Where to add FTK?

(1)

Fast Track + few

(Road Finder) CPUs Fast Track + few

(Road Finder) CPUs

ROBROB

offline quality Tracks:

Pt >1 GeV

Ev/sec = 50~100 kHz

Very low impact on DAQ

PIPELINE

LVL1LVL1

Fast network connection Fast network connection

CPU FARM CPU FARM

CALO MUON TRACKER CALO MUON TRACKER

Buffer Memory

ROD

Buffer

Memory ROB

L2 Algorithms

FE FE

Where to add FTK?

(2)

resolutionFull hits

resolutionLow super bins

Tracking in 2 steps: find Roads, then find Tracks inside Roads

Road Super Bin

Road = Pattern

Road

(3)

AM = BINGO PLAYERS

HIT # 1447

PATTERN N PATTERN 1 PATTERN 2

PATTERN 3

PATTERN 5 PATTERN 4

Dedicated device - maximum parallelism:

• Each pattern with private comparator

• Road search during detector readout

The Event

The Pattern Bank^...

TRACKING WITH PATTERN MATCHING

AM the Associative Memory

Bingo scorecard

(4)

ASSOCIATIVE MEMORY:

CHIP ARCHITECTURE

ONE PATTERN

FF FF FF FF

word word word word

Layer 1 Layer 2 Layer 3 Layer 4

HIT

Patt 0

Patt 1 Patt 2 Patt 3

Output Bus

HIT HIT HIT

(5)

1/2

 A M

1/2

 A M

Divide into

 sectors

6 buses 6 logical layers

2 sectors ATLAS Pixel + SCT

Feeding FTK @ 50KHz event rate

40MHz/bus

Pixels barrel SCT barrel Pixels disks

6 Logical Layers: full  coverage

~70MHz cluster/layer

(Low Luminosity, 50KHz ev.)

ATLAS-TDR-11

(6)

20 9U VME boards – 3 types

SUPER BINS DATA

ORGANIZER

ROADS ROADS + HITS

EVENT # N

PIPELINED AM

HITS

FASTRACK

BUFFER MEMORY BUFFER

MEMORY

Front End Tracker

DO-board

EVENT # 1

AM-board

50~100 KHz event rate

GB

Few CPUs

Offline quality Track parameters

(7)

The AM board

(8)

13^th Real Time Conference 2003 Alberto Annovi

The FTK Performances

AM-B7 AM-B8

AM-B1AM-B0DO5DO4DO3DO2DO1DO0

CUSTOM BACKPLANE

Ghost Buster

FTK INPUT

CPU0 CPU1

O(50 10

⁶

) patterns

AM-B2 AM-B3

CPU2 CPU3 AM-B4 AM-B5 AM-B6

• Offline quality tracking

• 50 KHz event (Low luminosity)

• 2 crates (4 to include stereo layers)

• Connection to experiment not included

(9)

• Track confined to a road, fit is simple

• Linear expansion in the hit positions x_i:

– ^ = _k (c_ik x_i)²  final cut

– d = d₀+a_i x_i  = ₀+ b_i x_i P_t = …

• Fit reduces to a few scalar products  fast

• Constants from detector geometry

– Calculate in advance

– Correction of mechanical alignments via linear algorithm

• fast and stable

• A tough problem made easy !

(10)

Is 2

^nd

step  offline?

Track parameter residuals:

(d

₀

) = 17 m



^

/N

ATLAS Genova: M. Cervetto, P. Morettini, F. Parodi, C. Schiavi, presented on 20-

Nov-2002 at PESA

• Track finding within a road is fast

• Fitting in linear approximation

• Testing the linear fit with a fast

simulation of ATLAS Silicon Tracker

(11)

SVT TDR ’96

Impact parameter SVT simulated on real data

superimposed to real offline

SVT just started Real data

CDF run 127844 No alignment corrections

 ~ 45 m

 ~ 48 m

(12)

b



b

b 



e 

 h b b

How to use Fast-Track to capture as much PHYSICS as possible

FTK



Hard life for all LVL2 objects!

