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SPIRE instrument and calibration web pages

Introduction

This page provides up-to-date information about using the SPIRE instrument: from preparing observations to reducing your data. This page also provides you with the latest calibration accuracies and known SPIRE calibration issues.

Observing with SPIRE

Warning, important The most up to date information on instrument calibration and performance is given in the SPIRE Observers' Manual. This is the reference document used by all the rest of the SPIRE user guides (eg data reduction guide, cookbooks etc). Sometimes it may happen that outdated values are quoted in some of the documents. In such a case use the values given in the SPIRE Observers' Manual.

AOT release notes

Photometer

Spectrometer

Reducing SPIRE data

Software and documentation

  • HIPE (Herschel Interactive Processing Environment): The latest User Release HCSS version that you should use for reducing SPIRE data is HIPE v9.0.0. It can be downloaded from: http://herschel.esac.esa.int/HIPE_download.shtml. FYI: this corresponds to the so-called CIB (continuous integration build) HIPE 9.0 build 2974.

  • We also provide access to the latest stable developer build (a.k.a latest stable CIB), used by the instrument experts at the ICC.
    • Beware _These developer builds do not undergo the same in-depth testing as the user releases do. The current latest stable developer build can be found here.

  • Within HIPE you can access all the SPIRE data reduction and HIPE-use documentation. For those who wish to read the SPIRE Data Reduction Guide (SDRG) in PDF form, we provide that here: SDRG version 2.1. This version can be used with HIPE v9.0.0 as well as all track 9 of the CIBs. (Note that within the PDF version, document links will not work.) The SDRG follows the pipeline scripts (see "Cookbooks" below) and also explains what you are doing as you pipeline process.

  • SPIA: The SPIRE Photometer Interactive Analysis (SPIA) is available as a plug-in for HIPE. SPIA provides a structured GUI based access to the more intricate parts of the scan map photometer pipeline for SPIRE without the immediate need to resort to scripts. More information can be found on the SPIA web page

The SPIRE Launch Pads

  • The SPIRE Launch Pads are single sheet quick entries (like a cheat sheet) into SPIRE data reduction and providing quick references to the relevant sections in the SPIRE Data Reduction Guide. There are launch pads for Data Access, SPIRE Photometer and Spectrometer data reduction.

DocumentSorted ascending Description Download Version
SPIRE Launch Pad Data Launch Pad PDF version 1.5
SPIRE Launch Pad Photometer Launch Pad PDF version 1.6
SPIRE Launch Pad Spectrometer Launch Pad PDF version 1.6

Photometer data reduction

Maps

  • 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, i.e. νSν = const. To calibrate your data for other cases or convert to e.g. Jy/sr, please refer to section 5.2 of the SPIRE Observers' Manual .
  • By default, the SPIRE pipeline uses a näive 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 artefact of the map-making process.

Level 2.5

  • As of HIPE 6.1.1, SPIRE observations may include a new Level 2.5. This product includes maps obtained merging all contiguous observations belonging to the same program and having same observing mode (i.e. small map, large map or Parallel Mode). Maps are produced using the standard pipeline, i.e.:
    1. query the database to retrieve all the required observations
    2. merge all Level 1s
    3. remove the baseline using a median fit from each scan
    4. build the maps using the näive map-maker
  • All the photometer known issues applies to these maps as well. Moreover, note that:
    • no astrometry fix is applied, so sources may be blurred/_duplicated_ if the shift between 2 or more observations is big (>5 arcsec);
    • in merging together multiple observations of the same field, you may not notice anymore some artifacts such as undetected glitches, temperature drifts or detectors jumps. In both cases, you need to re-reduce the data with the tips suggested below.
  • The list of observations used to build the Level 2.5 maps are included in the observations' metadata.

Solar System objects

  • When the target is a Solar System Object (SSO) having a proper motion, the spacecraft re-adjusts its position after each scan, in order to always be centred on the target. However, the products retrieved from the Herschel Science Archive have been reduced using a standard pipeline which does not correct for the target's proper motion. As a result, the background of SSO observations will be focused while the SSO itself will appear blurred (for short observations or slow objects) or as a streak (for longer ones or faster objects).
  • As of HIPE 8.0, a new script named SSO_MotionCorrection is available under the SPIRE Useful Scripts menu. The script computes the SSO speed in RA & Dec coordinates, applies the required shift to Level 1 timelines, computes the corrected maps and eventually saves the modified products to a local pool. The results are maps centred on the SSO (i.e. the target will appear focused) with a smooth/blurred background. More details can be found in the SPIRE Data Reduction Guide.

Data processing known issues

  • In order to obtain the best possible Level 2 SPIRE photometry data, the observations might have to be reprocessed with the latest HIPE User Release (see above).

