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Document: PICC-ME-TN-037Date: April 12, 2011Version: 1.0
PACS Photometer - Point-Source Flux Calibration Page 1
PACS Photometer - Point-Source Flux Calibration
T. Müller, M. Nielbock, Z. Balog, U. Klaas, E. Vilenius
This document provides a summary of the current (ODs 108...627)
PACS photometer flux calibra-tion and describes the transition from
the previous flux calibration, originally based on
chop-nodobservations of a set of stars, asteroids and the planets
Uranus and Neptune, to the new onenow based on mini scan-maps of 5
fiducial stars (β And, α Cet, α Tau, α Boo, γ Dra) and 18large
main-belt asteroids. The stars and asteroids have been observed
multiple times in all threebands with 20′′/s scan-speed, mainly in
2 scan directions (70◦ and 110◦) and in high gain. Alldata have
been processed with software version ”hcss.dp.pacs-6.0.1932” in a
homogeneous wayand well-defined settings for the various reduction
steps. The final flux densities were derivedusing aperture
photometry and colour correction. The aperture corrections for
point-sourceshave been brought inline with the most recent
EEF-values from a revision of the PSF-analysis.The new PACS
photometer flux calibration (response file version “FM, 6”) was
determined insuch a way that the measured fluxes of all standards
agree on average with the model fluxes ofthese objects. The
effective difference in flux calibration between the old ”FM, 5”
response file(combined with the old EEF-values) and the new ”FM, 6”
response file (combined with the newEEF-values) is only 0.3% in
blue, 2.0% in green and 4.3% in red. The new ”FM, 6” fluxes
arelower by these values.Point-source observations in chop-nod
technique produce systematically different fluxes. For anagreement
with the fluxes obtained via scan-maps one has to increase the
chop-nod fluxes by4% in blue, 4% in green and 6% in red. PACS
photometer observations of point-sources below∼100 Jy (reduced in
the described way) have absolute accuracy of 3% in blue and green
bandand better than 5% in the red band. The PACS bolometers enter a
non-linear regime for point-sources above about 100 Jy in all 3
bands. The fluxes of brighter targets are underestimated,typically
by a few percent.
Contents
1 Photometric Calibration Standards 31.1 Fiducial stars . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . 3
1.1.1 Summary of mini scan-map observations . . . . . . . . . .
. . . . . . . . . . . . . . . . . . 31.1.2 Model fluxes . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . 5
1.2 Asteroids . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . 51.2.1 Summary of
mini scan-map observations . . . . . . . . . . . . . . . . . . . .
. . . . . . . . 51.2.2 Model fluxes . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3 Planets Uranus and Neptune . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . 5
2 Determination of flux densities 82.1 Data reduction and
calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . 8
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2.1.1 Pre-processing . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . 82.1.2 2-step post
processing . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 9
2.2 Photometry . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . 132.2.1 Source flux
determination . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . 132.2.2 Source flux uncertainty determination . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . 142.2.3
Colour correction . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . 14
3 Results from mini scan-maps via responsivity file “FM, 5” and
new EEF-values 15
4 Results from mini scan-maps via responsivity file “FM, 6”
164.1 Results from mini-scan map observations of the 5 fiducial
stars . . . . . . . . . . . . . . . . . . . 164.2 Results from
mini-scan map observations of 18 asteroids . . . . . . . . . . . .
. . . . . . . . . . . 18
5 Results from chop-nod observations via responsivity file “FM,
6” 215.1 Results from chop-nod observations of the 5(6) fiducial
stars . . . . . . . . . . . . . . . . . . . . . 215.2 Results from
chop-nod observations of 16 asteroids . . . . . . . . . . . . . . .
. . . . . . . . . . . 21
6 Discussion 226.1 Total accuracy of the flux calibration . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226.2
Influence of high-pass filter width and aperture size on final
fluxes . . . . . . . . . . . . . . . . . 236.3 Correlated noise . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . 236.4 Offset between chop-nod and mini scan-map
observations . . . . . . . . . . . . . . . . . . . . . . 266.5 Gain
option (chop-nod and mini scan-map observations) . . . . . . . . .
. . . . . . . . . . . . . . 266.6 Dither option (chop-nod
observations) . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . 26
7 Conclusions 26
A Search for dependencies on evironmental parameters: fiducial
star mini scan-map obser-vations (“FM, 6”) and models 26
B Search for dependencies on evironmental parameters: asteroid
mini scan-map observations(“FM, 6”) and models 32
C Overview of observations in chop-nod mode 35C.1 Fiducial star
observations in chop-nod mode . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 35C.2 Asteroid observations in chop-nod mode
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
37
D Old and new EEF-values 39D.1 Old EEF-values connected to
response calibration file ”FM, 5” . . . . . . . . . . . . . . . . .
. . 39D.2 New EEF-values connected to response calibration file
”FM, 6” . . . . . . . . . . . . . . . . . . . 42
E Data reduction scripts 43E.1 Loading of new response
calibration file . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . 43E.2 Applying aperture corrections . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43E.3
Script to process mini scan-map observations of stars and asteroids
. . . . . . . . . . . . . . . . . 44E.4 Script to process chop-nod
observations of stars and asteroids . . . . . . . . . . . . . . . .
. . . . 59E.5 Additional applied scripts . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . 67
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1 Photometric Calibration Standards
1.1 Fiducial stars
1.1.1 Summary of mini scan-map observations
All measurements were taken as part of one of the following
calibration programmes: “RPPhotFlux 321B”,“RPPhotFlux 324B”,
“RPPhotFlux 321D”, “PVPhotAOTVal 514P”. All observations were taken
in mediumscan speed with 20′′/s, in “high gain” and only one single
repetition of each scan-map.
Table 1: PACS photometer observation details for α Boo (HR 5340;
HD124897; HIP 87833; Arcturus).
filter scan-angles scan-legs notes/OD OBSIDs bands [deg] len [′]
sep [′′] no. remarks
220 1342188245, 1342188246 b/r 63/117 4.0 4.0 81342188247,
1342188248 g/r 63/117 4.0 4.0 8
414 1342199603, 1342199604 b/r 70/110 3.0 4.0 101342199606,
1342199607 g/r 70/110 3.0 4.0 10
583 1342211280, 1342211281 b/r 70/110 3.0 4.0 101342211283,
1342211284 g/r 70/110 3.0 4.0 10
Table 2: PACS photometer observation details for α Cet (HR911;
HD 18884; HIP 14135; Menkar).
filter scan-angles scan-legs notes/OD OBSIDs bands [deg] len [′]
sep [′′] no remarks
259 1342189824, 1342189825 b/r 63/117 4.0 4.0 81342189827,
1342189828 g/r 63/117 4.0 4.0 8
457 1342203030, 1342203031 b/r 70/110 3.0 4.0 101342203033,
1342203034 g/r 70/110 3.0 4.0 10
614 1342212856, 1342212857 b/r 70/110 3.0 4.0 101342212853,
1342212854 g/r 70/110 3.0 4.0 10
Table 3: PACS photometer observation details for α Tau (HR1457;
HD 29139; HIP 21421; Aldebaran).
filter scan-angles scan-legs notes/OD OBSIDs bands [deg] len [′]
sep [′′] no remarks
118 1342183532, 1342183533 b/r 45/135 5.0 51.0 4 very low
coverage1342183534, 1342183535 g/r 45/135 5.0 51.0 4 very low
coverage
118 1342183538 b/r 63 10.0 3.0 15 no cross-scan1342183541 g/r 63
10.0 3.0 15 no cross-scan
284 1342190947, 1342190948 b/r 70/110 2.5 4.0 101342190944,
1342190945 g/r 70/110 2.5 4.0 10
456 1342202961, 1342202962 b/r 70/110 2.5 4.0 101342202958,
1342202959 g/r 70/110 2.5 4.0 10
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Table 4: PACS photometer observation details for β And (HR 337;
HD 6860; HIP 5447; Mirach).
filter scan-angles scan-legs notes/OD OBSIDs bands [deg] len [′]
sep [′′] no remarks
414 1342199609, 1342199610 b/r 70/110 3.0 4.0 101342199612,
1342199613 g/r 70/110 3.0 4.0 10
617 1342212507, 1342212508 b/r 70/110 3.0 4.0 101342212504,
1342212505 g/r 70/110 3.0 4.0 10
Table 5: PACS photometer observation details for γ Dra (HR6705;
HD 164058; HIP 87833; Etamin).
filter scan-angles scan-legs notes/OD OBSIDs bands [deg] len [′]
sep [′′] no remarks
108 1342182985, 1342182987 b/r 45/135 5.0 51.0 4 very low
coverage108 1342182997, 1342182980 g/r 45/135 5.0 51.0 4 very low
coverage108 1342182986 b/r 45 30.0 4.0 15 no cross-scan
1342182986 g/r 45 30.0 4.0 15 no cross-scan191 1342187147,
1342187148 g/r 63/117 3.9 5.0 8
1342187149, 1342187150 g/r 63/117 3.9 4.0 81342187151,
1342187152 g/r 63/117 3.0 4.0 81342187153, 1342187154 g/r 63/117
3.9 2.0 161342187155, 1342187156 g/r 63/117 3.0 2.0 16
213 1342188070, 1342188071 b/r 63/117 4.0 4.0 8244 1342189187,
1342189187 b/r 63/117 4.0 4.0 8286 1342191125, 1342191126 b/r
70/110 2.5 4.0 10300 1342191958, 1342191959 b/r 70/110 2.5 4.0
10
1342191961, 1342191962 g/r 70/110 2.5 4.0 10316 1342192780,
1342192781 b/r 70/110 2.5 4.0 10345 1342195483, 1342195484 b/r
70/110 2.5 4.0 10371 1342196730, 1342196731 b/r 70/110 2.5 4.0
10400 1342198499, 1342198500 b/r 70/110 3.0 4.0 10413 1342199481,
1342199482 b/r 70/110 3.0 4.0 10
1342199512, 1342199513 b/r 70/110 3.0 4.0 101342199526,
1342199527 b/r 70/110 3.0 4.0 10
414 1342199600, 1342199601 b/r 70/110 3.0 4.0 101342199639,
1342199640 b/r 70/110 3.0 4.0 101342199655, 1342199656 b/r 70/110
3.0 4.0 10
415 1342199707, 1342199708 b/r 70/110 3.0 4.0 101342199717,
1342199718 b/r 70/110 3.0 4.0 10
456 1342202942, 1342202943 b/r 70/110 3.0 4.0 10483 1342204209,
1342204210 b/r 70/110 3.0 4.0 10511 1342206001, 1342206002 b/r
70/110 3.0 4.0 10539 1342208971, 1342208972 b/r 70/110 3.0 4.0
10566 1342210582, 1342210583 b/r 70/110 3.0 4.0 10
1342210584, 1342210585 g/r 70/110 3.0 4.0 10607 1342212494,
1342212495 b/r 70/110 3.0 4.0 10
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1.1.2 Model fluxes
Based on early analysis of Herschel observations of potential
calibration stars and presentations during theHerschel calibration
workshop in December 2010 at ESAC, we decided to use only the
following 5 fiducial starsfor the final analysis: β And, α Cet, α
Tau, α Boo, γ Dra. The monochromatic flux densities at 70.0,
100.0and 160.0µm for these 5 stars are given in Table 6. α CMa
(Sirius) is also listed in Table 6, but was excludedin the
validation process due to an obvious 160 µm excess (see Section 5.1
for more details).