(13)

D^,D_s

D⁰

D⁰KK

B^D⁰

Bhh B_sD_s^*

(14)

Jets &

b-jets

(15)

Z0 bb

bbH/A bbbb tt qqqq-bb ttH qqqq-bbbb

H/A tt qqqq-bb H hh bbbb

H^+- tb qqbb

ATL-DAQ-2000-033

Offline-quality b-tagging for events rich of b-quarks

with FASTRACK offline b-tag

performances @LVL2

ATLAS TP 31/3/2000

0.6 100

10 1000

_b R_u

Calibration sample

(16)

“

ET200 +

j70 + j50 + j15

(^||<2.5)

“

6 + j25 + j10 (||<2.5)

ATLAS + FTK

“ 4

“ 2.6

5 b-jet 237

Inclusive b-jet

CMS ATL-COM-DAQ-2002-022Sources: CMS TDR 6 & F. Gianotti, LHCC, 01/07/2002

1 3b leading

4 3 b-tags

50 mini event 2 b-jets +

Mbb > 50

160 mini ev.

2 b-tags + Mbb > 50 0.2

0.2 0.2 j200

3j90 4j65

Triggers w/o and with FTK

Scenario: L= 2 x 10³³ deferral

25 j400

3j165 4j110

20 40

210

0.8

20 0.2

26

HLT rate HLT (Hz)

selection LVL1

rate (KHz) LVL1

selection

ATLAS

(17)

Events

Z  b-bbar

Important b-jet calibration tool

M_bb(GeV)

CDF Run II (with SVT)

(S/B = 35)

Cdf/anal/top/cdfr/4158

Events

M_bb(GeV)

2fb^-1

ATLAS + FTK 20fb

^-1

Problem: high L3 output rate

ATL-COM-DAQ-2002-022

Use mini events:

save FTK tracks & hits instead of full tracker data

 20 Mbb > 50

3j + ET200

 60 Mbb > 50

soft + 2j

LVL2

S/B

LVL1

(18)

bbH/A  bbbb

Analysis:

4 b-jets|^j|<2.5

P_T^j> 70, 50, 30, 30 GeVefficiency 10%

ATLAS-TDR-15 (1999)

Effect of jet P_T cuts is even worse with deferralsM_A(Gev)

tan

200

FTK triggers

LVL1 LVL2 Effic.

soft + 2j 3 b-tag 8%

3j + E_T200 3b leading 13%

(19)

Electron Identification

Swapping trigger algorithms can reduce trigger rate while increasing efficiency!

CERN/LHCC/2000-17

With FTK tracks are ready on the

shelf: using tracks is even faster than using calorimeter raw data!

Efficiency & jet rejection could be enhanced by using tracks before calorimeters.

L2 tracking

High Quality tracking

(20)

L=2x10³³cm^-2sec^-¹

HLT  selection @ CMS

H(200,500 GeV)   1,3h^±+ X)

0.4 0.5 0.6 0.7 0.8 0.9 1.

0 0.02 0.06 0.1 0.14  (QCD 50-170 GeV)

 (H(200,500 GeV)   1,3h+X)

m_H=500 m_H=200

TRK tau on first calo jets Pix tau on first calo jet Staged-Pix tau on

first calo jet

TRK tau on both calo jets Calo tau on first jet

CERN/LHCC 02-26 CMS TDR 6 December 15, 2002

Efficiency & jet rejection could be enhanced by using tracks before calorimeters.

0.007 0.004

(21)

 Trigger determina le misure di fisica al pari del rivelatore

 Oggi l’uso delle tracce nel trigger e` limitato dalla potenza di calcolo necessaria

 Pisa ha inventato un processore dedicato che permette di usare i tracciatori nel trigger al pari degli altri tipi di rivelatori

 b-jet tagging e -jet tagging sono possibili a frequenze di ~ 50 KHz e

alta efficienza

^→

piu` Higgs su nastro!

Conclusioni

(22)

Backup slides

(23)

Thin Road Width: pix 1mm x 6.5cm Si 3mm x 12.5cm Medium Road Width: pix 2mm x 6.5cm Si 5mm x 12.5cm Large Road Width: pix 5mm x 6.4cm Si 10mm x 12.5cm

ATLAS Barrel (~CERN/LHCC97-16)

7 layers: 3 Pixel + 4 micro-strip (no stereo)

Cylindrical Luminosity Region: R = 1mm, z = ±15cm Generate tracks (Pt>1 GeV) & store NEW patterns

1/4

BARREL

10M patterns

(24)

(25)

ATLAS configuration:

12 detector layers – 5

^•

10

⁵

SB/layer 128 chips/board PQ208 die: 16.3

²

mm

²

What chips do we have now ?