  • Stripes in PSW, PMW and/or PLW (Level 2) maps
    • Most of the stripes that are present in the final maps are due to a combination of thermal drifts (which in few cases are not efficiently removed) and median baseline subtraction. A similar effect is caused by very bright sources: in this case, the problem resides in the median baseline subtraction only.
    • Suggested solutions:
      • switch to a baseline subtraction using a polynomial fitting using the optional task baselineRemovalPolynomial. If there are no jumps in the timelines, you may also try to run the baseline removal on the entire timeline;
      • in the case of bright sources, you may try to mask them before running the baseline removal (either median or polynomial): you can use this script as a template.;
      • use the SPIRE Destriper: this new task is giving good results in most cases, especially for diffuse emission and extended sources. In the case of Parallel Mode observations, although the destroyer will work on single scans it is always better to merge 2 or more observations together using the map merge script within HIPE. The destriper documentation can be found on the NHSC website

  • De-glitcher masks faint sources
    • The de-glitcher is a very delicate process. In particular, for data taken in Parallel Mode (sampling at 10Hz) and at high speed (60"/s) the de-glitcher with standard parameters may flag very faint sources as glitches. Bright sources are different from glitches in that they have a Gaussian (i.e. beam/PSF) shape. For faint sources, the sampling rate could be not high enough and hence they have a "delta" shape, which is similar to a small glitch. The user might try to modify the correlation parameter to 0.95: this will decrease the number of detected glitches. In case of high scan rate and low sampling speed one may want to stay with a limited level 1 detection rate and defer to Level 2 deglitching.

  • Some sources have saturated the ADC and the corresponding data have been masked
    • There is nothing a user can do: the source was simply too bright. If the user has other sources still not observed and of the same intensity, it is suggested to change the AORs to use the bright source mode.

  • Thermistor jumps
    • As of HIPE 6.0.3, a new module together called signalJumpDetector in place to identify the jump and to exclude the affected thermistor(s).
    • This module should not be run with observations in bright mode as it can lead to too many unnecessarily excluded scans. Jumps seem not to occur in bright mode.

  • Cooler temperature variations
    • After the end of the SPIRE cooler recycle, the temperature is few mK below the plateau (i.e. the most stable value which lasts for about 40h): it takes about 7h to reach it. Between 6 to 7h after the cooler recycle ends, its temperature raises steeply and reaches the plateau. At present, the pipeline is not able to cope with these strong temperature variations (although a correction script is planned for HIPE v.9), hence observations taken during such times may exhibit stripes in the final maps (especially for extragalactic fields). To solve this, the user can try a baseline polynomial fit of order >2 on the entire baseline - or use the destriper.

  • NaNs pixels present in the PSW, PMW and/or PLW (Level 2) maps
    • This effect, related to data masked for various reasons and poor coverage (not enough redundancy), is more evident in single fast-scan Parallel Mode maps. To avoid NaNs, increase the pixel's dimension (i.e., decrease the map's resolution).
    • This effect can also happen with destriped maps. In this case check if increasing the sigma or switching off the Level 2 deglitcher helps.

  • Quality flags in the quality context
    • Currently, the quality flags at the quality context inside the observation context are just meant for HSC/ICC internal evaluation of the quality of the products and not for the users. In case the data had some serious quality problem, the PI of the program has been contacted about it. Otherwise, only information in the quality summary, when available, should concern the observers.

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")
obs.calibration.update(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). In addition, if you want to apply the extended gains correction then reprocessing of the data through the User Pipeline is required for all photometer data processed with HIPE versions earlier than v8.

  • 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
  • Bad baseline removal (see also above) 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")
    

    Spectrometer data reduction

    The best source of information for reducing SPIRE Spectrometer data is the SPIRE Data Reduction Guide available through the HIPE help. This runs through the User Pipeline scripts step by step, describes several other Useful Scripts, and offers advice for specific types of sources:

    • Faint (<10 Jy) and medium (<100 Jy) strength sources
    • Bright sources (>500 Jy)
    • Extended sources
    • H+L observations

    For faint sources, the subtraction of instrument, telescope and background emission is particularly important. Optimum subtraction can be performed in several ways (read the SPIRE Data Reduction Guide for details):

    1. Subtract the Dark Sky spectrum closest to your observation (use the "Background Subtraction" script in HIPE)
    2. Subtract the spectrum of surrounding detectors (use the "Background Subtraction" script in HIPE)

    Dark Sky observations are observed on every SPIRE Spectrometer OD, and are all public in the Archive.

    A listing of the available Dark Sky observations can be found here.

    Cookbooks

    SPIRE photometry cookbook.

    The current version of the cookbook is available here and provides practical guidelines on how to do photometry with SPIRE. HELP The cookbook is not HIPE specific.

    SPIRE calibration file versions

    Calibration files for SPIRE can be obtained here:

    The available calibration trees for SPIRE are listed below (with the current operational version at the top). More details of the changes in each version are given here.

