High Level Trigger

HLT Collaboration (20 Apr 2023) 1

High Level Trigger

L0L0L1L1L2L2HLTHLT

Dieter RoehrichUiB

• Trigger• Accept/reject events

• Select • Select regions of interest within

an event

• Compress• Reduce the amount of data

required to encode the event as far as possible without loosing physics information

• Provide HLT-ESDs for online monitoring• Access to the results of the event

reconstruction

• Physics Requirements


Physics Applications

• Quarkonium spectroscopy • Dielectrons• Dimuons

• Open Charm• Jets • Pileup removal in pp

Detectors

DAQ HLT

Mass storage


Quarkonium• Dielectrons

– HLT task• Reject fake TRD triggers and reduce trigger rate by factor of more than 10

– Status• Fast TPC pattern recognition – done• Additional PID by dE/dx – done• Adaption of Kalman filter for HLT – done• Combined track fit TRD-TPC-ITS – in progess

– To do• Emulate the TRD Global Tracking Unit

(TRD tracklet merging and PID)

• Dimuons– HLT task

– Utilizing tracking chamber information and improving momentum resolution

– Sharpening of pt-cut– Rejection factors: low pt-cut: 5, high pt-cut: 100

– Status– Complete simulation including cluster finder – done– Full scale prototype HLT farm (UCT) – done– FPGA cluster finder – in progress– FPGA interface – in progress

T. Vik, PhD thesis, Oslo, 2005


Open charm

• HLT task– Detection of hadronic charm decays: D0 K– + +

– About 1 D0 per event (central Pb-Pb) in ALICE acceptance

– After cuts

» signal/event = 0.001

» background/event = 0.01

• Status– Detailed study of timing profile of offline algorithm - done

– Adaption of ITS tracking to HLT and speed-up – done

– Optimization of D0 finder – in progress

– Combine HLT tracking and D0 algorithm – in progress

• To do– estimate the efficiency for appling D0-offline-cuts online

– extend study to D+, D*+


Online • Available modules

• TPC cluster finder (CF)

• TPC track follower (TF)

• Kalman fitter

• TPC Hough transform tracker (1)

• TPC Hough transform tracker (2)

• TPC cluster deconvolution

• TPC performance monitor

• TPC dE/dx

• TPC data compression (1)

• TPC data compression (2)

• ITS tracker

• Dimuon cluster finder

• Dimuon tracker

• Jet cone finder

• D0 finder

• PHOS pulse shape analysis


Tracking performance for CF/TF

Tracking efficiency Momentum resolution

Computing time: 13 sec per event (dn/dy=4000) on a 1kSPECInt machine

A. Vestbø, PhD thesis, Bergen, 2004


Tracking performance for Hough transform – version 1

• Gray-scale Hough transform– Image space: raw ADC counts

– Transform space: circle parameters

– Histogram increment: charge

Local Hough transform

Clusteranalysis

Peaks=track candidates

too CPU-time consuming

A. Vestbø, PhD thesis, Bergen, 2004


Tracking performance for Hough transform – version 2 (1)

• Linearized prehistoric Hough transform– Image space: conformal mapped cluster boundaries– Transform space: straight line parameters– Histogram increment: history of missing padrows, conditional

slice of TPC sector Corresponding Hough Space

Collaboration with the Offline group: Cvetan Cheshkov



Cvetan Cheshkov

Tracking efficiency

dN/dy=8000dN/dy=6000dN/dy=4000dN/dy=2000

B=0.5T



• Momentum resolution Pt/Pt=(1.8xPt+1.0)% (B=0.5T) ()=6.1mrad ()=5.5x10-3

• Computing time (1.3 kSpecInt machine)

Cvetan Cheshkov

dN/dy ~0 2000 4000 6000 8000

LUT Init 120ms

Hough Transform

0.7s(3ms/patch)

3.3s(15ms/patch)

5.9s(27ms/patch)

8.7s(40ms/patch)

11.3s(53ms/patch)


ITS tracking (1)

• Offline tracking– Modified offline code

– Speed-up of up to a factor of 30 for some modules

ITS Clusterer

clusters

HLT TPCTracker

ITS Vertexer

TPC tracks

ITS TrackerJ. Belikov, C.Cheshkov


ITS tracking (2)

J. Belikov, C.Cheshkov

• Tracking efficiency

TPC only (HT)ITS+TPCFakes

B=0.5T

Comparable to offline


ITS tracking (3)


• Impact parameter resolution

Dominated by SPD -> ”offline” quality,i.e. 1 GeV/c track: transverse impact parameter resolution = 60 microns


ITS tracking (4)


• Computing time (1.3 kSPECInt PC)

dN/dy Clusterer Vertexer Tracker

~0 0.5s 20ms 0.15s

2000 1.3s 45ms 0.45s

4000 1.5s 85ms 0.95s

6000 1.75s 150ms 1.70s

8000 2.0s 210ms 2.70s


D0 finder

• Offline algorithm– Cut on impact parameter

– calculate

» Distance of closest approach

» Invariant mass

» Decay angle

» Pointing angle

• Timing results (0.3 kSPECInt PC)

dN/dy 1000 2000 4000 6000 8000

CPU time [sec] 0.4 1.4 6 11 23


TPC Data Compression - Principle

Data model adapted to TPC tracking

Store (small) deviations from a model:(A. Vestbø et. al., to be publ. In Nucl. Instr. Meth. )

Cluster model dependson track parameters

Standard loss(less) algorithms; entropy encoders, vector quantization ... - achieve compression factor ~ 2 (J. Berger et. al., Nucl. Instr. Meth. A489 (2002) 406)

Tracking efficiency before and after comp. Relative pt-resolution before and after comp.

dNch

/d=1000

Tra

ckin

g ef

fici

ency

Rel

ativ

e pt

res

olut

ion

[%]


TPC Data Compression - Results

Achieved compression ratios and corresponding efficiencies

Compression factor: 10


PHOS Data Compression

• Data volume – 18k crystals

– Occupancy: ~10% (min. bias Pb+Pb, E > 10 MeV)

– 10 MHz sampling frequency

– 128 samples per pulse

– 2 channels per crystal

– 10 bits per sample

• Readout – all channels: 6 Mbyte/event

– discard empty channels (after zero-suppresion): 0.6 Mbyte/event

• Date rate– 2 kHz ’clean’ Pb+Pb interaction rate: 1.2 GByte/sec


PHOS Data Compression

• Online pulse shape analysis– Fit amplitude -> energy

– Fit time offset -> TOF

» Peak method

» Slope method

Gamma-2 fit

Peak Method : Offline time referenceat peak ( y’ =0 )

Slope Method:Offline time referenceat max. slope ( y”=0 )

(both reference points are amplitude independent)


• TPC– event reconstruction

» primary vertex

» primary vertex tracks

» secondary vertex tracks

» ghost (non-vertex) tracks

• ITS– SPD and SSD tracking

• TRD, PHOS, ...

• Full event reconstruction– data compression

– pile-up rejection

HLT task in pp


Pattern recognition scenario in pp

• TPC tracking strategy• Cluster finder • Track follower (conformal mapping method)

• First pass with vertex constraint• Second pass in order to improve efficiencies for low-pt

and secondary tracks• input all unassigned clusters from the first pass• no vertex constrain is imposed on the track follower

(conformal mapping done with respect to the first associated cluster on track)

• Kalman filter for track extension into TRD and ITS• PID in TRD and TPC

High Level Trigger

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