Sebastian Bablok – University Bergen -- Timm M. Steinbeck - University Heidelberg 1

HLT & Calibration

Sebastian Bablok – University Bergen -- Timm M. Steinbeck - University Heidelberg 2

HLT as Calibration Data Source– Among the main tasks of HLT (especially in the first

years)● Monitoring of the detector performance● Analysing calibration data (real events and calibration triggers)● Calculation of a (first) set of calibration parameters● Applying calibration to online event reconstruction● Storing parameters in relevant databases

– Procedures are being developed in collaboration with TPC, TRD, PHOS, and Dimuon detector

– HLT is open for calibration tasks for all participating detectors

● Compatibility w. Computing Power / Financial Budget

Sebastian Bablok – University Bergen -- Timm M. Steinbeck - University Heidelberg 3

HLT & Databases● The HLT will have contact to different databases, which are for

different purposes as well as under different administration.

● For now, it is foreseen to have contact to three different DBs:

– Configuration DB (DCS)

– Condition DB (DCS)

– Calibration DB (Detector Specific)

Sebastian Bablok – University Bergen -- Timm M. Steinbeck - University Heidelberg 4

HLT DB Remarks

• Use of dedicated HLT portal nodes to contact DBs

• Redundancy of these nodes to avoid single-point-of-failures

• Data of the DBs is cached (where possible) and distributed to the appropriate places inside the HLT cluster (using its publisher-subscriber framework)

• Output data to DBs will be written from the global layer of the HLT cluster.

• Contact to the databases will be performed via ODBC or similar techniques.

Sebastian Bablok – University Bergen -- Timm M. Steinbeck - University Heidelberg 5

TPC Calibration Example (1)– Pedestal run analysis and download– Pulser runs

● Accumulation of pulser events● Gain calibration and timing alignment● Map hot/dead pads

– Laser runs● Online reconstruction of laser tracks● Drift velocity map● ExB map● static E-field distortion map

– Electron attachment / gas contamination ● Online reconstruction of particle tracks / laser tracks● Cluster charge loss per drift length

Sebastian Bablok – University Bergen -- Timm M. Steinbeck - University Heidelberg 6

TPC Calibration Example (2)

–Drift velocity map» Nuclear collisions

» Accumulate charge per timebin

» Calculate charge step at central electrode

» E.g. NA49 data

NA49

charge step

Sebastian Bablok – University Bergen -- Timm M. Steinbeck - University Heidelberg 7

TPC Calibration Example (3)

–Space charge effects» Online reconstruction of tracks

» Histogram of track impact parameters

» E.g. STAR TPC – fluctuations on second timescale

Sebastian Bablok – University Bergen -- Timm M. Steinbeck - University Heidelberg 8

TPC Calibration Example (4)

– Histogram of track residuals» E.g. STAR TPC – grid leak distortions

• Ion leakage at gating grid gap

Sebastian Bablok – University Bergen -- Timm M. Steinbeck - University Heidelberg 9

PHOS Calibration Example– Pedestal run analysis and download

–LED runs» Accumulation of pulser events

» Gain calibration and timing alignment

» Map hot/dead channels

–Gain/energy calibration» Nuclear collisions

» Online pulse shape analysis

» Timing alignment (using fastest particles)

» Online reconstruction and accumulating of showers

» Relative gain calibration by equalizing channels

» Absolute energy calibration by monitoring pi0-peak

Sebastian Bablok – University Bergen -- Timm M. Steinbeck - University Heidelberg 10

HLT Questions/Open Points

To plan connections from HLT to DBs, HLT needs to know:

• At which places (processing steps) in the HLT cluster is DB data needed?

• What is the estimated processing budget for online calibration?

• What data for or/and from which databases?

• Which detector needs what data?

• How large is which data?

• (different for different data -> detailed list wanted)

• How often is data updated? (once (caching) regular requests to DB)

• Output data

• General

• e.g. Drift Velocity: • Type of measurement (once, continuous spectrum, every X sec., average, ...)

• Write to DB (continously, at end of run, ...)

• Who has access to data (online, offline)?