Online Pattern Recognition for the ALICE High Level Trigger

The ALICE High Level Trigger has to process data online, in order to select interesting (sub)events, or to compress data efficiently by modeling techniques.Focusing on the main data source, the Time Projection Chamber (TPC), we present two pattern recognition methods under investigation: a sequential approach "cluster finder" and "track follower") and an iterative approach ("track candidate finder" and "cluster deconvoluter"). We show, that the former is suited for pp and low multiplicity PbPb collisions, whereas the latter might be applicable for high multiplicity PbPb collisions, if it turns out, that more than 8000 charged particles would have to be reconstructed inside the TPC. Based on the developed tracking schemes we show, that using modeling techniques a compression factor of around 10 might be achievableComment: Realtime Conference 2003, Montreal, Canada to be published in IEEE Transactions on Nuclear Science (TNS), 6 pages, 8 figure

Real-time TPC Analysis with the ALICE High-Level Trigger

The ALICE High-Level Trigger processes data online, to either select interesting (sub-) events, or to compress data efficiently by modeling techniques. Focusing on the main data source, the Time Projection Chamber, the architecure of the system and the current state of the tracking and compression methods are outlined.Comment: 6 pages, 5 figures, to be published in NIM

ALICE: Physics Performance Report, Volume I

ALICE is a general-purpose heavy-ion experiment designed to study the physics of strongly interacting matter and the quark-gluon plasma in nucleus-nucleus collisions at the LHC. It currently includes more than 900 physicists and senior engineers, from both nuclear and high-energy physics, from about 80 institutions in 28 countries. The experiment was approved in February 1997. The detailed design of the different detector systems has been laid down in a number of Technical Design Reports issued between mid-1998 and the end of 2001 and construction has started for most detectors. Since the last comprehensive information on detector and physics performance was published in the ALICE Technical Proposal in 1996, the detector as well as simulation, reconstruction and analysis software have undergone significant development. The Physics Performance Report (PPR) will give an updated and comprehensive summary of the current status and performance of the various ALICE subsystems, including updates to the Technical Design Reports, where appropriate, as well as a description of systems which have not been published in a Technical Design Report. The PPR will be published in two volumes. The current Volume I contains: 1. a short theoretical overview and an extensive reference list concerning the physics topics of interest to ALICE, 2. relevant experimental conditions at the LHC, 3. a short summary and update of the subsystem designs, and 4. a description of the offline framework and Monte Carlo generators. Volume II, which will be published separately, will contain detailed simulations of combined detector performance, event reconstruction, and analysis of a representative sample of relevant physics observables from global event characteristics to hard processes

High-level trigger system for the LHC ALICE experiment

The central detectors of the ALICE experiment at LHC will produce a data size of up to 75 MB/event at an event rate less than approximately equals 200 Hz resulting in a data rate of similar to 15 GB/s. Online processing of the data is necessary in order to select interesting (sub)events ("High Level Trigger"), or to compress data efficiently by modeling techniques. Processing this data requires a massive parallel computing system (High Level Trigger System). The system will consist of a farm of clustered SMP-nodes based on off- the-shelf PCs connected with a high bandwidth low latency network

Online pattern recognition for the ALICE high level trigger

The ALICE High Level Trigger system needs to reconstruct events online at high data rates. Focusing on the Time Projection Chamber we present two pattern recognition methods under investigation: the sequential approach (cluster finding, track follower) and the iterative approach (Hough Transform, cluster assignment, re-fitting). The implementation of the former in hardware indicates that we can reach the designed inspection rate for p-p collisions of 1 kHz with 98% efficiency

Level-3 trigger for a heavy ion experiment at LHC

At the upcoming large hadron collider (LHC) at CERN one expects to measure 20,000 particles in a single Pb-Pb event resulting in a data rate of ~75 MByte/event. The event rate is limited by the bandwidth of the storage system. Higher rates are possible by selecting interesting events and sub-events (Level-3 trigger) or compressing the data efficiently with modeling techniques. Both techniques require a fast parallel pattern recognition. One possible solution to process the detector data at such rates is a farm of clustered SMP nodes, based on off-the-shelf PCs, and connected by a high bandwidth, low latency network. (8 refs)