Config. Technology Status Density

(patt/chip)

CDF old full custom on-line 128

CDF old FPGA working 64

CDF current FPGA ~ designed 1000

~ATLAS old FPGA under test 32

ATLAS stand. cell 0.18 estimate ^(now) 11000 ATLAS stand. cell 0.1 estimate ⁽¹⁹⁹⁹⁾ 40000

International Technology Roadmap for Semiconductor 1998

2005: patt / 9U-board

XCS40XL (.13) 64x10³ Virtex (.1) 330x10³ Stand. Cell (.1) 5000x10³

(26)

Standalone program to produce hits from tracks, it includes:

• multiple scattering

• ionization energy losses

• detector inefficiencies

• resolution smearing

• primary vertex smearing: _xy=1mm _z=6cm

Detector hits generated from (Pythia):

• QCD10 sample: QCD Pt>10 GeV

all samples + noise + <5 MB>.

Road finding 6 layers/7 (FTK simulation)

(27)

resolutionFull hits

resolutionLow super bins

Tracking in 2 steps: find Roads, then find Tracks inside Roads

Road Super Bin

Road = Pattern

Road

(28)

Nfits





_x



13 comb x 34 roads = ~440 comb/track 1.4 comb x 4 roads = 6 comb/track QCD Pt10 2.3 comb x 6 roads = 14 comb/track QCD Pt40 7.8 comb x 9.5 roads = 74 comb/track QCD Pt100 27 comb x 25 roads = 658 comb/track QCD Pt200

thin

thin large

large

(29)

Pt 200 Pt 100 Pt 40 Pt 10

Step 2: Software Linear Fit

Ncomb /trk

658 74

14 6

Ntrk /ev

17 16 10 8

L1 Trig jet jet soft jet

soft 

L1 Rate 200Hz

<2KHz

~5KHz

~20KHz

Fits/sec 2.2MHz

<3MHz 750kHz 1.5MHz

<8MHz

Full 3D fit

fit/s 0.6 MHz

2D Fit

fit/s 2.2 MHz PIII 800MHz

2.5D Fit

fit/s 1.1 MHz

Htt 4400 fit/ev <latency> = 1ms

max latency = 100ms Pt 200 11200 fit/ev. <latency> = 3ms

only 8 CPUs (barrel)

Latency Test

Nfit /ev

11186 1184

140 48

(30)

More examples

qqH, VH  bb+njets

L1: hadron L2: Mbb50

qqH WH ttH Z⁰H Efficiency % ~25 ~25 ~ 90 ~35

ttH  bbbb+njets, H+Z0  bb+bb

L1: hadron L2: 3-b

(31)

Pythia vs CDF RUN I data

Physics background Pythia Xsec

study sample Data Xsec

pp bbbbbb 4 bjet+X 30 pp VH bbqq 2bjet+2jet 10 tt bbqqqq 2bjet+4jet 15

Multijet QCD background

Shower Monte Carlos expected to underestimate data cross-sections^.

IN CDF WE OBSERVE THE OPPOSITE ?

We are studying this background:

Different Monte Carlos:

1) Herwig vs Pythia

2) matrix element calculations vs shower Monte Carlos

(32)

Hqq+HV 10 pb

6 + hadron

0.1 pb on tape

2000 H/year Hbb+Htt 1 pb

6 + hadron

0.05 pb on tape

1000 H/year gg H 30 pb

6

1 pb on tape

20000 H/year ^^bb

bb

 _bb

qq

bbbbqqqq

bbqq

bb

 _bbbb

bbH/Abbbb CDF

ATLAS

(33)

20 9U VME boards – 3 types

SUPER BINS DATA

ORGANIZER

ROADS ROADS + HITS

EVENT # N

PIPELINED AM

HITS

FASTRACK

BUFFER MEMORY BUFFER

MEMORY

Front End Tracker

DO-board

EVENT # 1

AM-board

50~100 KHz event rate

GB

Few CPUs

Offline quality Track parameters

(34)

The AM board

(35)

• Track confined to a road, fit is simple

• Linear expansion in the hit positions x_i:

– ^ = _k (c_ik x_i)²  final cut

– d = d₀+a_i x_i  = ₀+ b_i x_i P_t = …

• Fit reduces to a few scalar products  fast

• Constants from detector geometry

– Calculate in advance

– Correction of mechanical alignments via linear algorithm

• fast and stable

• A tough problem made easy !