    SPIRE Calibration Tree Applicable HIPE Version Comment
    SPIRE_CAL_9_1 HIPE v9 Final v9 cal tree, currently used in operations.
    SPIRE_CAL_8_1 HIPE v8 Final v8 cal tree
    SPIRE_CAL_7_0 HIPE v7 Final v7 cal tree.
    SPIRE_CAL_6_1 HIPE v6 Final v6 cal tree
    (SPIRE_CAL_6_0) HIPE v6 Spec major update
    SPIRE_CAL_5_2 HIPE v5 Final v5 cal tree
    (SPIRE_CAL_5_1) HIPE v5  
    (SPIRE_CAL_5_0) HIPE v5 Phot flux conv. based on Neptune. Spec major update
    SPIRE_CAL_4_0 HIPE v4 Spec point source flux conv based on Uranus
    SPIRE_CAL_3_2 HIPE v3  
    (SPIRE_CAL_3_1) HIPE v3  
    (SPIRE_CAL_3_0) HIPE v3  
    SPIRE_CAL_2_1 HIPE v2 Spec point source flux conv based on Vesta
    (SPIRE_CAL_2_0) HIPE v2  
    SPIRE_CAL_1_2 HIPE v1 Phot flux conv based on Ceres
    (SPIRE_CAL_1_1) HIPE v1 Pre-launch dummy values

    Any of the calibration trees can be retrieved in HIPE from the HSA using (e.g.) cal = spireCal(calTree="spire_cal_9_1") etc. The default (applicable to the HIPE version) can be obtained with cal = spireCal(calTree="spire_cal")

    See the SPIRE Data Reduction Guide for more details.

    SPIRE calibration and performance

    Photometer calibration

    • SPIRE Photometer Beams: These are available in the SPIRE calibration context, at the standard map pixel size of (6,10,14) arcsec/pixel for (250,350,500) µm bands, and can be accessed in HIPE after a calibration context has been loaded (see above):

    BeamPSW = cal.phot.getBeamProf('PSW')
    BeamPMW = cal.phot.getBeamProf('PMW')
    BeamPLW = cal.phot.getBeamProf('PLW')
    

    Tip, idea The observed beams at much finer scale of 1 arcsec/pixel, as well as the theoretical ones, are available from here . Please read the release note for more details.

    • 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 after a calibration context has been loaded (See above):

    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.

    Spectrometer 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.

    Interest groups and scripts

    • The following interest groups relate to processing of observations taken with SPIRE. The links provided allow subscription to these interest groups.
      • subscribe to the SPIRE Photometer interest group
      • subscribe to the SPIRE and PACS parallel mode large map and point source extraction interest group
      • subscribe to the SPIRE Spectrometer interest group
      • subscribe to the PACS, SPIRE and HIFI spectral maps interest group
    • User contributed scripts: Users are welcome to submit scripts and software that they believe could be of general interest to the community to the Herschel Helpdesk.

    Further information

    Topic attachments
    I Attachment History Action Size Date Who Comment
    PDFpdf Phot_Pipeline_Issue7.pdf r1 manage 453.9 K 2011-05-06 - 11:07 LucaConversi  
    PDFpdf Pipe_Description_2.1.pdf r1 manage 2078.9 K 2011-05-06 - 11:06 LucaConversi  
    PDFpdf SPIRE-BSS-DOC-002966_SPIRE_Spectrometer_pipeline_description_Issue_4_0_1.pdf r1 manage 793.5 K 2012-02-28 - 09:49 EdwardPolehampton  
    PDFpdf SPIREPhotometryCookbook_jul2011_2.pdf r1 manage 62.0 K 2011-08-11 - 09:27 IvanV SPIRE Photo cookbook
    PDFpdf SPIRE_DataLaunchPad_v1.5.pdf r1 manage 203.3 K 2012-02-04 - 20:49 ChrisPearson SPIRE Data Launch Pad v1.5
    PDFpdf SPIRE_PhotometerLaunchPad_v1.5.pdf r2 r1 manage 203.1 K 2012-02-10 - 11:56 ChrisPearson SPIRE Photometer Launch Pad v1.5
    PDFpdf SPIRE_PhotometerLaunchPad_v1.6.pdf r1 manage 204.4 K 2012-03-14 - 16:48 ChrisPearson SPIRE Photometer Launch Pad v1.6
    PDFpdf SPIRE_SpectrometerLaunchPad_v1.6.pdf r1 manage 192.0 K 2012-02-04 - 20:50 ChrisPearson SPIRE Spectrometer Launch Pad v1.6
    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
    PDFpdf beam_release_note_v1-1.pdf r1 manage 4044.5 K 2011-05-11 - 09:44 LucaConversi  
    Texttxt bendoSourceFit_v0_9.py.txt r1 manage 25.7 K 2011-08-10 - 09:03 IvanV  
    Texttxt maskTimelines.py.txt r1 manage 5.8 K 2011-05-27 - 10:34 LucaConversi  
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