Table 6: Information on the selected fiducial stars.
Monochromatic flux densities at 70.0, 100.0 and 160.0µmare given.
1Note: α CMa was excluded due to an apparent excess in the red band
(systematically seen inchop-nod and mini scan-maps).
Temp Model flux [mJy]HR HD HIP ID Name RA (J2000) Dec (J2000)
SpType [K] 70 µm 100 µm 160 µm
337 6860 5447 β And Mirach 01:09:43.9236 +35:37:14.008 M0III
3880 5594 2737 1062911 18884 14135 α Cet Menkar 03:02:16.8
+04:05:24.0 M1.5IIIa 3740 4889 2393 928
1457 29139 21421 α Tau Aldebaran 04:35:55.2387 +16:30:33.485
K5III 3850 14131 6909 26775340 124897 69673 α Boo Arcturus
14:15:39.6720 +19:10:56.677 K1.5III 4320 15434 7509 28916705 164058
87833 γ Dra Etamin 17:56:36.3699 +51:29:20.022 K5III 3960 3283 1604
6212491 48915 32349 α CMa Sirius1 06:45:08.92 -16:42:58.0 A1V 10150
2955 1427 545
1.2 Asteroids
1.2.1 Summary of mini scan-map observations
All measurements were taken as part of the following calibration
programmes “RPPhotFlux 324B”, “PVPho-tAOTVal 514L”, “RPPhotFlux
631A”, “RPPhotSpatial 314A”. All observations were taken in medium
scanspeed with 20′′/s, in “high gain” and only one single
repetition of each scan-map (exception: 19 Fortuna in OD132 was
taken with 4 repetitions).
1.2.2 Model fluxes
Asteroid model predictions are provided by Thomas Müller (MPE,
[email protected]). The thermophysicalmodel and the key input
parameters are listed in the publications by Müller & Lagerros
(1998 A&A...338..340M;2002 A&A...381..324M). The model
predictions for chop-nod observations and the model predictions for
miniscan-map observations for the specific epochs are summarised in
the asteroid model summaries on the PACSinternal twiki-pages1.
Please note that not all asteroid models are of the same quality.
The models of Diotimaand Carlova might be off by up to 20%. Most of
the other asteroid models should be accurate on a 10% level,the
brightest ones (Ceres, Pallas, Juno, Vesta) and Lutetia are
accurate on a 5% level. Ceres is already in thenon-linear regime of
the PACS bolometers: the fluxes derived from these measurements are
typically 0-10% toolow, depending on the band.
1.3 Planets Uranus and Neptune
The planets Uranus and Neptune have flux densities well above
100 Jy in all three bands. This flux regime isclearly outside the
linear bolometer range. We have therefore not included these two
planets in our analysis.After a careful validation exercise of the
non-linearity correction, which became available in early 2011,
thesetargets might also be used for flux calibration purposes in
the high flux regime at a later stage.
1http://herschel.esac.esa.int/twiki/bin/view/Pacs/PacsPhotFluxCalibrationSources
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Table 7: PACS photometer mini scan-map observation details for
the asteroids. The 19 Fortuna observationswere taken with 4
repetitions, all others with only 1 repetition of each mini
scan-map.
filter scan-angles scan-legsAsteroid OD OBSIDs bands [deg] len
[′] sep [′′] no.
1 Ceres 286 1342191130, 1342191131 b/r 70/110 2.5 4.0
101342191133, 1342191134 g/r 70/110 2.5 4.0 10
485 1342204324, 1342204325 b/r 70/110 3.0 4.0 101342204327,
1342204328 g/r 70/110 3.0 4.0 10
2 Pallas 245 1342189264, 1342189265 b/r 63/117 4.0 4.0
81342189266, 1342189267 g/r 63/117 4.0 4.0 8
446 1342202076, 1342202077 b/r 70/110 3.0 4.0 101342202079,
1342202080 g/r 70/110 3.0 4.0 10
3 Juno 221 1342188360, 1342188361 b/r 63/117 4.0 4.0
81342188362, 1342188363 g/r 63/117 4.0 4.0 8
593 1342211812, 1342211813 g/r 70/110 3.0 4.0 101342211815,
1342211816 b/r 70/110 3.0 4.0 10
4 Vesta 345 1342195470, 1342195471 b/r 42.4/317.6 10.0 3.0
151342195472, 1342195473 b/r 42.4/317.6 10.0 3.0 151342195474,
1342195475 g/r 42.4/317.6 10.0 3.0 151342195476, 1342195477 g/r
42.4/317.6 10.0 3.0 15
348 1342195624, 1342195625 b/r 70 /110 2.5 4.0 101342195627,
1342195628 g/r 70 /110 2.5 4.0 10
6 Hebe 413 1342199515, 1342199516 b/r 70/110 3.0 4.0
101342199518, 1342199519 g/r 70/110 3.0 4.0 10
579 1342211153, 1342211154 b/r 70/110 3.0 4.0 101342211156,
1342211157 b/r 70/110 3.0 4.0 10
8 Flora 566 1342210639, 1342210640 g/r 70/110 3.0 4.0
101342210642, 1342210643 b/r 70/110 3.0 4.0 10
19 Fortuna 132 1342184287, 1342184288 b/r 45/45 5.0 30.0 1020
Massalia 221 1342188348, 1342188349 b/r 63/117 4.0 4.0 8
1342188350, 1342188351 g/r 63/117 4.0 4.0 821 Lutetia 221
1342188334, 1342188335 g/r 63/117 4.0 4.0 8
1342188336, 1342188337 g/r 63/117 4.0 4.0 8400 1342198492,
1342198493 g/r 70/110 3.0 4.0 10
1342198494, 1342198495 b/r 70/110 3.0 4.0 10
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Table 8: PACS photometer mini scan-map observation details for
the asteroids (con’t).
filter scan-angles scan-legsAsteroid OD OBSIDs bands [deg] len
[′] sep [′′] no.
29 Amphitrite 497 1342205033, 1342205034 b/r 70/110 3.0 4.0
101342205036, 1342205037 g/r 70/110 3.0 4.0 10
47 Aglaja 245 1342189258, 1342189259 b/r 63/117 4.0 4.0
81342189260, 1342189261 g/r 63/117 4.0 4.0 8
512 1342206032, 1342206033 b/r 70/110 3.0 4.0 101342206034,
1342206035 g/r 70/110 3.0 4.0 10
52 Europa 286 1342191111, 1342191112 g/r 70/110 2.5 4.0
101342191114, 1342191115 b/r 70/110 2.5 4.0 10
54 Alexandra 400 1342198509, 1342198510 b/r 70/110 3.0 4.0
101342198511, 1342198512 g/r 70/110 3.0 4.0 10
65 Cybele 221 1342188354, 1342188355 b/r 63/117 4.0 4.0
81342188356, 1342188357 g/r 63/117 4.0 4.0 8
456 1342202949, 1342202950 b/r 70/110 3.0 4.0 101342202951,
1342202952 g/r 70/110 3.0 4.0 10
88 Thisbe 469 1342203465, 1342203466 g/r 70/110 3.0 4.0
101342203467, 1342203468 b/r 70/110 3.0 4.0 10
93 Minerva 484 1342204236, 1342204237 g/r 70/110 3.0 4.0
101342204238, 1342204239 b/r 70/110 3.0 4.0 10
423 Diotima 285 1342191020, 1342191021 b/r 70/110 2.5 4.0
101342191023, 1342191024 g/r 70/110 2.5 4.0 10
704 Interamnia 446 1342202081, 1342202082 b/r 70/110 3.0 4.0
101342202083, 1342202084 g/r 70/110 3.0 4.0 10
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2 Determination of flux densities
2.1 Data reduction and calibration
2.1.1 Pre-processing
The data reduction was done with software version
”hcss.dp.pacs-6.0.1932” and the following steps, parametersand
calibration file versions:
• flagging of bad pixels (badPixelMask: FM, 5)
• flagging of saturated pixels (clSaturationLimits: FM, 1;
satLimits: FM, 2)
• conversion digital units to Volts
• adding of pointing and time information
• response calibration (responsivity: FM, 5)
• flat fielding (flatField: FM, 3)
• extension of valid frames with which apparently are still
taken at constant scan speed: extendBBID with8 frames at the start
of each scan leg and 15 frames at the end. Note, that this should
be replacedeventually by a true selection on scan speed (frames =
filterOnScanSpeed(frames, lowScanSpeed,highScanSpeed, copy=None))
which will be available on track 7 releases2.
