A bottom-up approach to data calibration Carlo Izzo - PowerPoint PPT Presentation

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A bottom-up approach to data calibration Carlo Izzo

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Title: A bottom-up approach to data calibration Carlo Izzo


1
A bottom-up approach to data calibrationCarlo
Izzo
2
MOSES
  • Multi
  • Object
  • Spectroscopy
  • Empirical
  • Self-calibration

Many MOS
MOSes!
3
Major requirements for automatic data reduction
Robustness
  • managing unexpected situations
  • supplying information about what went wrong
  • actual monitoring of the instrument

Flexibility
  • using general algorithms
  • applying instrument-independent DRS strategies
  • low maintenance cost

4
Major requirements for automatic data reduction
Google search flexible software reusable
software robust software working
software bug-free software
  • 551,000 hits
  • 495,000
  • 353,000
  • 223,000
  • 56,000

Robustness
  • managing unexpected situations
  • supplying information about what went wrong
  • actual monitoring of the instrument

Flexibility
  • using general algorithms
  • applying instrument-independent DRS strategies
  • low maintenance cost

5
Identification of reference objects
Reference objects are used for data calibration
  • Stars
  • astrometric calibration
  • photometric calibration
  • Spectral lines (arc lamp, sky)
  • wavelength calibration
  • Spectral edges (flat)
  • spectral curvature
  • Slit positions (pinhole mask)
  • mask-to-CCD transformation

6
Identification of reference objects
How to identify reference objects? The top-down
approach
The model
The catalog
The objects
The improved model
7
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8
Identification of reference objects
How to identify reference objects? The top-down
approach
The model
The catalog
The objects
The improved model
9
A typical MOS arc lamp exposure
10
Using first-guess model to find reference lines
11
Earthquake!
12
False matches confirm expectations
VIMOS-MOS, LR_red, all quadrants
13
Finding standard stars
14
Finding standard stars
ERROR no standard star found cannot compute
zeropoint
15
Advantages of the top-down approach
  • The safest approach for stable instruments
  • The only possible approach if very few reference
    objects are available

16
Disadvantages of the top-down approach
  • Not robust it fails with unstable instruments
  • Not flexible it requires frequent
    reconfiguration
  • Biased toward expectations
  • Misled by contaminations

17
Finding standard stars
The stars
The catalog
18
Finding standard stars
The catalog (i.e., the pattern)
19
Finding standard stars
The stars
The catalog (i.e., the pattern)
20
Finding standard stars
The stars
The catalog (i.e., the pattern)
21
Finding standard stars
The stars
The catalog (i.e., the pattern)
22
Point pattern-matching (2D)
G. S. Cox et al. (1991) A New Method of
Rotation, Scale and Translation Invariant Point
Pattern Matching Applied To the Target
Acquisition and Guiding of an Automatic
Telescope
23
Looking for peaks
  • ______________________________________

24
Looking for patterns
  • The pattern wavelengths
  • 5400.562
  • 5460.742
  • 5764.419
  • 5769.598
  • 5790.656
  • 5852.488
  • 5875.620
  • 5881.900
  • The data pixel positions
  • 1220.64
  • 1253.23
  • 1299.44
  • 1304.07
  • 1339.30
  • 1400.33
  • 1450.28
  • 1457.32
  • 1471.00
  • 1496.21

25
Looking for patterns
  • The pattern wavelengths
  • 5400.562
  • 5460.742
  • 5764.419
  • 5769.598
  • 5790.656
  • 5852.488
  • 5875.620
  • 5881.900
  • The data pixel positions
  • 1220.64
  • 1253.23
  • 1299.44
  • 1304.07
  • 1339.30
  • 1400.33
  • 1450.28
  • 1457.32
  • 1471.00
  • 1496.21

26
A simple case calibrating a single spectrum
  • _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

27
Identifying arc lamp lines
28
Wavelength map
  • Mean accuracy 0.05 pixel

29
Resampled spectrum
Mean accuracy 0.05 pixel
30
A case with many spectra (VIMOS GRIS_HRred)
31
Wavelength map
Mean accuracy 0.07 pixel
32
Another example (FORS2-MXU GRIS_150I)
33
Wavelength map
  • Mean accuracy 0.05 pixel

34
Identifying the spectra
  • Select the reference wavelength in this
    example, l 7000.00 A

35
Identifying the slits
The mask
  • Select the reference wavelength in this
    example, l 7000.00 A

The CCD
36
Measuring the spectral curvature
37
Measuring the spectral curvature
38
Rectified image
39
Rectified image
40
Identification of reference objects
How to identify reference objects? The bottom-up
approach
The generated model
The pattern
The candidate objects
41
Identification of reference objects
How to identify reference objects? The bottom-up
approach
The generated model
The pattern
The candidate objects
42
Robustness
  • This approach can cope with unexpected position
    and/or number of spectra
  • Reference lines are more safely identified for
    being part of a pattern, rather than for being
    close to some expected position

43
Flexibility
  • This approach is (MOS) instrument-independent
  • Low maintenance cost, even at instrument upgrades
    (new chip, new grism, new lamps)
  • This approach may be applied to extracted
    products from any kind of spectroscopic data (not
    just MOS, but also IFU, echelle, etc.).

44
GIRAFFE Medusa1_H525.8nm
Mean accuracy 0.10 pixel
45
Disadvantages of the bottom-up approach
  • As the top-down approach depends on a model, the
    bottom-up approach depends on the data!
  • This approach is a black box as for any program
    based on a bottom-up strategy (e.g., using
    trained neural networks), it is often difficult
    to find the reason of a failure.