(36)

AM input bandwidth = 40 MHz cluster/bus AM input buses = 6

<cluster/event> cluster rate

Pix 0 1300 65 MHz

Pix 2 + extra 1500 75 MHz SC0 + extra 1140 57 MHz SC1 + extra 1300 65 MHz

SC2 + extra 1160 58 MHz

SC3 + extra 1500 75 MHz

Ev/sec

50KHz

2 AM partitions

for the whole Pix+Si tracker More partitions as a backup option Less partitions Less hardware

conservative estimates from inner-detector & pixel TDRs

(37)

# of channles 16 10⁶ Occupancy at High lum. 4.4 10^-4

<# of hits/event> 7000 Divide by 5 for low lum. 1400 Add Hard Scattering (3MB) 2250 Add noise (10^-5) 2400 Divide by 2 (<hit/cluster>) 1200 Multiply by 50KHz 60MHz

conservative estimates from inner-detector & pixel TDRs

Rate of cluster in Pix0

(38)

SVT TDR ’96

Impact parameter SVT simulated on real data

superimposed to real offline

SVT just started Real data

CDF run 127844 No alignment corrections

 ~ 45 m

 ~ 48 m

(39)

D^,D_s

D⁰

D⁰KK

B^D⁰

Bhh B_sD_s^*

(40)

• The natural implementation of the linear fit is coupled with hits selection made by

dedicated hardware (Pisa group proposal).

But could be also an important tool for the online software selection.

• The importance of the size of (assumed) linearity region has been studied in the cases:

– Large region (₀</6, ||<0.5, |z₀|<10cm): the detector geometry gives the dominant

contribution to track resolution ((d₀) = 90

m).

– Smallest region (each possible pattern of modules has different tuning): good results ((d₀) = 17 m), but big effort is requested to tune all the detector. The memory needed in this case could be large: N possible patterns X 95 tuning constants (considering six layers) X 4 bytes (variables in float precision).

Conclusions

(by M.Cervetto on linear fit)

(41)

Calorimet. LVL2 algorithm for Tau selection Efficiency for H vs. output rate

CMS-IN 2000-033

Tau Identification

(42)

Composition of LVL1 soft  sample

26% of the events have no b-quarks inside 74% of the events have at least a b-jet:

13% direct production 27.5% flavor excitation 33.3% g splitting

23% of the events have at least 2 b-jet:

13% direct production 3% flavor excitation 7% g splitting

===========================================

no-btagging:

mjj>70 GeV Rate=1.2 ± 15% kHz double b-tagging:

mbb>70 GeV Rate=110 Hz

(43)

Level 2 rates: Pythia+ATLfast

Ideal: _b=100% _c =0% _u,d =0%

Real: _b=60% _c =10% _u,d =1%

Mis-tag: _b=100% _c =10% _u,d =1%

(44)

ATL-DAQ-99-014

# RODS # RODS 360^O in 180^O in 

Pix 0 36 18

Pix 2 32 16

Pixdisk 16 8

SC0-3 44 22

SCdisk 48 24

TOT 176 88

FTK inputs 24

(45)

Now: CDF-like configuration: 0.45 G

bit

/s 6 layers - 48000 250 wide SB/Layer

•

full custom (.7)

- 128 patt/chip

- 16x10

³

patt/9U board

•

XCS30XL (.35

) - 128 patt/chip - 16x10

³

patt/9U board

•

XC2S200E (0. 186 lay) 50 euro/chip

- 300 patt/chip

- 38x10

³

patt/9U board

• XC2V1000 (0.158 lay – 0.12 transistors

) - 1200 patt/chip

- 153x10

³

patt/9Uboard

• EP1C20F324C8 (0.1350 euro/chip



- 1100 patt/chip

- 141x10

³

patt/9Uboard

• Stand.Cell (.35)

- 1000 patt/40 mm

²

•

Stand.Cell (.18)

- 4000 patt/40 mm

²

• Stand.Cell (.13)

- 16000 patt/40 mm

²

The Associative Memory CHIP

128 chips/board PQ208 (die:16.3

²

mm

²

)

(46)

2005: LHC-like configuration: 4.G

bit

/s 12 layers - 500000 SB/Layer

•

XCS40XL (.13

) - 64x10

³

patt/9U board

•

Virtex (.1)

-330x10

³

patt/9U board

•

Stand.Cell (.1)

- 5x10

⁶

patt/9U board

International Technology Roadmap for Semiconductor 1998

CDF AM = 400 k pat.  4 milioni di pat.

XC2S200E55 $/chip;14000 chips: 1.4 GL XC2V1000 200$/chip; 3500 chips: 1.4 GL Standard Cell  1000 chips; 200 + 200 ML