• MMTdeglitching (nsigma=5, nscale=3) with masking of the source
center (maskthreshold 0.01 Jy/pixelwhich masks only the brightest
parts of the source), see Fig. 1;
• no second level deglitching
• save the observation context into a fits-file (including still
all frames)
Extraction of the CalTree from the processing:
PacsCalPhot Calibration Products:absorption : FM,
2arrayInstrument : FM, 6calSources : FM, 1clTransferFunction : FM,
1corrZeroLevel : FM, 3crosstalkMatrix : FM, 2detectorSortMatrix :
FM, 3filterTransmission : FM, 1flatField : FM, 3gain : FM, 1masks :
FM, 1noisePerPixel : FM, 1photometricStabilityThreshold : FM,
1responsivity : FM, 5satLimits : FM, 2subArrayArray : FM, 5timedep
: FM, 13
2maybe already in future track 6 releases
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Figure 1: MMT deglitching masks for the 21 Lutetia observations
from OD400. Top: OBSIDs 1342198494 (70◦
scan angle, blue/red bands) & 1342198495 (110◦ scan angle,
blue/red bands); Bottom: OBSIDs 1342198492(70◦ scan angle,
green/red bands) & 1342198493 (110◦ scan angle, green/red
bands).
2.1.2 2-step post processing
The two steps are performed one after the other. The purpose of
the first step is to create a map to locate andmask the source,
while the second steps includes the final high-pass filtering of
the data.
First step (to locate the source and to establish a reliable
mask for the second step high-pass filtering):
• set on-target flag for all frames to ’true’
• hp-filtering with a hp-filter width of 15, 20, 35 in blue,
green and red respectively, corresponding to width
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of 31, 41, 71 (2×hp-parameter +1) frames which is adjusted to
the FWHM of the corresponding PSFsand masking of signals as for the
MMT-deglitching (threshold 0.01 Jy/map pixel), see Fig. 1;
• merging frames (join) of both scan directions (usually 70◦ and
110◦ scan angles in instrument frame:approx. along the diagonals of
the bolometer array); note, that for stars this is done in the
non-movingsky reference frame, while for asteroids it is done in
the co-moving object reference frame.
• selection of data by BBID (using the slightly extended scans
with 8/15 frames added on each side)
• photProject (only selected by BBID, reduced pixel sizes of
1.1′′, 1.4′′, 2.1′′) (to have the map pixelssampling about the same
fraction of the PSF)
Second step (final high-pass filtering and final
projection):
• set on-target flag for all frames to ’true’
• high-pass filtering with a hp-filter width of 15, 20, 35 in
blue, green and red respectively (adjusted to theFWHM of the
corresponding PSFs) and masking of signals above 0.6 mJy in blue
and green and above1.0mJy per map-pixel in the red band
(hp-filtering is done on all frames, including data from the
satelliteturn-arounds), see Fig. 2
• merging frames (join) of both scan directions (usually 70◦ and
110◦ scan angles in instrument frame:approx. along the diagonals of
the bolometer array)
• selection of data by BBID (slightly extended scans with
8/15)
• final projection of all data with photProject(), using the
default pixel fraction (pixfrac = 1.0) andreduced pixel sizes of
1.1′′, 1.4′′, 2.1′′
• save final map as fits-file (see Fig. 3)
Aspects related to the high-pass filter width:
• the satellite scan-speed for all measurements here was
20′′/s
• the bolometer data are taken with 40 Hz with an onboard
averaging of 4 frames in both channels in thePACS prime mode (this
is different for the PACS/SPIRE parallel mode!), leading to a data
rate of 10 Hzin the downlink
• the FWHM of a point-sources is about 5.6′′ in blue, 6.8′′ in
green and 11.3′′ (average values for 20′′/sscan speed,
PICC-ME-TN-033, v1.01)
• in a signal time-line for a given pixel a central hit of the
source has therefore the following width: 5.6′′ /20′′/s = 0.28 s or
2.8 frames in blue, 6.8′′ / 20′′/s = 0.34 s or 3.4 frames in green,
11.3′′ / 20′′/s = 0.565 sor 5.65 frames in red
• the high-pass filter widths are 15, 20, 35, corresponding to
31, 41, 71 frames (2×hp-parameter +1)
• the ratios between FWHM and high-pass filter width are
2.8/31=0.09 in blue, 3.4/41=0.08 in green and5.65/71=0.08 in
red
• these ratios are very similar in the three bands and at a very
conservative level so that the high-passfiltering is not ”damaging”
the source flux with the appropriate masking
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Figure 2: High-pass filter masks for the 21 Lutetia observations
from OD400. Top: combined OBSIDs1342198494 & 1342198495 (left:
blue; right: red); Bottom: combined OBSIDs 1342198492 &
1342198493(left: green; right: red).
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Figure 3: Final maps for the 21 Lutetia observations from OD400.
Top: combined OBSIDs 1342198494 &1342198495 (left: blue; right:
red); Bottom: combined OBSIDs 1342198492 & 1342198493 (left:
green; right:red).
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2.2 Photometry
2.2.1 Source flux determination
• no background subtraction (because of the HP filtering, the
background is set at 0 artificially so there isno reason to correct
for it)
• aperture photometry (see Fig. 5 for an example) with aperture
sizes of 12′′, 12′′, 22′′ in blue, green,red, respectively
(centering the aperture on the peak flux). These values are based
on an analysis ofthe influence of the high-pass filter width on
aperture photometry (see Sect. 6.2). It shows that a smallaperture
is better, as long as the aperture correction is applied. The
larger the aperture, the morethe photometry will depend on the
filter width. These apertures are chosen such that the
uncertaintybecause of the high-pass filter width is less than 1%.
This approach has the advantage that the resultscan be relatively
independent of high-pass filter width. Note, that at lower flux
levels one should selectsignificantly smaller aperture sizes!
• apply aperture correction factors in blue/green/red:- blue
band, 12′′ aperture radius: 0.794 (true flux is about 26%
larger)
- green band, 12′′ aperture radius: 0.766 (true flux is about
31% larger)
- red band, 22′′ aperture radius: 0.810 (true flux is about 23%
larger)
These new values are based on very large maps (extending to 15′
from a bright source) indicating thatabout 10% more flux is in the
far away wings of the PSFs beyond 60′′. Note, that the previous
values,connected to earlier versions of PSFs (and assuming that
there is no flux beyond 60′′) and to the sameaperture sizes, were:
0.886 (blue band, 12′′ aperture radius), 0.866 (green band, 12′′
aperture radius), and0.916 (red band, 22′′ aperture radius). These
values were correct for responsivity calibration file (FM,5),but
are not correct for later versions (see
http://herschel.esac.esa.int/twiki/pub/Public//PacsCalibrationWeb/PhotMiniScan
ReleaseNote 20101112.pdf).
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2.2.2 Source flux uncertainty determination
• background for noise estimate is taken inside a sky annulus
with sizes 20-30′′ in blue/green and 30-40′′in red: signal r.m.s.
inside the selected sky annulus
• correction for the correlated noise (rms-noise values have to
be divided by these factors), see also Sec-tion 6.3 where these
relations are determined and discussed:
- blue band: 0.95× (pixsize/3.2)1.68; here: 0.95× (1.1/3.2)1.68
= 0.157983(→ noise increases by a factor of 6.3)
- green band: 0.95× (pixsize/3.2)1.68; here: 0.95× (1.4/3.2)1.68
= 0.236901(→ noise increases by a factor of 4.2)
- red band: 0.88× (pixsize/6.4)1.73; here: 0.88× (2.1/6.4)1.73 =
0.128006(→ noise increases by a factor of 7.8)
• determine the error of the source flux:corrected r.m.s.values
×
√(number of map pixel inside the specified aperture) / aperture
correction
• numbers of pixels inside the specified apertures:- blue:
(19.34)2 = 373.9 pixel of size 1.1′′ inside the 12′′ radius
aperture;
- green: (15.19)2 = 230.8 pixel of size 1.4′′ inside the 12′′
radius aperture;
- red: (18.57)2 = 344.8 pixel of size 2.1′′ inside the 22′′
radius aperture;
Note, that the source flux uncertainties in case of the fiducial
stars and the asteroids are very small. S/N valuesare well above
100. The error bars throughout this report are therefore in most
cases smaller than the symbolsizes.
2.2.3 Colour correction
The following colour correction factors have been used to obtain
monochromatic flux densities and uncertaintiesat 70.0, 100.0,
160.0µm:
Objects 70.0µm 100.0 µm 160.0 µm
stars 1.016 1.033 1.074asteroids 1.00 1.02 1.07Vesta 1.00 1.03
1.07
The observed flux (FD) has to be divided by the colour
correction factors given in the above table to obtainmonochromatic
flux densities at the PACS bolometer reference wavelengths of 70.0,
100.0 and 160.0 µm:
FDcc = FD/ccAn overview of colour corrections for different
types of sources is given in PICC-ME-TN-038 (March 2011) andin
Poglitsch et al. (2010, A&A 518L, 2P).The colour corrections
for the stars are based on a 4000 K black-body, very close to the
effective temperatureof the stars (see Tbl. 6), ranging between
3740K and 4320K. The corrections for the asteroids are based onTPM
predictions for typical main-belt objects at the Herschel-relevant
observing and illumination geometry.Note that different solar
system objects might require slightly different colour corrections.
The most extremecase in our sample is Vesta with its very high
albedo.
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3 Results from mini scan-maps via responsivity file “FM, 5”
andnew EEF-values
Using the newly derived EEF-values from PICC-ME-TN-033 (v. 1.01,
November 3, 2010) to correct the fluxesderived on basis of the
responsivity calibration file “FM, 5” we obtained the following
results for the fiducialstars:
Table 9: Observed and calibrated (“FM, 5”) monochromatic flux
densities at 70, 100, 160 µm divided by thecorresponding model
predictions for all 5 fiducial stars.
Target blue obs/model green obs/model red obs/modelname no. med.
mean stdev no. med. mean stdev no. med. mean stdev
β And 2 — 1.132 0.017 2 — 1.166 0.012 2 — 1.169 0.022α Cet 3 —
1.132 0.009 3 — 1.159 0.006 3 — 1.179 0.033α Tau 4 — 1.106 0.013 4
— 1.127 0.009 5 — 1.147 0.014α Boo 3 — 1.120 0.013 3 — 1.151 0.011
3 — 1.182 0.014γ Dra 24 1.103 1.101 0.011 9 1.151 1.143 0.017 30
1.193 1.195 0.051
mean/stdev 1.119±0.014 1.151±0.015 1.174±0.017
The derived mean values have been used to establish a new
responsivity file “FM, 6”. Note that we have usedthe median value
for γ Dra since this target was also used to monitor a complete
bolometer cold cycle andtherefore includes extremes of the
bolometer temperatures.These ratios of 1.119, 1.151 and 1.174
reflect the following aspects:
1. The transition from the old EEF-values (assuming that there
is no flux beyond 60′′ and re-centering offrames on the source) to
the new EEF-values based on a re-evaluation of the measured PSFs
out to 15′
and without recentering the individual images produces ratios of
1.116, 1.131, 1.131 in blue, green andred, respectively.
