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SPIRE instrument and calibration web pages

Observing with SPIRE

Overall Calibration

Calibration files for all three instruments can be obtained here:

The available calibration trees for SPIRE are listed with details here. A .jar file can be downloaded and then read in HIPE as a calibration tree like this:
cal = spireCal(jarFile="spire_cal_5_2.jar")

The calibration trees spire_cal_5_0 and above can be retrieved in HIPE from the HSA using
cal = spireCal(calTree="spire_cal_5_0") etc. The default one can be obtained with cal = spireCal(calTree="spire_cal")

SPIRE photometer

AOT release notes

Photometer point source mode

Photometer scan map mode

Photometer small scan map mode


  • SPIRE Photometer Beams: The theoretical and the observed SPIRE photometer beams are available from here. Please read the release note for more details. These are also available in the SPIRE calibration context and can be accessed in HIPE:
cal = SpireCal.getInstance()
beamPLW = cal.phot.refs["BeamProfList"].product.refs[0].product
beamPMW = cal.phot.refs["BeamProfList"].product.refs[1].product
beamPSW = cal.phot.refs["BeamProfList"].product.refs[2].product

  • SPIRE Photometer filter transmission curves: You can access the filter transmission curves (also known as Relative Spectral Response Function, RSRF) from here. These are also available in the SPIRE calibration context and can be accessed in HIPE:
cal = SpireCal.getInstance()
rsrf = cal.phot.rsrf

  • Neptune and Uranus models used for the SPIRE flux calibration: the ESA2 models currently used in the SPIRE calibration are available here.

Data processing

HIPE data processing documentation can all be found at:


  • Note that SPIRE maps are in units of Jy/beam, and are calibrated in the assumption of a point source having a spectral index equal to -1. To calibrate your data for other cases or convert to e.g. Jy/sr, please refer to the section 5.2 of the SPIRE Observers' Manual.
  • By default, the SPIRE pipeline uses a nšive map-maker. In this case, the error map is simply the standard deviation of all the data points falling into a given pixel. As a consequence, error maps contain increased errors associated with binning data from Gaussian sources, producing a torus shape; this is an artifact of the map-making process.

Tips to re-reduce your data

  • Always remember to update to the latest calibration tree compatible with the HIPE built you are using (See the SPIRE Data Reduction Guide, Chapter 3 for a detailed explanation and examples). Assuming the observation is loaded into HIPE as a variable named obs:
cal = spireCal(calTree="spire_cal")
  • If the observation you retrieved from HSA has been reduced with SPG v. 2.x or less, than start reprocessing from level 0 (i.e., run again the engineering conversion level 0 -> 0.5)
  • Main issues you might find in your data are: undetected glitches, thermistor or detector jumps, bad baseline removal.
    • Undetected glitches: you may try to play with the parameters of the waveletDeglitcher, in particular changing correlationThreshold parameter; other solution is to use the alternative sigmaKappaDeglitcher
    • Thermistor jumps: this should be automatically solved re-reducing your observation as of HIPE v. 6. If this is not the case, you must exclude the affected thermistor when running the temperatureDriftCorrection adding e.g. pswThermistorSelect='T1'
    • Failure of Temperature Drift Correction: Due to an update of the Temperature Drift Correction task in the pipeline, the pipline may fail with an Index argument 0 is out of range error if run with Calibration Tree spire_cal_6_0. Please update to use spire_cal_6_1 to solve the problem (See the SPIRE Data Reduction Guide, Chapter 3).
    • Bad baseline removal as of Hipe v. 6.x, a new polynomial fit (in comparison to the standard median) for baseline removal has been added as a prototype. Assuming that your Observation Context is stored in a variable named obs, you can call it as e.g.:
from herschel.spire.ia.pipeline.phot.baseline import BaselineRemovalPolynomialTask
baselineRemovalPolynomial = BaselineRemovalPolynomialTask()

scansBaseline = baselineRemovalPolynomial(input=obs.level1, polyDegree=3)
mapBaseline   = naiveScanMapper(input=scansBaseline, array="PLW")

Source extraction

Tests have demonstrated that a source fitter working on the detectors' timeline works better than the map-based, such as sourceExtractorDaophot or sourceExtractorSussex. The algorithm will be included in future Hipe releases in the form of a task.

For the time being, you can use the jython script written by G. Bendo it will fit a Gaussian function to the baseline-subtracted SPIRE timelines in a SpireListContext.

Example Use

This example is based on fitting the peak of Gamma Dra in ObsID 0x50005984 in the PSW band.

fitter=bendoSourceFit(inputContext)"PSW", 269.1515617, 51.488894, 200)"PSW", 269.1515617, 51.488894, 22, 300, 350)

The first line defines an instance of the fitter object.

The second line calls a method in which the data within a 200 arcsec circle centerd on RA=269.1515617 and Dec=51.488894 is fit with a Gaussian function. The default is to fit an elliptical Gaussian function with a variable background. The first parameter will be the peak flux density.

The third line calls a methods in which a background is measured within an annulus between radii of 300 and 350 arcsec and then a Gaussian function is fit to both the central 22 arcsec and the background annulus. The default function, an elliptical Gaussian function with a variable background, is still used in this case.

See the comments at the beginning of the code to learn how to select optional functions, set parameters for the fits, or get additional data based on the resulting fits (e.g. uncertainties in the best fitting parameters).

SPIRE Fourier-Transform Spectrometer (FTS)

SPIRE spectrometer AOTs

FTS point source mode (sparse)

FTS mapping mode (intermediate, full)

FTS bright source modes

FTS calibration

Important FTS information, including calibration, point source and extended source calibration etc, is available in the SPIRE Observers' Manual, Sections 4.2 and 5.3. These two sections are a must-read for anybody processing SPIRE FTS data.

FTS data processing

  • Telescope RSRFs (daily dark sky observation) are available here for HIPE 5.x and here for HIPE 6.x. These can be used directly in the user processing script. For best results, one should use the telescope RSRF derived from a daily dark taken in the day of the observation.

-- AnthonyMarston - 24 Jan 2011

Topic attachments
I Attachment History Action Size Date Who Comment
PDFpdf aa14519-10.pdf r1 manage 1321.5 K 2011-01-24 - 09:51 AnthonyMarston SPIRE A&A paper, Griifin et al 2010
PDFpdf aa14605-10.pdf r1 manage 264.1 K 2011-01-24 - 16:48 IvanV SPIRE in-flight calibration
Texttxt r1 manage 18.9 K 2011-03-16 - 14:05 LucaConversi  
Texttxt r1 manage 24.2 K 2011-04-13 - 08:45 LucaConversi  
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Topic revision: r13 - 2011-04-13 - LucaConversi
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