46
The CRIRES data reduction challenges
  • Inputs
  • The Estimate Polynomial
  • The Wavelength Error of the estimate (WLerror)
  • The degree of the searched polynomial (degree)
  • The number of samples (nsamples)
  • The lines catalogue (OH, Gas cell, Lamps, Hitran)
  • The signal to calibrate (in pixels)
  • Algorithm
  • Consider degree1 positions Ai regularly spaced,
    and nsamples points spread within WLerror around
    these positions
  • For each possible sequence of points
    (nsamples(degree1) possibilities), the
    interpolation polynomial is created and
    considered as candidate
  • The candidate polynomial is used to convert the
    signal to calibrate from pixels to wavelengths.
    This signal is compared to the signal generated
    from the catalogue. A likelihood coefficient in
    computed
  • The best likelihood parameter gives the best
    candidate, i.e. the polynomial that is the
    closest to the solution
  • A second pass (or more) is used to refine the
    solution with the first pass solution used as
    estimate, with a smaller WLerror and a higher
    degree.

47
  • Inputs
  • The Estimate Polynomial
  • The Wavelength Error of the estimate (WLerror)
  • The degree of the searched polynomial (degree)
  • The number of samples (nsamples)
  • The lines catalogue (OH, Gas cell, Lamps, Hitran)
  • The signal to calibrate (in pixels)
  • Algorithm
  • Consider degree1 positions Ai regularly spaced,
    and nsamples points spread within WLerror around
    these positions
  • For each possible sequence of points
    (nsamples(degree1) possibilities), the
    interpolation polynomial is created and
    considered as candidate
  • The candidate polynomial is used to convert the
    signal to calibrate from pixels to wavelengths.
    This signal is compared to the signal generated
    from the catalogue. A likelihood coefficient in
    computed.
  • The best likelihood parameter gives the best
    candidate, i.e. the polynomial that is the
    closest to the solution
  • A second pass (or more) is used to refine the
    solution with the first pass solution used as
    estimate, with a smaller WLerror and a higher
    degree

48
CRIRES Wavelength Calibration
  • Inputs
  • The Estimate Polynomial
  • The Wavelength Error of the estimate (WLerror)
  • The degree of the searched polynomial (degree)
  • The number of samples (nsamples)
  • The lines catalogue (OH, Gas cell, Lamps, Hitran)
  • The signal to calibrate (in pixels)
  • Algorithm
  • Consider degree1 positions Ai regularly spaced,
    and nsamples points spread within WLerror around
    these positions
  • For each possible sequence of points
    (nsamples(degree1) possibilities), the
    interpolation polynomial is created and
    considered as candidate
  • The candidate polynomial is used to convert the
    signal to calibrate from pixels to wavelengths.
    This signal is compared to the signal generated
    from the catalogue. A likelihood coefficient in
    computed
  • The best likelihood parameter gives the best
    candidate, i.e. the polynomial that is the
    closest to the solution
  • A second pass (or more) is used to refine the
    solution with the first pass solution used as
    estimate, with a smaller WLerror and a higher
    degree.

49
Thanks to
Michele Peron (ESO/SDD) Sabine Moehler
(ESO/DFO) Pascal Ballester (ESO/SDD) Lars Lundin
(ESO/SDD) Kieran OBrien (ESO/PSO) Emmanuel Jehin
(ESO/PSO) Ralf Palsa (ESO/SDD) Marguerite Pierre
(CEA, Saclay, France) Stefano Cristiani (INAF,
Trieste, Italy) Christophe Adami (LAM, Marseille,
France) Harald Kuntschner (ESO/ST-ECF) Martino
Romaniello (ESO/DFO) Vanessa Doublier
(ESO) Sandro Villanova (DdA, Padova,
Italy) Stefano Bagnulo (ESO/PSO) Burkhard Wolff
(ESO/DFO) Emanuela Pompei (ESO/LSO) Ivo Saviane
(ESO/LSO)
50
Thanks to
  • - prime mover
  • supervisor
  • quality control (FORS)
  • image processing advisor
  • math advisor
  • requirements
  • requirements
  • software advisor
  • astronomer advisor
  • astronomer advisor
  • beta-tester (FORS)
  • astronomer advisor
  • astronomer advisor
  • beta-tester (FORS)
  • beta-tester (FORS)
  • astronomer advisor
  • quality control (VIMOS)
  • requirements (EMMI)
  • requirements (EFOSC)

Michele Peron (ESO/SDD) Pascal Ballester
(ESO/SDD) Sabine Moehler (ESO/DFO) Yves Jung
(ESO/SDD) Lars Lundin (ESO/SDD) Kieran OBrien
(ESO/PSO) Emmanuel Jehin (ESO/PSO) Ralf Palsa
(ESO/SDD) Marguerite Pierre (CEA, Saclay,
France) Stefano Cristiani (INAF, Trieste,
Italy) Christophe Adami (LAM, Marseille,
France) Harald Kuntschner (ESO/ST-ECF) Martino
Romaniello (ESO/DFO) Vanessa Doublier
(ESO) Sandro Villanova (DdA, Padova,
Italy) Stefano Bagnulo (ESO/PSO) Burkhard Wolff
(ESO/DFO) Emanuela Pompei (ESO/LSO) Ivo Saviane
(ESO/LSO)
51
Looking for patterns
  • The pattern wavelengths
  • 5400.562
  • 5460.742
  • 5764.419
  • 5769.598
  • 5790.656
  • 5852.488
  • 5875.620
  • 5881.900
  • The data pixel positions
  • 1220.64
  • 1253.23
  • 1299.44
  • 1304.07
  • 1339.30
  • 1400.33
  • 1450.28
  • 1457.32
  • 1471.00
  • 1496.21

52
Looking for peaks
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