2. The transition from a calibration based on chop-nod
observations only to the new calibration based onmini scan-maps
only (around 4%, 4%, 6% in blue, green and red).
3. A small offset in the old responsivity calibration file of a
few percent with respect to the 5 fiducial starmodels: the ”FM, 5”
calibration was based a larger set of stars and only very few
asteroids, includingalso poor calibrators (as β Peg, β UMi, α CMa
and 2 or 3 poor asteroid calibrators).
The effective difference in flux calibration between the old
”FM, 5” response file (combined with the old EEF-values) and the
new ”FM, 6” response file (combined with the new EEF-values) is
only 0.3% in blue, 2.0values.
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4 Results from mini scan-maps via responsivity file “FM, 6”
Based on the above analysis of the 5 fiducial stars, repeatedly
observed in mini scan-maps in all 3 bands, theresponse calibration
file has been adjusted in order to obtain a perfect match with the
model predictions forthese stars.
4.1 Results from mini-scan map observations of the 5 fiducial
stars
The new responsivity file “FM, 6” was validated against the same
sample of fiducial star observations (seeTables 1, 2, 3, 4, 5)
without modifications in the reduction steps.
Table 10: Observed and calibrated (“FM, 6”) monochromatic flux
densities at 70, 100, 160µm divided by thecorresponding model
predictions for all 5 fiducial stars.
Target blue obs/model green obs/model red obs/modelname no. med.
mean stdev no. med. mean stdev no. med. mean stdev
β And 2 — 1.015 0.015 2 — 1.010 0.010 2 — 0.989 0.018α Cet 3 —
1.015 0.008 3 — 1.004 0.005 3 — 0.995 0.020α Tau 4 — 0.991 0.011 4
— 0.976 0.009 5 — 0.974 0.010α Boo 3 — 1.004 0.012 3 — 0.997 0.009
3 — 1.003 0.012γ Dra 24 0.989 0.988 0.010 9 0.997 0.990 0.016 30
1.006 1.011 0.038
mean/stdev 1.003±0.013 0.997±0.013 0.993±0.013
On average, the mini scan-map observations of the 5 fiducial
stars agree within 1.3% in all 3 bands with thecorresponding model
predictions (giving each of the 5 stars the same weight).The
overall standard deviations of all observation/cc/model-values (cc:
colour-correction factor) in the aboveanalysis are:
• blue band, 36 independent observations: stdev = 0.014
• green band, 21 independent observations: stdev = 0.016
• red band, 43 independent observations: stdev = 0.035
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Figure 4: Comparison of fiducial star observations and models as
a function of model flux.
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4.2 Results from mini-scan map observations of 18 asteroids
Figure 5: Aperture photometry on asteroid (3) Juno, observed in
mini scan-map technique. On the right sidethe growth-curve as well
as the radial profile are shown.
The relevant information for the asteroid observations taken in
mini scan-map mode are listed in Tables 7and 8. Similar to the
fiducial star observations, the mini scan-map parameters vary for
some observationstaken in early mission phases, mainly related to
scan-leg length, separation and number of scan legs. But
allmeasurements have been taken with a satellite scan speed of
20′′/s and in high gain. The data reduction wasdone in a similar
way with the same settings for the key reduction steps, but in the
object’s reference frameinstead of the sky-frame (to account for
the apparent motion of the moving target).
Table 11: Observed, calibrated (“FM, 6”) and colour-corrected
monochromatic flux densities at 70, 100, 160µmdivided by the
corresponding model predictions for all 18 asteroids.
No. of blue obs/model green obs/model red obs/modelasteroids no.
med. mean stdev no. med. mean stdev no. med. mean stdev
all 18 ast. 79 1.006 1.003 0.068 83 0.988 0.994 0.059 184 0.995
0.995 0.058without 423 76 1.009 1.011 0.059 80 0.992 1.001 0.046
177 0.997 1.001 0.050high quality ast. 53 1.012 1.014 0.036 53
0.999 1.003 0.036 119 0.997 0.996 0.042
Some asteroids turned out to be problematic in the current
analysis:
• 19 Fortuna (OD 132): wrong scan-direction setting in one
OBSID; large separation of scan legs
• 21 Lutetia: tracking correction did not work correctly
(elongated object in final map)
• 29 Amphitrite (OD 497, green band): automatic centering of the
photometry aperture was off
• 47 Aglaja: showing larger than normal scatter in the obs/model
ratios (due to model problems?)
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• 65 Cybele: showing a very large scatter in the obs/model
ratios (due to model problems?)
• 88 Thisbe: low quality model (poor input data and shape
model)
• 93 Minerva: showing larger than normal scatter in the
obs/model ratios (due to model problems?)
• 423 Diotima: poor model prediction (also confirmed by
Akari)
For the results in Table 11 in the line ”high quality sample”
these 8 asteroids have been excluded. The agreementbetween
observations and models is confirming the validity of the new
response file (FM, 6). The larger scatterbetween observations and
models in case of the asteroids is due to model shortcomings (shape
not well known,rotational properties not well known, uncertainties
in absolute size and albedo).
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Figure 6: Comparison of asteroid observations and models as a
function of model flux.
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5 Results from chop-nod observations via responsivity file “FM,
6”
The new responsivity file “FM, 6” was also checked against
fiducial star observations (see list of observations inAppendix
C.1) and asteroid observations (see list of observations in
Appendix C.2) taken in chop-nod technique.The observations were
reduced in a standard way (now with the responsivity calibration
file “FM, 6”), followedby aperture photometry with aperture radii
of 10′′, 10′′ and 14′′ in blue, green and red, respectively.
Thecorresponding correction factors based on the new EEF-values
from the re-evaluated PSF measurements are0.765 (blue), 0.717
(green) and 0.705 (red). In case of taking exactly the same
aperture radii as in the miniscan-maps the changes in photometry
would be less than 1%.
5.1 Results from chop-nod observations of the 5(6) fiducial
stars
Table 12: Observed and calibrated (“FM, 6”) monochromatic flux
densities at 70, 100, 160µm divided by thecorresponding model
predictions for all 5(6) fiducial stars. 1: α CMa was excluded due
to an apparent excessin the red band (systematically seen in
chop-nod and mini scan-maps).
Target blue obs/model green obs/model red obs/modelname no. med.
mean stdev no. med. mean stdev no. med. mean stdev
β And 2 — 0.976 0.011 2 — 0.969 0.006 4 — 0.925 0.005α Cet 3 —
0.968 0.006 3 — 0.967 0.002 6 — 0.946 0.018α Tau 4 — 0.951 0.029 5
— 0.952 0.026 9 — 0.926 0.012α Boo 3 — 0.951 0.003 3 — 0.955 0.003
6 — 0.946 0.007γ Dra 26 0.950 0.954 0.020 5 0.958 0.958 0.023 31
0.939 0.941 0.041α CMa1 3 — 0.927 0.032 2 — 0.959 0.028 5 — 1.017
0.026
mean/stdev 0.959±0.012 0.960±0.007 0.936±0.010
For all fiducial stars and in all 3 bands the derived aperture-
and colour-corrected fluxes are about 4-7% too low(as compared to
the model predictions). The reason for the discrepancy between
chop-nod and mini scan-mapsis not known. One possible explanation
are slightly different PSF shapes in these two modes.
5.2 Results from chop-nod observations of 16 asteroids
Table 13: Observed, calibrated (“FM, 6”) and colour-corrected
monochromatic flux densities at 70, 100, 160µmdivided by the
corresponding model predictions for all 16 asteroids.
No. of blue obs/model green obs/model red obs/modelasteroids no.
med. mean stdev no. med. mean stdev no. med. mean stdev
all 16 asteroids 23 0.986 0.978 0.071 23 0.958 0.963 0.062 45
0.947 0.952 0.056without 423 Diotima 22 0.986 0.987 0.058 22 0.961
0.972 0.048 43 0.951 0.960 0.041high quality sample 15 0.986 0.981
0.044 15 0.961 0.965 0.040 29 0.946 0.956 0.036
Some asteroids turned out to be problematic in the current
analysis:
• 1 Ceres: seems to be affected by non-linear detector
response
• 10 Hygiea: lower quality model
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Figure 7: Aperture photometry on asteroid (360) Carlova,
observed in chop-nod technique. On the right sidethe growth-curve
as well as the radial profile are shown.
• 21 Lutetia: lower quality old model
• 65 Cybele: showing a very large scatter in the obs/model
ratios (due to model problems?)
• 360 Carlova: low quality model
• 423 Diotima: poor model prediction (also confirmed by
Akari)
For the results in Table 13 in the line ”high quality sample”
these 6 asteroids have been excluded. Theagreement between
observations and models is confirming the results found for the
fiducial stars: the chop-nodmode underestimates the fluxes by about
4/4/6% in blue/green/red. The larger scatter between
observationsand models in case of the asteroids is due to model
shortcomings (shape not well known, rotational propertiesnot well
known, uncertainties in absolute size and albedo).
6 Discussion
6.1 Total accuracy of the flux calibration
The absolute accuracy of the flux calibration is based on the
measured absolute standard deviation of theobs/model-values of all
independent fiducial star observations:
• blue band, 36 independent observations, 5 fiducial stars:
stdev = 0.014
• green band, 21 independent observations, 5 fiducial stars:
stdev = 0.016
• red band, 43 independent observations, 5 fiducial stars: stdev
= 0.035
The individual stellar models have an absolute accuracy of 5% in
the PACS wavelength range.
