Solutions for Quality Control of multi-detector instruments SPIE Marseille, 25 June 2008

Solutions for Quality Control of multi-detector instrumentsSPIE Marseille, 25 June 2008

Solutions for Quality Control of multi-detector instrumentsand their application to CRIRES and VIMOS

Burkhard Wolff, Reinhard Hanuschik, Mark Neeser, Wolfgang HummelData Processing and Quality Control Group

ESO, Garching, Germany

1. Data Quality Control at ESO2. Aggregates of QC Parameters3. Scoring of QC Parameters4. Conclusions


Data processing and Quality Control at ESOData processing and Quality Control at ESO

Paranal Science Operations• Science and calibration data• Basic quality checks

Paranal Science Operations• Science and calibration data• Basic quality checks

Archive• Raw data• Processed data• Meta data

Archive• Raw data• Processed data• Meta data

QC Garching• Pipeline processing of raw data• Evaluation of data quality and instrument health

QC Garching• Pipeline processing of raw data• Evaluation of data quality and instrument health

QC ReportsQC Reports


QC toolset: pipeline products, graphical reports, and QC parametersQC toolset: pipeline products, graphical reports, and QC parameters

Raw DataRaw Data ProductsProducts Evaluation•Parameters OK?•Reports OK?•Long-term trends•Feedback to Mountain

Evaluation•Parameters OK?•Reports OK?•Long-term trends•Feedback to MountainQC ParametersQC Parameters

• Products: pipeline produced• QC reports: graphical representation of products• QC parameters: abstract of information from products and reports (e.g. medium bias level)


QC tools: graphical reportsQC tools: graphical reports


QC tools: QC parameters (trending over time)QC tools: QC parameters (trending over time)


QC of multi-detector instruments: volume and complexity challengeQC of multi-detector instruments: volume and complexity challenge

Complexity: • already about 1000 QC parameters existingVolume:• current VLT instruments: maximum of 4 detectors• survey telescopes VISTA and VST: 16 and 32 detectorsAll this needs to be followed and checked on a regular basis!

Complexity: • already about 1000 QC parameters existingVolume:• current VLT instruments: maximum of 4 detectors• survey telescopes VISTA and VST: 16 and 32 detectorsAll this needs to be followed and checked on a regular basis!

Solutions: • aggregates of QC parameters across detectors• automated scoring of QC parametersApplied to operational instruments CRIRES and VIMOS (4 detectors each).

Solutions: • aggregates of QC parameters across detectors• automated scoring of QC parametersApplied to operational instruments CRIRES and VIMOS (4 detectors each).


CRIRES and VIMOSCRIRES and VIMOS

CRIRES• high resolution echelle spectrograph• operating at 1 to 5 microns• four 1k x 1k detectors, read-out to 1k x 0.5k, complete array: 4k x 0.5k

CRIRES• high resolution echelle spectrograph• operating at 1 to 5 microns• four 1k x 1k detectors, read-out to 1k x 0.5k, complete array: 4k x 0.5k

VIMOS• multi-mode instrument• IMG: U, B, V, R, I, z filters• MOS: slit masks, medium resolution• IFU: 6400 fibres, medium resolution• four detectors: 2148 x 2440 pixels (IMG) 2148 x 4096 pixels (MOS, IFU)

VIMOS• multi-mode instrument• IMG: U, B, V, R, I, z filters• MOS: slit masks, medium resolution• IFU: 6400 fibres, medium resolution• four detectors: 2148 x 2440 pixels (IMG) 2148 x 4096 pixels (MOS, IFU)

Q2

Q3 Q4

Q1

DET 1 DET 2 DET 3 DET 4


Aggregates of QC parametersAggregates of QC parameters

QC parameters per detector• calculated separately for each detector• challenge for multi-detector instruments• single detector outliers must be detected• coherent changes must be detected

QC parameters per detector• calculated separately for each detector• challenge for multi-detector instruments• single detector outliers must be detected• coherent changes must be detected

Aggregates of QC parameters• defined as average and rms (standard deviation) across all detectors• information condensed into two values

Aggregates of QC parameters• defined as average and rms (standard deviation) across all detectors• information condensed into two values


Aggregates of QC parameters: coherent changesAggregates of QC parameters: coherent changes

CRIRES wavelength calibration: separate solution for each detector→ central wavelength per detector → averages across detectors

coherent changes

rms unchanged


Aggregates of QC parameters: incoherent changes of correlated valuesAggregates of QC parameters: incoherent changes of correlated values

Wavelength calibration: incoherent changes after interventions, coherent changes due to T→ AVG and RMS provide all necessary information

interventions

rms changing

T change


Aggregates of QC parameters: incoherent changes of non-correlated values (I)Aggregates of QC parameters: incoherent changes of non-correlated values (I)

VIMOS median bias levels: averages across quadrants (detectors)each 2 detectors share electronics → AVG and RMS often vary simultaneously

coherence ?

incoherence

incoherence


Aggregates of QC parameters: incoherent changes of non-correlated values (II)Aggregates of QC parameters: incoherent changes of non-correlated values (II)

VIMOS median bias level: fake coherence→ only one detector changes: quadrant 2 (◄)


Aggregates of QC parameters: impact on parameter definitionAggregates of QC parameters: impact on parameter definition

VIMOS total noise in bias exposures: dominated by quadrants 1 an 3→ change calculation method

change in Q4

no change in AVG and RMS


Scoring of QC parameters: static and dynamic limitsScoring of QC parameters: static and dynamic limits

thresholds outlier

Scoring• define upper and lower limits on (subset of) QC parameters• within limits: score 0 outside limits: score 1 total score per detector and product• static limits: based on experience• dynamic limits: from statistics

Scoring• define upper and lower limits on (subset of) QC parameters• within limits: score 0 outside limits: score 1 total score per detector and product• static limits: based on experience• dynamic limits: from statistics


Scoring of QC parameters: limits from functions and relative changesScoring of QC parameters: limits from functions and relative changes

Additions to scoring• limits determined by a function: wavelength λ depends on T• limits as relative changes: calibrations applied to science were measured under similar conditions (e.g. at same temperature)

Additions to scoring• limits determined by a function: wavelength λ depends on T• limits as relative changes: calibrations applied to science were measured under similar conditions (e.g. at same temperature)

How to score?


Scoring of QC parameters: application to aggregatesScoring of QC parameters: application to aggregates

Scoring on aggregates• provides additional functionalities not possible with scoring on single detectors• example: CRIRES spectrum extraction• position of spectra is arbitrary but RMS of positions should be low

Scoring on aggregates• provides additional functionalities not possible with scoring on single detectors• example: CRIRES spectrum extraction• position of spectra is arbitrary but RMS of positions should be low

QC ReportsQC Reports


Advanced QC toolset: aggregates and scoring addedAdvanced QC toolset: aggregates and scoring added

Raw DataRaw Data ProductsProducts

Evaluation•Information on demand

Evaluation•Information on demandQC ParametersQC Parameters

QC Parameter Aggregates

QC Parameter Aggregates

ScoringScoring

Conclusions:• aggregates and scoring tested• ready for future instruments

Conclusions:• aggregates and scoring tested• ready for future instruments

Solutions for Quality Control of multi-detector instruments SPIE Marseille, 25 June 2008

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