Difference: SpireCalibrationWeb (86 vs. 87)

Revision 872013-06-10 - LucaConversi

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META TOPICPARENT name="WebHome"

SPIRE instrument and calibration web pages

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  • The SPIRE Photometer filter transmission curves, also known as Relative Spectral Response Functions (RSRF) are available here. For more details, please read the .readme file in this ftp folder.
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Planck-HFI & Herschel-SPIRE cross calibration: absolute offset re-processing

Herschel-SPIRE detectors are only sensitive to relative variations, as a consequence the absolute brightness of the observed region is unknown and maps are constructed such that they have zero median. Planck-HFI detectors are similar to the SPIRE ones, however its observing strategy allows it to (almost) observe a sky's great circle every minute (having a 1 rpm spinning rate). By comparing the sky brightness as measured by COBE-FIRAS at the galactic poles (where the dust emission is lower), HFI is capable of setting an absolute offset to its maps. SPIRE and HFI share two channels with overlapping wavebands: SPIRE-PMW and HFI-857 have a similar filter profile, while SPIRE-PLW and HFI-545 are shifted by $\sim 10$\%.

As of HCSS 10, a new task named zeroPointCorrection is available: this task calculates the absolute offset for a SPIRE map based on cross-calibration with HFI-545 and HFI-857 maps, colour-correcting HFI to SPIRE wavebands assuming a grey body function with fixed beta. At first, Planck data needed by the task were delivered to HSC under special agreement: as a consequence, Herschel users were not able to re-process the absolute offset calculation. However, Planck data became public in April 2013 and it is now possible to exectue the zeroPointCorrection.

Files needed:

  • Download the HFI545 and HFI-857 maps from the HSC/SPIRE FTP area. These maps are derived from the ones available in the Planck Legacy Archive, but convolved with an 8 arcmin Gaussian beam in order to circularize the effective maps' beams, plus the maps absolute offset as estimated by the Planck-HFI team via cross-calibration with FIRAS (see Planck Collaboration VIII. 2013, In preparation)
  • Download the colour correction table file SpireHfiColourCorrTab_v1.1

The offsets are computed on extdPxW maps, calibrated for extended emission, with extended gain correction applied and in units of MJy/sr (as explained in the section 5.7 of the SPIRE Data Reduction Guide). Hence, the re-processing will start from a level-1 context (which may be the result of merging multiple observations, see e.g. the Photometry Map Merging scirpt available in HIPE under the menu ScriptsSPIRE Useful script) and then executing the following code:

#################### SCRIPT BEGINS ####################

# The script assumes that:
# 1. a Level1Context is already defined and it is named "level1"
# 2. a Level2Context is already defined and it is named "level2"
# E.g.:
#
# obs    = getObservation(1342195871,useHsa=1)
# level1 = obs.level1
# level2 = obs.level2

# Define properties needed by zeroPointCorrection task
Configuration.setProperty("spire.spg.hfi.colorc", "PATH_TO_FILE/SpireHfiColourCorrTab_v1.1.fits")
Configuration.setProperty("spire.spg.hfi.545map", "PATH_TO_FILE/DX9_map_545_smooth_8arcmin.fits")
Configuration.setProperty("spire.spg.hfi.857map", "PATH_TO_FILE/DX9_map_857_smooth_8arcmin.fits")

# Check if zero-point correction task is runnable 
zeroPointIsRunnable = zeroPointCorrection.isRunnable()
if zeroPointIsRunnable:
    print "Configuration is properly set. Zero-point correction task is runnable in this environment" 

# Run zero-point correction task 
if (zeroPointIsRunnable): 
   level2ZeroPoint = MapContext()
   for key in level2.meta.keySet():
      level2ZeroPoint.meta[key]=level2.meta[key].copy()
   #
   # Load relative gain correction file
   chanRelGains = obs.calibration.phot.chanRelGain
   #
   # Create new Level1Context
   scansZeroPoint = Level1Context()
   scansZeroPoint.meta = level1.meta
   #
   # Apply relative gain correction, loading the original Level1Context from the "level1" variable
   for i in range(level1.getCount()):
      psp = level1.getProduct(i)
      if psp.type=="PPT":  psp.setType("PSP")   #for old Level 1 contexts
      psp = applyRelativeGains(psp, chanRelGains)
      scansZeroPoint.addProduct(psp)
   #
   # Try to load the de-striper diagnostic products to speed-up re-processing
   arrays = ["PSW","PMW","PLW"]
   for array in arrays:
      diagref = level2.refs['psrc'+array.upper()+'diag']
      if diagref != None:
         diag = diagref.product
      else: 
         diag = None
      #
      # (Re-)run destriper on new Level1Context
      newscans,mapZero,diagZero, p4,p5 = destriper(level1=scansZeroPoint, array=array, nThreads=2, \
            withMedianCorrected=True, useSink=True, startParameters=diag)
      #
      # Save diagnostic product, this time with prefix extd, into the "level2" variable
      level2ZeroPoint.refs.put(array,ProductRef(mapZero))
      level2.refs.put('extd'+array.upper()+'diag', ProductRef(diagZero))
   #
   # Run the zeroPointCorrection tasks on extdPxW maps
   zeroPointMaps, zeroPointParam=zeroPointCorrection(level2=level2ZeroPoint, hfiFwhm=8.0)
   #
   # Populate the "level2" variable with extdPxW maps, with the computed offset
   for array in arrays:
         level2.refs.put("extd"+array.upper(),ProductRef(zeroPointMaps.getProduct("extd"+array.upper())))