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The total obtained absolute flux accuracy in the 3 PACS bands is
therefore derived as:
• blue band at 70.0 µm:√
1.42 + (5.0/√
5.0)2 = 2.64%
• green band at 100.0 µm:√
1.62 + (5.0/√
5.0)2 = 2.75%
• red band at 160.0µm:√
3.52 + (5.0/√
5.0)2 = 4.15%
It is worth to emphasize again that the data reduction details
do play a very important role for the final accuracyof the fluxes.
Main players are:
• high-pass filter width
• masking of the source for deglitching and high-pass
filtering
• aperture size for the final photometry
• drizzling (if pixfrac
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Figure 8: Influence of highpass filter on photometry.
from the empirical fit equations given in the plots, which serve
as a guideline. Canonical values of pixel sizesused in projected
maps are 1′′ for the blue and 2′′ for the red detector. Assuming
these numbers, the truenoise per pixel is decreased by factors of
7.4 and 8.5, respectively. A detailed study on the correlated
noisebehaviour and the influencing parameters “drop-size”,
high-pass filter width and output pixel size can be foundin
Casertano et al. (2000), AJ 120, Appendix (2821-2824) and Fruchter
& Hook (2002), PASP 114, 144-152.
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Figure 9: The ratio between measured and theoretical rms-values
as a function of pixel size in the final maps.Top: blue filter;
bottom: red filter. All numbers have been calculated for the
default drop size (pixfrac=1.0).
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6.4 Offset between chop-nod and mini scan-map observations
Observations of point-sources taken in chop-nod mode produce
fluxes which are systematically lower by 4% (blueband), 4% (green
band) and 6% (red band). The most likely reason for the discrepancy
is the different distribu-tion of encircled energy in the two
different modes. The signal modulation due to the scanning is
certainly dif-ferent to the chop-nod modulation, combined with the
detector time constants and the effects of final projectionof the
measured fluxes onto the sky this can explain the apparent flux
underestimation in the chop-nod tech-nique. Meanwhile most of the
point-source observations are done in mini scan-map technique (see
release
note:http://herschel.esac.esa.int/Docs/AOTsReleaseStatus/PACS
PhotMiniScan ReleaseNote 12Nov2010.pdf) whichwas one of the main
drivers for switching from chop-nod based calibration to a
calibration connected to miniscan-maps of point-sources.
6.5 Gain option (chop-nod and mini scan-map observations)
Several measurements have been taken in low gain, either
intentionally or by mistake. In general, these lowgain measurements
on our calibrators give the same flux conversion factor as the high
gain measurements (butthe statistics is very small). Only at very
low fluxes the low gain measurements might have suffer from the
notoptimised dynamic range.
6.6 Dither option (chop-nod observations)
The dither option is a more critical parameter. At high flux
levels (e.g., asteroid (360) Carlova) the ditheringis not relevant
and the flux is very reliable, but at lower level (e.g., the red
band measurements of δ Dra fromOD 108, the obtained flux values
differ by more than a factor of 2! At intermediate fluxes (above a
few hundredmJy) the dither option is also not very critical: e.g.,
γ Dra from OD 108. The dither option should be used inall chop-nod
observations to obtain reliable fluxes!
7 Conclusions
We are in very good shape!
The flux calibration derived from the 5 fiducial stars agrees
within better than 1% with the response derived viaa sample of
asteroids (see Table 11). Since both types of calibrators have very
different SED-shapes and verydifferent flux levels at
NIR/MIR-wavelength, we can exclude NIR/MIR filter leaks with very
high confidence.The analysis of stars and asteroids is based on a
careful datareduction described in Section 2.1 which works
nicelyfor sources of intermediate fluxes. In case of fainter
point-sources we would recommend several modificationsin this
procedure:
• lower thresholds for the high-pass filtering (see also
corresponding ipipe-scripts)
• smaller aperture sizes for the photometry: 5, 4, 5 pixels in
blue, green, red (corresponding to 5.5′′, 5.6′′and 10.5′′ aperture
radii) are a good choice
• frame selection for the final map production via speed
selection (e.g., including all frames where thesatellite speed was
above 10′′/s)
A Search for dependencies on evironmental parameters:
fiducialstar mini scan-map observations (“FM, 6”) and models
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Figure 10: Comparison of fiducial star observations and models
as a function of OD.
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Figure 11: Comparison of fiducial star observations and models
as a function of differential CalBlock-signal(squares: γDra).
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Figure 12: Comparison of fiducial star observations and models
as a function of time since last recycling (squares:γDra).
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Figure 13: Comparison of fiducial star observations and models
as a function of evaporator temperature (squares:γDra).
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Figure 14: Comparison of fiducial star observations and models
as a function of primary mirror temperature.
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B Search for dependencies on evironmental parameters:
asteroidmini scan-map observations (“FM, 6”) and models
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Figure 15: Comparison of asteroid observations and models as a
function of operational day (OD).
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Figure 16: Comparison of asteroid observations and models as a
function of asteroid number.
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C Overview of observations in chop-nod mode
C.1 Fiducial star observations in chop-nod mode
The observations were taken from the following calibration
programmes:“PV/RPPhotFPG 261G”, “PV/RPPhotFPG 261H”,
“PV/RPPhotAOTVal 511A”, “PV/RPPhotFlux 321C”,“PV/RPPhotSpatial
314B”, “PV/RPPhotFlux 324A”, “PV/RPPhotFlux 321A”and include
measurements taking with dithering (Dith=1), without dithering
(Dith=0), single repetitions (nod-pattern A-B), 3 repetitions
(nod-pattern A-B-B-A-A-B), low and high gain.
OD OBSID Target Filter Gain Dith
Rep_________________________________________________________72(x)
1342180683 gammaDra blue Low 1 172(x) 1342180683 gammaDra red Low 1
192(x) 1342182100 alphaBoo blue Low 1 192(x) 1342182100 alphaBoo
red Low 1 192(x) 1342182181 alphaBoo blue Low 1 192(x) 1342182181
alphaBoo red Low 1 1108 1342182990 gammaDra blue High 1 1108
1342182990 gammaDra red High 1 1108 1342182991 gammaDra blue High 0
1108 1342182991 gammaDra red High 0 1108 1342182993 gammaDra blue
High 1 1108 1342182993 gammaDra red High 1 1108 1342182994 gammaDra
green High 0 1108 1342182994 gammaDra red High 0 1108 1342182995
gammaDra green High 1 1108 1342182995 gammaDra red High 1 1108
1342182996 gammaDra green High 1 1108 1342182996 gammaDra red High
1 1118 1342183530 alphaTau blue High 1 1118 1342183530 alphaTau red
High 1 1118 1342183531 alphaTau green High 1 1118 1342183531
alphaTau red High 1 1118 1342183536 alphaTau blue High 1 1118
1342183536 alphaTau red High 1 1118 1342183537 alphaTau green High
1 1118 1342183537 alphaTau red High 1 1132 1342184285 alphaTau
green High 1 1132 1342184285 alphaTau red High 1 1132(x) 1342184286
alphaTau green Low 1 1132(x) 1342184286 alphaTau red Low 1 1161
1342186191 gammaDra blue High 1 1161 1342186191 gammaDra red High 1
1213 1342188069 gammaDra blue High 1 1213 1342188069 gammaDra red
High 1 1220 1342188243 alphaBoo blue High 1 1220 1342188243
alphaBoo red High 1 1220 1342188244 alphaBoo green High 1 1220
1342188244 alphaBoo red High 1 1244 1342189188 gammaDra blue High 1
1244 1342189188 gammaDra red High 1 1259 1342189823 alphaCet blue
High 1 1259 1342189823 alphaCet red High 1 1
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259 1342189826 alphaCet green High 1 1259 1342189826 alphaCet
red High 1 1284 1342190943 alphaTau green High 1 1284 1342190943
alphaTau red High 1 1284 1342190946 alphaTau blue High 1 1284
1342190946 alphaTau red High 1 1286 1342191124 gammaDra blue High 1
1286 1342191124 gammaDra red High 1 1300 1342191957 gammaDra blue
High 1 1300 1342191957 gammaDra red High 1 1300 1342191960 gammaDra
green High 1 1300 1342191960 gammaDra red High 1 1316 1342192779
gammaDra blue High 1 1316 1342192779 gammaDra red High 1 1345
1342195482 gammaDra blue High 1 1345 1342195482 gammaDra red High 1
1371 1342196729 gammaDra blue High 1 1371 1342196729 gammaDra red
High 1 1400 1342198498 gammaDra blue High 1 1400 1342198498
gammaDra red High 1 1413 1342199480 gammaDra blue High 1 1413
1342199480 gammaDra red High 1 1413 1342199511 gammaDra blue High 1
1413 1342199511 gammaDra red High 1 1413 1342199525 gammaDra blue
High 1 1413 1342199525 gammaDra red High 1 1414 1342199599 gammaDra
blue High 1 1414 1342199599 gammaDra red High 1 1414 1342199602
alphaBoo blue High 1 1414 1342199602 alphaBoo red High 1 1414
1342199605 alphaBoo green High 1 1414 1342199605 alphaBoo red High
1 1414 1342199608 betaAnd blue High 1 1414 1342199608 betaAnd red
High 1 1414 1342199611 betaAnd green High 1 1414 1342199611 betaAnd
red High 1 1414 1342199638 gammaDra blue High 1 1414 1342199638
gammaDra red High 1 1414 1342199654 gammaDra blue High 1 1414
1342199654 gammaDra red High 1 1415 1342199706 gammaDra blue High 1
1415 1342199706 gammaDra red High 1 1415 1342199716 gammaDra blue
High 1 1415 1342199716 gammaDra red High 1 1456 1342202941 gammaDra
blue High 1 1456 1342202941 gammaDra red High 1 1456 1342202957
alphaTau green High 1 1456 1342202957 alphaTau red High 1 1456
1342202960 alphaTau blue High 1 1456 1342202960 alphaTau red High 1
1457 1342203029 alphaCet blue High 1 1457 1342203029 alphaCet red
High 1 1457 1342203032 alphaCet green High 1 1457 1342203032
alphaCet red High 1 1
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483 1342204208 gammaDra blue High 1 1483 1342204208 gammaDra red
High 1 1511 1342206000 gammaDra blue High 1 1511 1342206000
gammaDra red High 1 1539 1342208970 gammaDra blue High 1 1539
1342208970 gammaDra red High 1 1566 1342210581 gammaDra blue High 1
1566 1342210581 gammaDra red High 1 1583 1342211279 alphaBoo blue
High 1 1583 1342211279 alphaBoo red High 1 1583 1342211282 alphaBoo
green High 1 1583 1342211282 alphaBoo red High 1 1607 1342212493
gammaDra blue High 1 1607 1342212493 gammaDra red High 1 1607
1342212496 gammaDra green High 1 1607 1342212496 gammaDra red High
1 1607 1342212503 betaAnd green High 1 1607 1342212503 betaAnd red
High 1 1607 1342212506 betaAnd blue High 1 1607 1342212506 betaAnd
red High 1 1614 1342212852 alphaCet green High 1 1614 1342212852
alphaCet red High 1 1614 1342212855 alphaCet blue High 1 1614
1342212855 alphaCet red High 1 1
118 1342183544 alphaCMa blue High 1 1118 1342183544 alphaCMa red
High 1 1118 1342183545 alphaCMa green High 1 1118 1342183545
alphaCMa red High 1 1300 1342191972 alphaCMa blue High 1 1300
1342191972 alphaCMa red High 1 1484 1342204225 alphaCMa green High
1 3484 1342204225 alphaCMa red High 1 3484 1342204228 alphaCMa blue
High 1 3484 1342204228 alphaCMa red High 1
3________________________________________________________
The measurements marked with “x” were not included in the
calculations for section 5.1. α CMa was alsoexcluded from the final
analysis due to a flux excess at 160µm.
