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FIFI LS SITR December 2008. Randolf Klein (UCB) ‏. Quick Summary. QA Issues and Mitigations. Ghost Images in Red Spectrometer – Minor Ghost images have been seen in the red spectrometer tests. The ghost images are spectrally very dilute.

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FIFI LSSITRDecember 2008

Randolf Klein (UCB)‏


QA Issues and Mitigations

  • Ghost Images in Red Spectrometer – Minor
    • Ghost images have been seen in the red spectrometer tests. The ghost images are spectrally very dilute.
    • Want to remove ghost images to minimize contamination of continuum sources.
    • Analysis of the ghost properties, spatial and spectral, is underway and progressing well.
      • Norbert analyzed a similar issue in PACS.
      • Need to isolate ghost path, then mitigate via optical baffling.
    • Assigned to Norbert Geis, Walfried Raab, Rainer Hoenle

QA Issues and Mitigations

  • Grating Fine Drive Software– Minor
    • The PID control loop for the grating fine drive (piezo actuator) is not performing optimally.
    • Intermittent loss of control loop (sometimes requiring controller reset) is degrading observing efficiency.
    • Debugging of the code is underway. Compiler optimization of the code may be the cause.
    • Further investigation assigned to Randolf Klein

Schedule Issues

  • Blue Spectrometer
  • We are making good progress, but not as much as originally hoped. The main efforts are focussed on characterizing the red spectrometer.
  • Limited amount of manpower is responsible for the schedule slip concerning the testing of the red spectrometer.

Schedule Issues

  • Documentation
  • The System Safety Analysis is submitted for comments to Dryden. We hope that this will be a positive experience allowing us to continue our airworthiness documentation.
  • Leslie Looney (UIUC) is working on the SSA, again.
  • Help from DSI is needed, too, but no one is available as far as we know.
  • Leslie works also on the Integration and Commissioning with help by Murad Hamidouche (USRA).


  • EOOP: underspending as planning had some margin

FIFI LS itself is funded by the MPE.


Progress Summary

  • Extensive Performance tests of the Red Spectrometer
    • Verification that misalignment is corrected
    • Full Water spectrum for further wavelength calibration (all pixels)
    • First line scans with grating fine drive to verify the spectral resolution
    • Maps of point sources to verify the point-spread function
  • Blue CRE modules bonded soon
raw spectral line data
Raw Spectral Line Data
  • Online Viewer:data from a fine line scan
  • Alignment problems have been solved
  • 17 unconnected pixels (4%)They can easily be reconnected.

Time lapse movie of a line scan

wavelength calibration
Wavelength Calibration
  • Coarse spectral scan:
  • Line fit (Lorentzian)to 136.5μm water line
  • Full water spectrum:108μm – 187μm
wavelength calibration1
Wavelength Calibration
  • Coarse spectral scan:
  • Line fit (Lorentzian)to 136.5μm water line
  • Full water spectrum:108μm – 187μm
  • Model fit reproduces grating constant, smile/frown.
water line fine scan
Water Line – Fine Scan

Wavelength calibration for each individual pixel

Water line at 144.5 μm

Line fit for all pixels of the central module

Fit function: Lorentz function with offset

and linear and quadratic baseline


Characterization of FIFI LS : Measuring the LSF

A grating coarse scan => modestly sampled single pixel line scan

CO line at 153 µm (J: 17-16)

CO line at 163 µm (J: 16-15)



grating equation:

= central wavelength + correction for

pixel offset

assume: shrinking factor for g

+ const. angle offset from

position read out

  • fit wavelength to literature

wavelength values using the angles

from position read out.

sigma = 0.014 µm (1/10 Dl)

wavelength [µm]

Characterization of FIFI LS : Doing the wavelength


focal plane geometry
Focal Plane Geometry
  • Continuum point source at 158μm [CII], all pixels spectrally collapsed
  • You see:
    • Point source
    • Holding wire
    • Continuum Ghost (w/ wire)
focal plane geometry1
Focal Plane Geometry
  • Continuum point source at 158μm [CII], all pixels spectrally collapsed
  • You see:
    • Point source
  • Peak positions from “point source” fitwith spectrally collapsed “[C II]” data
focal plane geometry2
Focal Plane Geometry
  • “Spaxels” are not on an ideal, rectangular grid
  • “Spaxel” position is wavelength-independent
  • All spectral pixels within a “spaxel” look at same position
point spread function
Point Spread Function
  • Drizzle map (1mmx1mm resolution) to combine all pixels to a PSF map.
  • Point source (width 1.76mm)as predicted (1.88mm)
psfs across the wavelengths
PSFs across the Wavelengths
  • PSFs are fully understood in terms of diffraction and pixel sampling


[N II]

[O I]

[C II]

CO 14-13

[N II]

15.051 10.2868

1.44015 1.3904

15.0419 10.2371

1.60656 1.48581

14.9415 10.2448

1.92769 1.71332

15.0692 10.2936

1.4805 1.4169

15.0232 10.2357

1.68634 1.53105

14.9241 10.2271

2.18659 1.86903


FIFI LSEOOPExtended Observing Opportunity Program

Allowing the US community to use FIFI LS as if it were a facility instrument.



  • We received the EMCCD guider camera, needed for EOOP operations. It will be integrated into FIFI LS at the MPE.
  • A data reduction pipeline is built up to demodulated data.
  • Currently, we are testing the pipeline on real data from the tests.
  • Next steps:
    • Extend the pipeline to create drizzled data cubes.
    • Interface to DCS and test data ingestion.
3d drizzle
3D Drizzle

This is what we did 2-d: divide up the flux between 4 adjacent grid cells with weight factors defined by overlap areas

And this is the 3-d analogue: divide up the flux between 8 grid cells with weight factors defined by overlap volumes

data reduction
Data Reduction
  • Various single routines to reduce and analyze the data have been developed by the team. At Berkeley, we now gather these algorithms to provide a single set of tools for the team and build the data reduction pipeline from them for EOOP (all IDL).
  • FIFI LS observations basic building blocks are spectral scans (with chopping[/nodding])
  • Simplest observation is single pointing, but to eliminate systematic errors dither maps will be used mostly
  • The final data product will be a calibrated data cube based on a regular grid in (ΔR.A., ΔDec.) and λ (or ΔvLSR).
  • Currently we are using a drizzle algorithm to combine data onto a regular grid.
  • A Maximum Entropy Method will be used for cube-making, ultimately.
worm holes through cube

Peak position fit

Literature value:


“Worm Holes” Through Cube



(Line is pressure broadened)

FIFI LS Instrument Overview


flight through cube
Flight Through Cube
  • With telescope simulator, we realized a monochromaticpoint source
  • The observation was carried out as fine, spatial raster+ spectral scan