The ALICE High Level Trigger

Transcription

1 The ALICE High Level Trigger Richter Department of Physics and Technology, of Bergen, Norway for the ALICE HLT group and the ALICE Collaboration Meeting for CERN related Research in Norway Bergen, November 19, 2005

2 Outline Physics motivation Track Reconstruction Data Compression Hardware Architecture Software Architecture Summary & Outlook M. 2

3 Tasks of the HLT Data Rate Reduction: 25 Gbyte/sec > 1.25 GByte/sec 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 Online Monitoring Access to the results of the event reconstruction M. 3

4 Physics Applications: Two examples Quarkonium: Dielectron case T. Vik, PhD thesis, Oslo, 2005 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) Open Charm Detection of hadronic charm decays: D0 K + π + About 1 D0 per event (central Pb Pb) in ALICE acceptance After cuts signal/event = background/event = 0.01 M. 4

5 Track reconstruction for the TPC two approaches: Cluster finding Reconstruct space points from 2D clusters Sequential tracking Iterative tracking Hough transform on Raw ADC Data gives track candidates Cluster fitting with respect to track parameters Iterative tracking Track reconstruction Connect space points into tracks and fit them to a model (helix) Sequential tracking Cluster finding (weighted mean) Track follower Local Hough transform Cluster analysis M. Peaks= track candidates 5

6 Performance of track reconstruction Integrated tracking efficiency dnch/dη=4000 Relative pt resolution [%] Tracking efficiency Efficiency of the Online HLT track reconstruction algorithms for TPC sequential tracking dnch/dη=4000 Reconstruction of whole event possible Sophisticated online event selection based on the reconstructed event A. Vestbø, PhD thesis, Bergen, 2004 M. 6

7 Data compression for TPC data Standard loss(less) algorithms; entropy encoders, vector quantization... achieve compression factor ~ 2 (J. Berger et. al., Nucl. Instr. Meth. A489 (2002) 406) Data model adapted to TPC tracking Store (small) deviations from a model: Cluster model depends on track parameters (A. Vestbø et. al., to be publ. In Nucl. Instr. Meth. ) dnch /dη=1000 Relative pt resolution before and after comp. Relative pt resolution [%] Tracking efficiency Tracking efficiency before and after comp. M. 7

8 Data compression: implementation 10 bit ADC raw data Compression ratio track/event reconstruction up to 20 clusters assignment to tracks track/cluster compression store remaining clusters with significant accuracy 7 10 additional cluster analyzer Tracking efficiency: left removing all remaining clusters, right keeping selection TPC display before and after cluster asignment and removal M. 8

9 HLT Hardware architecture HLT is a generic high performance cluster copy of detector raw data from DAQ HLT RORC (HLT Readout Receiver Card) HLT input/output FPGA co processor ~ 500 dual processor nodes Input from ~ 250 HLT RORCs M. 9

10 Data transportation and analysis Online: Publisher Subscriber Framework Offline: Aliroot Communication framework running on HLT cluster Common interface for communication between processes on the same node and also between different nodes across the underlying network Generic modular framework allowing arbitrary connectivity metric (one to many, many to one) Full ALICE Offline Reconstruction and Analysis package based on ROOT Provides Testbench for development of HLT analysis components Not optimized on data transport Most potential developers of HLT components familiar with AliRoot Easy integration into Online framework required Quality of the online analysis components has to be compared to the offline methods Binary compatibility of Analysis code desirable, PubSub is only data transportation framework M. 10

11 Software: Schematic overview Online Pub/Sub Framework Wrapper Processing Component Offline Component Handler Shared Library Component Base C++ Class Component Handler C++ Class HLT TPC Shared Library Clusterfinder C++ Class Tracker C++ Class Merger C++ Class.... HLT ITS Shared Library... HLT... Shared Library... C Wrapper Interface (pure C) Common policy and Binary compatibility through shared libraries and abstract interface M. AliRoot 11

12 Current TPC test environment pp Data simulated with AliRoot CF 0 CF 1 CF 2 CF 3 CF 4 CF 5 Sector Tracker TCP Dump Subscriber Ethernet Pub/Sub Framework (HLT farm) HOMER HLT Online Monitoring Environment including ROOT (Ali)Root (analysis workstation) schematic view for one TPC sector M. 12

13 Summary The HLT will enhance the yield of rare cross section signals in ALICE by online event reconstruction and/or data compression The system will consist of up to 500 dual processor PCs, partially equipped with FPGA Co processors Current online tracking performance is sufficient for dnch/dη < 4000 already now Data modeling indicate compression factors of about 10 with almost no efficiency loss Binary compatibility between Online and Offline analysis Prototype for TPC Online monitoring running M. 13