C.2 Asteroid observations in chop-nod mode
The observations were taken from the following calibration
programmes:“PV/RPPhotFlux 324A”, “PV/RPPhotSpatial 314B”,
“PV/RPPhotAOTVal 511B”and include measurements taking with
dithering (Dith=1), without dithering (Dith=0), single repetitions
(nod-pattern A-B), 2 repetitions (nod-pattern A-B-B-A) and only
high gain.
OD OBSID Target Filter Gain Dith
Rep__________________________________________________________108
1342182969 360Carlova blue High 0 2108 1342182969 360Carlova red
High 0 2108 1342182970 360Carlova green High 0 2108 1342182970
360Carlova red High 0 2108 1342182971 360Carlova green High 1 2
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108 1342182971 360Carlova red High 1 2108 1342182972 360Carlova
blue High 1 2108 1342182972 360Carlova red High 1 2124 1342183903
19Fortuna blue High 1 1124 1342183903 19Fortuna red High 1 1124
1342183904 19Fortuna green High 1 1124 1342183904 19Fortuna red
High 1 1221 1342188332 21Lutetia green High 1 1221 1342188332
21Lutetia red High 1 1221 1342188333 21Lutetia blue High 1 1221
1342188333 21Lutetia red High 1 1221 1342188346 20Massalia blue
High 1 1221 1342188346 20Massalia red High 1 1221 1342188347
20Massalia green High 1 1221 1342188347 20Massalia red High 1 1221
1342188352 65Cybele blue High 1 1221 1342188352 65Cybele red High 1
1221 1342188353 65Cybele green High 1 1221 1342188353 65Cybele red
High 1 1221 1342188358 3Juno blue High 1 1221 1342188358 3Juno red
High 1 1221 1342188359 3Juno green High 1 1221 1342188359 3Juno red
High 1 1245 1342189256 47Aglaja blue High 1 1245 1342189256
47Aglaja red High 1 1245 1342189257 47Aglaja green High 1 1245
1342189257 47Aglaja red High 1 1245 1342189262 2Pallas blue High 1
1245 1342189262 2Pallas red High 1 1245 1342189263 2Pallas green
High 1 1245 1342189263 2Pallas red High 1 1285 1342191019
423Diotima blue High 1 1285 1342191019 423Diotima red High 1 1285
1342191022 423Diotima green High 1 1285 1342191022 423Diotima red
High 1 1286 1342191110 52Europa green High 1 1286 1342191110
52Europa red High 1 1286 1342191113 52Europa blue High 1 1286
1342191129 1Ceres blue High 1 1286 1342191129 1Ceres red High 1
1286 1342191132 1Ceres green High 1 1286 1342191132 1Ceres red High
1 1343 1342195353 10Hygiea blue High 1 1343 1342195353 10Hygiea red
High 1 1343 1342195354 10Hygiea green High 1 1343 1342195354
10Hygiea red High 1 1348 1342195623 4Vesta blue High 1 1348
1342195623 4Vesta red High 1 1348 1342195626 4Vesta green High 1
1348 1342195626 4Vesta red High 1 1413 1342199514 6Hebe blue High 1
1413 1342199514 6Hebe red High 1 1413 1342199517 6Hebe green High 1
1413 1342199517 6Hebe red High 1 1
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446 1342202075 2Pallas blue High 1 1446 1342202075 2Pallas red
High 1 1446 1342202078 2Pallas green High 1 1446 1342202078 2Pallas
red High 1 1485 1342204323 1Ceres blue High 1 1485 1342204323
1Ceres red High 1 1485 1342204326 1Ceres green High 1 1485
1342204326 1Ceres green High 1 1485 1342204326 1Ceres red High 1
1497 1342205032 29Amphitrite blue High 1 1497 1342205032
29Amphitrite red High 1 1497 1342205035 29Amphitrite green High 1
1497 1342205035 29Amphitrite red High 1 1566 1342210638 8Flora
green High 1 1566 1342210638 8Flora red High 1 1566 1342210641
8Flora blue High 1 1566 1342210641 8Flora red High 1 1579
1342211152 6Hebe blue High 1 1579 1342211152 6Hebe red High 1 1579
1342211155 6Hebe green High 1 1579 1342211155 6Hebe red High 1 1593
1342211811 3Juno green High 1 1593 1342211811 3Juno red High 1 1593
1342211814 3Juno blue High 1 1593 1342211814 3Juno red High 1 1613
1342212774 52Europa green High 1 1613 1342212774 52Europa red High
1 1613 1342212777 52Europa blue High 1 1613 1342212777 52Europa red
High 1 1627 1342213532 6Hebe green High 1 1627 1342213532 6Hebe red
High 1 1627 1342213535 6Hebe blue High 1 1627 1342213535 6Hebe red
High 1
1__________________________________________________________
D Old and new EEF-values
D.1 Old EEF-values connected to response calibration file ”FM,
5”
The old aperture correction factors can be taken from Fig. 17
(PICC-ME-TN-033, Version 0.3) or from Table 14.
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Figure 17: Left: Encircled energy fraction as a function of
circular aperture radius for the three bands. Derivedfrom slow scan
OD160 Vesta data.The EEF fraction shown is normalized to the signal
in aperture radius60arcsec, with background subtraction done in an
annulus between radius 61 and 70 arcsec. The right panelshows the
corresponding S/N curve under the assumption that noise scales
linearly with aperture radius. Notethat this assumption is not met
for scanmaps with 1/f noise.
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Table 14: Encircled energy fraction as a function of circular
aperture radius for the three bands. Derived fromslow scan OD160
Vesta data.The EEF fraction shown is normalized to the signal in
aperture radius 60 arcsec,with background subtraction done in an
annulus between radius 61 and 70 arcsec.
encircled energy fraction Radius encircled energy fractionRadius
[′′] blue green red [′′] blue green red
1 0.047 0.032 0.018 31 0.978 0.978 0.9562 0.214 0.156 0.069 32
0.979 0.980 0.9593 0.402 0.318 0.146 33 0.981 0.981 0.9634 0.548
0.474 0.241 34 0.982 0.983 0.9665 0.642 0.595 0.341 35 0.983 0.984
0.9696 0.701 0.672 0.438 36 0.984 0.985 0.9727 0.750 0.718 0.524 37
0.985 0.986 0.9758 0.794 0.749 0.597 38 0.986 0.987 0.9779 0.830
0.778 0.656 39 0.987 0.988 0.980
10 0.856 0.809 0.700 40 0.988 0.989 0.98211 0.873 0.840 0.734 41
0.989 0.990 0.98312 0.886 0.866 0.759 42 0.989 0.991 0.98513 0.895
0.885 0.781 43 0.990 0.992 0.98714 0.904 0.900 0.801 44 0.991 0.993
0.98815 0.913 0.910 0.820 45 0.992 0.994 0.99016 0.922 0.917 0.838
46 0.992 0.994 0.99117 0.931 0.923 0.855 47 0.993 0.995 0.99218
0.938 0.928 0.871 48 0.993 0.996 0.99319 0.945 0.932 0.885 49 0.994
0.996 0.99420 0.949 0.938 0.897 50 0.995 0.997 0.99521 0.953 0.943
0.907 51 0.995 0.997 0.99622 0.957 0.948 0.916 52 0.996 0.997
0.99723 0.960 0.954 0.923 53 0.997 0.998 0.99824 0.963 0.958 0.929
54 0.997 0.998 0.99825 0.966 0.963 0.934 55 0.998 0.998 0.99926
0.968 0.966 0.938 56 0.998 0.999 0.99927 0.970 0.970 0.942 57 0.999
0.999 0.99928 0.973 0.972 0.946 58 0.999 0.999 0.99929 0.974 0.975
0.949 59 1.000 1.000 1.00030 0.976 0.977 0.953 60 1.000 1.000
1.000
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D.2 New EEF-values connected to response calibration file ”FM,
6”
Figure 18: Combined encircled energy fractions for all three
PACS bands and out to 1000′′ (taken from PICC-ME-TN-033, version
1.01 from Nov 3, 2010).