#################### SCRIPT ENDS ####################
 
Data Processing Issues

The main issues that you might find in your data are: undetected glitches, thermistor or detector jumps, and bad baseline removal.

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  • After either of those cases, you must then re-run level 1 to 2 steps on the newly modified level1 product. If your observation has been already re-reduced with HIPE 10, original and new level1s are already destriped, so you can directly run the naive map-maker on the new level1. Otherwise, you must run the destriper step: check the pipeline script for details.
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Planck-HFI & Herschel-SPIRE cross calibration: absolute offset re-processing

Herschel-SPIRE detectors are only sensitive to relative variations, as a consequence the absolute brightness of the observed region is unknown and maps are constructed such that they have zero median. Planck-HFI detectors are similar to the SPIRE ones, however its observing strategy allows it to (almost) observe a sky's great circle every minute (having a 1 rpm spinning rate). By comparing the sky brightness as measured by COBE-FIRAS at the galactic poles (where the dust emission is lower), HFI is capable of setting an absolute offset to its maps. SPIRE and HFI share two channels with overlapping wavebands: SPIRE-PMW and HFI-857 have a similar filter profile, while SPIRE-PLW and HFI-545 are shifted by $\sim 10$\%.

As of HCSS 10, a new task named zeroPointCorrection is available: this task calculates the absolute offset for a SPIRE map based on cross-calibration with HFI-545 and HFI-857 maps, colour-correcting HFI to SPIRE wavebands assuming a grey body function with fixed beta. At first, Planck data needed by the task were delivered to HSC under special agreement: as a consequence, Herschel users were not able to re-process the absolute offset calculation. However, Planck data became public in April 2013 and it is now possible to exectue the zeroPointCorrection.

Files needed:

  • Download the HFI-545 and HFI-857 maps from the HSC/SPIRE FTP area. These maps are derived from the ones available in the Planck Legacy Archive, but convolved with an 8 arcmin Gaussian beam in order to circularize the effective maps' beams, plus the maps absolute offset as estimated by the Planck-HFI team via cross-calibration with FIRAS (see Planck Collaboration VIII. 2013, In preparation)
  • Download the colour correction table file SpireHfiColourCorrTab_v1.1

The offsets are computed on extdPxW maps, calibrated for extended emission, with extended gain correction applied and in units of MJy/sr (as explained in the section 5.7 of the SPIRE Data Reduction Guide). Hence, the re-processing will start from a level-1 context (which may be the result of merging multiple observations, see e.g. the Photometry Map Merging scirpt available in HIPE under the menu ScriptsSPIRE Useful script) and then executing the zeroPointCorrection task with one of the following methods:

  1. Run the zeroPointCorr.py script. It assumes that a Level1Context and Level2Context are already defined and named level1 and level2, respectively. It also sets three required properties needed by the zeroPointCorrection task, i.e. the location of two HFI maps and the colour correction table: please modify the PATH_TO_FILE accordingly to your set-up.
  2. Alternatively, run the correction using the SPIA interface (SPIRE Photometer Interactive Analysis). In order to be able to run the zeroPointCorrection task, the user.props file present (by default) in you $HOME/.hcss directory must be modified and the following lines added (please modify the PATH_TO_FILE accordingly to your set-up):
    • spire.spg.hfi.545map = PATH_TO_FILE/DX9_map_545_smooth_8arcmin.fits
    • spire.spg.hfi.857map = PATH_TO_FILE/DX9_map_857_smooth_8arcmin.fits
    • spire.spg.hfi.colorc = PATH_TO_FILE/SpireHfiColourCorrTab_v1.1.fits
 

Source Extraction and Photometry

  • The current recommended method for photometry sourceExtractorTimeline task (formerly known as the Timeline Fitter) which works on the detector timelines. The Map based algorithm sourceExtractorSussex (SUSSEXtractor) providers good results and is useful on larger maps where the sourceExtractorTimeline will be significantly slower. sourceExtractorDaophot (DAOphot) also provides a reasonable estimate of the source flux but may require an aperture correction.
 
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