Difference: SpireCalibrationWeb (59 vs. 60)

Revision 602012-07-24 - IvanV

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META TOPICPARENT name="WebHome"

SPIRE instrument and calibration web pages

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Software and documentation

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  • HIPE (Herschel Interactive Processing Environment): The latest User Release HCSS version that you should use for reducing SPIRE data is HIPE v8.2.0. 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 8.0 build 3459.
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  • 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.
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  • 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 2.0. This version can be used with HIPE v8.2.0 as well as all track 8 and 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.
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  • 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.
 
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  • 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).
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    • This effect can also happen with destriped maps. In this case check if increasing the sigma or switching off the Level 2 deglitcher helps. Especially the HIPE 8 destriper should not be currently used with the Level 2 deglitcher active.
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    • This effect can also happen with destriped maps. In this case check if increasing the sigma or switching off the Level 2 deglitcher helps.
<-- Especially the HIPE 8 destriper should not be currently used with the Level 2 deglitcher active. -->
 
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  • WCS in 3-colour images
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  • 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.
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 obs.calibration.update(cal)
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  • 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, ff 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 <8.
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  • 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
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    • 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 pipeline may fail with an Index argument 0 is out of range error if run with Calibration Tree spire_cal_6_0. Please update to at least use spire_cal_6_1 to solve the problem (See the SPIRE Data Reduction Guide, Chapter 3).
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<--      * 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 pipeline may fail with an Index argument 0 is out of range error if run with Calibration Tree spire_cal_6_0. Please update to at least use spire_cal_6_1 to solve the problem (See the SPIRE Data Reduction Guide, Chapter 3). -->
 
    • 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.:
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from herschel.spire.ia.pipeline.phot.baseline import BaselineRemovalPolynomialTask
baselineRemovalPolynomial = BaselineRemovalPolynomialTask()
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 mapBaseline = naiveScanMapper(input=scansBaseline, array="PLW")
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Spectrometer data reduction

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  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)
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  1. Substitute the standard teleRsrf calibration file for one derived specifically for the OD of your observation (in the User Pipeline Script in HIPE)
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<--  3. Substitute the standard teleRsrf calibration file for one derived specifically for the OD of your observation (in the User Pipeline 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.

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In order to obtain the teleRsrf calibration file derived for the OD of your observation and valid for your HIPE/calibration tree version, please raise a Helpdesk ticket (select the "SPIRE FTS" department) specifying the observation that you are trying to process.
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<-- In order to obtain the teleRsrf calibration file derived for the OD of your observation and valid for your HIPE/calibration tree version, please raise a Helpdesk ticket (select the "SPIRE FTS" department) specifying the observation that you are trying to process. -->
 
 
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