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Table 15: New encircled energy fraction as a function of
circular aperture radius for the three bands (based
onPICC-ME-TN-033, version 1.01 from Nov 3, 2010).
encircled energy fraction Radius encircled energy fractionRadius
[′′] blue green red [′′] blue green red
1 0.053 0.038 0.015 31 0.887 0.878 0.8502 0.186 0.138 0.059 32
0.889 0.881 0.8533 0.344 0.273 0.125 33 0.891 0.882 0.8574 0.476
0.406 0.206 34 0.893 0.884 0.8605 0.567 0.513 0.294 35 0.895 0.886
0.8636 0.626 0.586 0.379 36 0.897 0.888 0.8667 0.672 0.632 0.456 37
0.898 0.890 0.8698 0.710 0.663 0.521 38 0.900 0.891 0.8729 0.742
0.690 0.573 39 0.902 0.893 0.874
10 0.765 0.717 0.613 40 0.903 0.895 0.87711 0.782 0.743 0.643 41
0.905 0.896 0.87912 0.794 0.766 0.667 42 0.906 0.898 0.88113 0.804
0.784 0.687 43 0.908 0.899 0.88314 0.812 0.798 0.705 44 0.909 0.900
0.88515 0.821 0.808 0.723 45 0.911 0.902 0.88716 0.829 0.815 0.739
46 0.912 0.903 0.88917 0.837 0.821 0.755 47 0.913 0.904 0.89018
0.844 0.826 0.769 48 0.915 0.906 0.89219 0.850 0.831 0.782 49 0.916
0.907 0.89420 0.855 0.836 0.793 50 0.917 0.908 0.89521 0.859 0.841
0.802 51 0.919 0.909 0.89722 0.863 0.847 0.810 52 0.920 0.910
0.89823 0.866 0.852 0.817 53 0.921 0.911 0.90024 0.869 0.857 0.823
54 0.922 0.913 0.90125 0.872 0.861 0.828 55 0.924 0.914 0.90226
0.875 0.865 0.832 56 0.925 0.915 0.90427 0.878 0.868 0.836 57 0.926
0.916 0.90528 0.880 0.871 0.839 58 0.927 0.917 0.90629 0.883 0.874
0.843 59 0.929 0.918 0.90730 0.885 0.876 0.846 60 0.930 0.919
0.908
E Data reduction scripts
E.1 Loading of new response calibration file
pcal6=fitsReader("PCalPhotometer_Responsivity_FM_v6.fits")...frames
= photRespFlatfieldCorrection(frames, calTree =
calTree,responsivity=pcal6)
E.2 Applying aperture corrections
The aperture correction values (old and new values) are stored
in a dedicated calibration file "PCalPhotometer ApertureCorrection
FM v2.fits".These values can be accessed in the following way:
apphot =
annularSkyAperturePhotometry(image=map,centroid=centroid,\fractional=True,algorithm=4,\
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centerX=cxpixfit,centerY=cypixfit,\radiusArcsec=raper,\innerArcsec=rskyin,outerArcsec=rskyout)
appphot_ps_corrected =
photApertureCorrectionPointSource(apphot[band] [calTree]
[apertureCorrection] [responsivityVersion][apertureRadius])
@jparameter apphot, INPUT, AnnularSkyAperturePhotometryProduct
,MANDATORY, NO default value
@jparameter band, INPUT, String, OPTIONAL, Default : None"Blue"
or "Green" or "Red"
@jparameter calTree, INPUT, PacsCal, OPTIONAL, Default :
NonePACS Calibration Tree
@jparameter apertureCorrection, INPUT, ApertureCorrection,
MANDATORY, NOdefault valueApertureCorrection calibration data
@jparameter responsivityVersion, INPUT, Integer, MANDATORY, NO
default valueForcing the responsivity file to use 5 : = version
6
@jparameter apertureRadius, INPUT, Double, MANDATORY, NO default
valueAperture Radius
@jparameter result, OUTPUT, Product, MANDATORY, NO default
valueResult with the correct data :
result[’acflux’]result[’acfluxbsub’]result[’acerror’]result[’pacerror’]
The task apertureCorrectionPointSourcePhotometry finds the right
value ”apertureCorrection” in the cal-ibration file based on the
filter band, the response file which was used for calibration (”FM,
5” or older) or(”FM, 6” or newer) and the aperture radius
(radiusArcsec) from "annularSkyAperturePhotometry". Detailsare
described in SCR ”PACS-3447”:
http://herschel.esac.esa.int/jira/browse/PACS-3447.
E.3 Script to process mini scan-map observations of stars and
asteroids
Processing data up to level 1:
def
ScanMapScript_L1_20100813_T5B975(obsid1,obsid2,cam,poold=’/a73d3/nielbock/lstore/’,pool=’EPOS’,MMT=False,SL=False,SLslice=False):
"""
processing data up to level1
"""
###############################################################################
# PACS Phot Scan Map Script (L0 -> L1)
# Version 2010-08-13
# to be used with HIPE 5.0/975 onwards
#
# This script processes PACS scan map observations up to level
1. The final
# map (level 2) is then produced in a separate script. It
provides:
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#
# - combined reduction of scan and cross scan (two OBSIDs)
# - correct handling of tracked solar system objects (SSOs)
# - optional MMT deglitching (not to be used in connection with
MadMap)
# - source masks in connection with MMT deglitching to avoid
clipping of
# bright sources, optionally by:
# a) using the standard pipeline level 2 product as a source
model
# b) providing an external FITS (single extension)
# Note that HIPE expects an format that contains the three FITS
extensions
# that are produced at the level 2 stage. For this reason, the
standard
# pipeline level 2 product is loaded that provides the correct
data
# structure. The manually defined source mask is inserted
there.
# The parameter "maskthreshold" is the lower flux level per
pixel that defines
# what should be considered as the source area. The mask is
stored in the
# level 1 product as "HighpassMask". No mask will be created, if
MMT
# deglitching is omitted.
# - 2nd level deglitching with optional sliced processing
(secDegSlice=True) to
# reduce the memory allocation (especially useful for many map
repetitions).
# This reduces the processing speed.
#
from herschel.ia.obs.auxiliary.fltdyn import Horizons
from herschel.ia.obs.auxiliary.fltdyn import Ephemerides
from herschel.share.fltdyn.ephem.horizons import
HorizonsFileEphemSet
from java.util import Date
import os
dir = os.getcwd()+"/"
date=FineTime(Date()).toString().split(’T’)[0]
pcal6=fitsReader("PCalPhotometer_Responsivity_FM_v6.fits")
###############################################################################
# Modify accordingly
POOLDIR=poold
POOLNAME=pool
OBSID=[obsid1,obsid2]
camera=cam
maskthreshold=0.01
# MMT Deglitching parameters
# Set to False for MadMap
#MMTdeglitch=True
MMTdeglitch=MMT
nscale=3
# Set sigma clipping to a value, that does not affect the target
flux!
nsigma=25
# Will the object mask be read from an external FITS file?
#fitsmask = True
fitsmask = False
srcmodel=dir+’objectmask.fits’
# 2nd level deglitching parameters
secDeg=SL
secDegSlice=SLslice
#secDeg=True
#secDegSlice=True
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#secDegMethod=’framessignal’
secDegMethod=’timeordered’
secondnsigma=40
###############################################################################
framescube=[]
for obsid in OBSID:
print "Retrieving observation context : ", Date()
#obsCont=getObservation(long(obsid),poolName=POOLNAME,
poolLocation=POOLDIR,verbose=True)
obsCont=getObservation(long(obsid),useHsa=True,verbose=True)
print "Extracting calibration database ..."
calTree=getCalTree(obs=obsCont)
# retrieve auxiliary products:
print "Obtaining the data : ", Date()
print " Housekeeping"
photHk =
obsCont.level0.refs["HPPHK"].product.refs[0].product["HPPHKS"]
print " Pointing"
pp = obsCont.auxiliary.refs["Pointing"].product
print " Time Correlation"
timeCorr = obsCont.auxiliary.refs[’TimeCorr’].product
print " OrbitEphemeris"
oep = obsCont.auxiliary.orbitEphemeris
# Is it a solar System Object ?
isSso = isSolarSystemObject(obsCont)
if (isSso):
try:
hp =
obsCont.refs["auxiliary"].product.refs["HorizonsProduct"].product
ephem = Ephemerides(oep)
print "Extracting horizon product ..."
if hp.isEmpty():
print "ATTENTION! Horizon product is empty! Cannot correct SSO
proper motion!"
horizons = None
else:
horizons = Horizons(hp, ephem)
except:
print "ATTENTION! No horizon product available! Cannot correct
SSO proper motion!"
horizons = None
else:
horizons = None
print " Metadata"
object=obsCont.meta[’object’].value
TARGET=object.replace(’ ’,’’)
TSTART=obsCont.meta["startDate"].value.toString().split(’
’)[0]
TEND=obsCont.meta["endDate"].value
AOR=obsCont.meta["aorLabel"].value
CalAOR=AOR.split(’-’)[-1]
OD=obsCont.meta["odNumber"].value
BAND=obsCont.meta[’blue’].value
if (camera == ’blue’):
l0_status=obsCont.refs["level0"].product.refs["HPPAVGB"].product.refs[0].product["Status"]
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if (BAND == ’blue2’):
filter = ’green’
else:
filter = ’blue’
else:
l0_status=obsCont.refs["level0"].product.refs["HPPAVGR"].product.refs[0].product["Status"]
filter = ’red’
bolst=l0_status["BOLST"].data
dataType= bolst/64- bolst/1024*16
if (MEDIAN(dataType) % 2) == 0:
manGAIN= "High"
else:
manGAIN= "Low"
GAIN=obsCont.meta["PACS_PHOT_GAIN"].value
OBSMODE=obsCont.meta[’obsMode’].value
CUSMODE=obsCont.meta[’cusMode’].value
PMODE=obsCont.meta[’pointingMode’].value
RA=obsCont.meta["ra"].value
DEC=obsCont.meta["dec"].value
PA=obsCont.meta["posAngle"].value
REP=obsCont.meta["repFactor"].value
NOLEGS=obsCont.meta["mapScanNumLegs"].value
pHK_times=Double1d(photHk["Time"].data/1.e6)
pHK_T_bol_EV=Double1d(photHk["BOL_TEMP_EV"].data)
Tbol_EV=MEAN(pHK_T_bol_EV)
#---------------- At this stage, we have the data so actual work
can proceed.
