Observational Astronomy Primer Exercises: 3.) Reducing SPIRE Data, Aperture Photometry

Tasks/exercises for SPIRE data in HIPE


As an example, work through one supernova remnant from the Hi-Gal survey, G332.4+0.1.  You can find more information about this object at, e.g., SIMBAD.

  • Try to download the Hi-Gal data that has already been calibrated and mapped.  Check the data quality to determine if you need to re-reduce or make the map again (for example, if there is some residual striping from the cross-scans).
  • If you decide you need to re-reduce it, you can follow the links I’ve listed below.
  • You will need the OBSID – you can look for this with the HSA Tool at  http://www.cosmos.esa.int/web/herschel/science-archive
    –> Input the object name or coordinates and it will return a list of observations and IDs
    There will be many observations with PACS/SPIRE/HIFI. Choose the ones you want to see.  The SPIRE large map obs for G332.4+0.1 were OBSID 1342192055
  • MAKE SURE you are using a recent version of HIPE if you plan to reprocess the data.  You will need LOTS of RAM to reduce Herschel data – aim for a machine with at least 32GB.

 

 

Data reduction pipeline in HIPE

 


==> See tutorials on NASA/IPAC’s site:
https://nhscsci.ipac.caltech.edu/sc/index.php/Spire/PhotDataAnalysis
And the DRG for reprocessing http://herschel.esac.esa.int/hcss-doc-15.0/load/spire_drg/html/photometer_launchpad.html#d0e571
The tutorial for the SPIRE large map pipeline reprocessing:
http://herschel.esac.esa.int/hcss-doc-15.0/load/spire_drg/html/spire-largemap.html#spire-largemap-reprocess-data-user-guide
That one is quite good – much better than was available when I started learning how to reduce SPIRE data, with many picture examples of things to look out for in your final images
They have also made available a large number of web video tutorials over the past few years:
https://nhscsci.ipac.caltech.edu/sc/index.php/Spire/VideoTutorials
https://nhscsci.ipac.caltech.edu/sc/index.php/Spire/Webinars

 


 

SPIRE photometry ‘recipe’


http://herschel.esac.esa.int/hcss-doc-15.0/load/dag/html/Dag.ImageAnalysis.HowTo.AperturePhotometry.html
http://herschel.esac.esa.int/hcss-doc-15.0/load/spire_drg/html/ch06s09.html  –> section 6.9.1.6
Also see the HIPE Data Analyis Guide for a description of basically any analysis task you would care to do in HIPE:
http://herschel.esac.esa.int/hcss-doc-15.0/

Overall summary of commands from the SPIRE ‘recipe’, downloading level-2 data from the HSA:

obsid = 1342192055  # Specify the observation ID from the HSA  (Kes 32)
alpha = 2   #For a source with spectrum S(ν) proportional to Sα (SPIRE default pipeline assumes α = -1)
array = "PSW"  # Run for an individual SPIRE band: "PSW", "PMW", "PLW"
obs = getObservation(obsid, useHsa=True, instrument='SPIRE') # Loading an observation of Gamma Dra from the HSA
# obs = getObservation(obsid, poolName='mypool', instrument='SPIRE') # Alternative, for observation from your own local pool
mapExtd = obs.level2.refs["extd"+array].product  #Extract  Extended (MJy/sr) calibrated maps from the Observation Context

cal = spireCal() #Load the calibration tree
#  --> if that doesn't work: cal = spireCal(calTree="spire_cal", saveTree=1)
beamCorrTable  = cal.phot.refs["ColorCorrBeam"].product
kCorrExtdTable = cal.phot.colorCorrKList.refs[0].product
beamArea  = beamCorrTable.meta["beamPipeline"+array.title()+"Arc"].double
kCorrExtd = kCorrExtdTable.getAlphaCorrection(alpha, array)

mapExtended = convertImageUnit(image=mapExtd, newUnit='Jy/pixel') # convert maps from MJy/sr to Jy/pix
mapExtendedCorrected = imageMultiply(image1=mapExtended, scalar=kCorrExtd) #Color Correction

ra  = '244.252833'   # Target RA and string values:  16h 17m 00.68s
dec = '-50.799300'   # Target Dec and string values: -50d 47' 57.48''
photrad = 200.0      #photometry source radius, in arcsec  (I'm just making these up - you will have to choose an appropriate region)
phot_ann_in = 300.0  #photometry annulus inner radius, in arcsec
phot_ann_out = 300.0 #photometry annulus outer radius, in arcsec
# Carry out circular/annulus Aperture Photometry
annularPSW = annularSkyAperturePhotometry(image= mapExtendedCorrected, \
    centerRA=ra, centerDec=dec, fractional=1, \
    radiusArcsec=photrad, innerArcsec=phot_ann_in, outerArcsec=phot_ann_out)
# annularSkyAperturePhotometry() #Alternatively, define the photometry regions manually

flux = annularPSW.getTargetTotal()  # Final target brightness in Jy/pixel
print 'PSW flux = %5.3f Jy'%(flux)

 

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