associative Memories, FTK will provide a complete list of 3D tracks at the beginning of Level-2 processing
• Time saved by FTK can be used in Level-2 for more advanced algorithms– Bonus: access to tracks outside Regions of Interest
• FTK easily integrates with current ATLAS DAQ
• Builds on success of Silicon Vertex Trigger (SVT) at CDF
• More info: http://www.pi.infn.it/~orso/ftk/https://twiki.cern.ch/twiki/bin/view/Atlas/FastTracker
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Thanks for your attention
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xi
Non-linear geometrical constraint for a circle:
F(x1 , x2 , x3 , …) = 0
But for sufficiently small displacements:
F(x1 , x2 , x3 , …) ~ a0 + a1x1 + a2x2 + a3x3 + … = 0
with constant ai(first order expansion of F)
From non-linear to linear constraints
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Constraint surface
14 measured coordinates: x1 … x14
5 parameters to fit : , d0, pT, , z0
9 constraints
Linear approximation is good within any given set of physical modules.
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• Discovery reach (FTK double b-tag 25 Hz L2 output)
• Note that FTK is rather insensitive to a much higher background rate due to either higher luminosity or MC reality.