# here I will print some information on the current
observation
print "OD: ", OD
print "AOR label: ", AOR
print "Target: ", TARGET
print "CUS mode: ", CUSMODE
print "OBS mode: ", OBSMODE
print "Pipeline version: ",
obsCont.meta.get(’creator’).value
if (obsCont.meta.get(’cusMode’).value !=
’SpirePacsParallel’):
print "Dithering on? ", obsCont.meta.get(’dither’).value
print "Repetition factor: ", REP
print "Pointing mode: ", PMODE
print "Blue filter: ", BAND
# by default the readout frequency is 10Hz (important to compute
the HP width)
imPerSec = 10.
if (PMODE == ’Line_scan’):
if (CUSMODE != ’SpirePacsParallel’):
print "Scan leg length: ",
obsCont.meta.get(’mapScanLegLength’).value
print "Scan speed: ", obsCont.meta.get(’mapScanSpeed’).value
print "Scan angle: ", obsCont.meta.get(’mapScanAngle’).value
print "Scan angle reference: ",
obsCont.meta.get(’mapScanAngleRef’).value
legLength = obsCont.meta.get(’mapScanLegLength’).value * 60.
scanSpeed = obsCont.meta.get(’mapScanSpeed’).value
else:
# in parallel mode the effective blue readout frequency is 5
Hz
if (camera == ’blue’):
imPerSec = 5.
print "Scan length: ", obsCont.meta.get(’mapSize1’).value
print "Scan width: ", obsCont.meta.get(’mapSize2’).value
print "Scan speed: ", obsCont.meta.get(’mapScanRate’).value
legLength = obsCont.meta.get(’mapSize1’).value * 60.
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scanSpeed = obsCont.meta.get(’mapScanRate’).value
# determines the HP width from the scan length
if (scanSpeed == ’low’) or (scanSpeed == ’slow’):
leg_width = imPerSec * (legLength / 10.)
elif (scanSpeed == ’medium’):
leg_width = imPerSec * (legLength / 20.)
else:
leg_width = imPerSec * (legLength / 60.)
leg_width = int(leg_width)
print "Number of images per scan leg: ", leg_width
print "Photometer gain: ", manGAIN
print "Photometer mode: ",
obsCont.meta.get(’PACS_PHOT_MODE’).value
print "Observation start:
",FineTime(l0_status["FINETIME"].data[0])
print "Observation end:
",FineTime(l0_status["FINETIME"].data[-1])
finetime=long(MEDIAN(l0_status["FINETIME"].data))
if
(MEDIAN(Int1d(pp.getPointingDataset(finetime)["isStrInterlacing"].data))
== 1.0):
print "STR Mode: Interlacing was enabled!"
else:
print "STR Mode: Interlacing was disabled!"
System.gc()
# Start processing
print "Starting processing: ", Date()
print "Retrieving Level 0.5 data: ", Date()
level0_5 = PacsContext(obsCont.level0_5)
frames = level0_5.getCamera(camera).averaged.product
frames=frames.getScience(0)
del(level0_5,l0_status)
System.gc()
print "Flagging bad pixels: ", Date()
frames = photFlagBadPixels(frames, calTree=calTree)
print "Flagging daturated pixels: ", Date()
frames =
photFlagSaturation(frames,calTree=calTree,hkdata=photHk,check=’full’)
print "Converting digital units to physical units: ", Date()
frames = photConvDigit2Volts(frames, calTree=calTree)
System.gc()
print "Converting chopper position to angle: ", Date()
frames = convertChopper2Angle(frames,calTree=calTree)
System.gc()
print "Adding pointing: ", Date()
frames = photAddInstantPointing(frames,pp,
calTree=calTree,orbitEphem=oep,horizons=horizons)
#new Sso treatment including Xscans
if (isSso == True and (horizons != None)):
print "Correcting coordinates for SSO ...", Date()
if (obsid == OBSID[0]):
timeOffset = frames.getStatus("FINETIME")[0]
frames = correctRaDec4SsoScanXScan(frames, horizons,
timeOffset)
frames = photAssignRaDec(frames, calTree=calTree)
System.gc()
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PACS Photometer - Point-Source Flux Calibration Page 49
print "cleanPlateauFrames: " , Date()
frames = cleanPlateauFrames(frames, calTree=calTree)
System.gc()
print "Adding UTC reference time frame: ", Date()
frames = addUtc(frames, timeCorr)
System.gc()
print "Applying response correction and detector flatfield: ",
Date()
frames = photRespFlatfieldCorrection(frames, calTree =
calTree,responsivity=pcal6)
#frames =
photRespFlatfieldCorrection(frames,responsivity=myresp)
print "Extending scan leg data content: ", Date()
frames = extendBbid(frames,215131301L,8,15)
#print "Masking problematic data: ", Date()
#frames =
photMaskFrames(frames,beforeFirstScanLeg=False,noScanData=False)
# MMT deglitching and source mask
if (MMTdeglitch):
print "Reading in the significant map ...", Date()
if (fitsmask==True):
print " ... from a manually defined FITS file."
mask=fitsReader(srcmodel)
maskwcs=mask.getWcs()
rawimage=mask.image
idx=rawimage.where(rawimage>maskthreshold).toInt1d()
rawimage=rawimage*0.
rawimage[Selection(idx)]=1.
mapMask=obsCont.refs["level2"].product.refs["HPPPMAPB"].product.refs[0].product
mapMask.setImage(rawimage)
mapMask.setWcs(maskwcs)
else:
print " ... from the level 2 product in the
ObservationContext."
if (camera == ’blue’):
mapMask=obsCont.refs["level2"].product.refs["HPPPMAPB"].product.refs[0].product
else:
mapMask=obsCont.refs["level2"].product.refs["HPPPMAPR"].product.refs[0].product
rawimage=Double2d(mapMask.image)
idx=rawimage.where(rawimage>maskthreshold).toInt1d()
rawimage=rawimage*0.
rawimage[Selection(idx)]=1.
mapMask.setImage(rawimage)
print "Find the pixels of the cube that see the object: ",
Date()
framesC = frames.copy()
framesC =
photReadMaskFromImage(framesC,mapMask,threshold=0.1,\
calTree=calTree,extendedMasking=True)
objectMask = framesC.getMask(’Highpassmask’).copy()
print "MMT Deglitching the data using the source mask: ",
Date()
framesC = photMMTDeglitching(framesC,copy=False,\
scales=nscale,nsigma=nsigma,\
incr_fact=2,mmt_mode=’multiply’,\
sourcemask=’Highpassmask’,onlyMask=True)
mmt_mask = framesC.getMask(’MMT_Glitchmask’)
# Adding MMT deglitching mask to frames
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PACS Photometer - Point-Source Flux Calibration Page 50
frames.addMaskType(’MMT_Glitchmask’,’MMT deglitching’)
frames.setMask(’MMT_Glitchmask’,mmt_mask)
nMask = mmt_mask.where(mmt_mask == True).length()
frac = 100.*nMask/len(framesC.signal)
print ’ MMT deglitching has masked ’+str(nMask)+’ pixels.’
print ’ MMT deglitching has masked %.2f’%frac+’% of the
data.’
del(mmt_mask,nMask,frac,framesC)
# recreate a copy of frames for second level deglitching
framesC = frames.copy()
System.gc()
# Second level deglitching
if (secDeg):
print "Second level deglitching: ", Date()
print " Creating a new map to find/show modified pixels: ",
Date()
framesC = highpassFilter(framesC,leg_width/2)
if (secDegSlice):
print " Creating the map-to-cube index per repetition: ",
Date()
for iRep in range(REP):
print " Processing repetition "+str(iRep+1)+" out of
"+str(REP)
# first we select the frames
fsub = framesC.select(framesC.status[’Repetition’].data ==
iRep+1)
mapToCubeIdx = mapindex(fsub,slimindex=False)
s =
Sigclip(10,secondnsigma,behavior="clip",outliers="both",mode=Sigclip.MEDIAN)
IIndLevelDeglitch(mapToCubeIdx,fsub,map=False,mask=True,\
maskname=’SecondGlitchmask’,algo=s,\
deglitchvector=secDegMethod)
if (iRep == 0):
framesOut = fsub.copy()
else:
framesOut.join(fsub)
# cleanup
del(mapToCubeIdx,fsub,s)
pass
framesC = framesOut
else:
print " Creating the map-to-cube index: ", Date()
s =
Sigclip(10,secondnsigma,behavior="clip",outliers="both",mode=Sigclip.MEDIAN)
mapToCubeIdx = mapindex(framesC,slimindex=False)
IIndLevelDeglitch(mapToCubeIdx,framesC,map=False,mask=True,\
maskname=’SecondGlitchmask’,algo=s,\
deglitchvector=secDegMethod)
del(mapToCubeIdx,s)
mask = framesC.getMask(’SecondGlitchmask’)
nMask = mask.where(mask == True).length()
frac = 100.*nMask/len(framesC.signal)
print " Second level deglitching has masked "+str(nMask)+"
pixels."
print ’ Second level deglitching has masked %.2f’%frac+’% of the
data.’
# now I add this mask to the frames
frames.addMaskType(’SecondGlitchmask’,’Second level
deglitching’)
frames.setMask(’SecondGlitchmask’,mask)
# now it is time to clean up
del(mask,nMask,frac,framesC)
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if (secDegSlice):
del(framesOut)
System.gc()
else:
print " Second level deglitching skipped...: ", Date()
if (MMTdeglitch):
frames.addMaskType(’HighpassMask’,’Pixels that see the
object’)
frames.setMask(’HighpassMask’,objectMask)
pacsPropagateMetaKeywords(obsCont,’1’,frames)
print "Saving level 1 frames: ", Date()
FitsArchive().save(str(obsid)+’_’+filter+’_level1Frames.fits’,frames)
#convertL1ToScanam(frames)
System.gc()
pass
System.gc()
Post-processing for non-moving targets:
def
postProcess_L2_highPass_20110127_T6B1932(obsid1,obsid2,obsid3,obsid4,filt,poold=’/a73d3/nielbock/lstore/’,\
pool=’EPOS’,outsize=3.2,hpwidth=15):
"""
post processing for High pass filtering
"""
###############################################################################
# PACS Phot Scan Map Script (L1 -> L2)
# for highpass filtering and photProject()
# Version 2010-08-13
# to be used with HIPE 5.0/975 onwards
#
# This script processes PACS scan map observations from level 1
to level 2, i.e.
# the final map. It provides:
#
# - source masks in connection with highpass filtering,
optionally by:
# a) using the "HighpassMask" from the level 1 processing
(fitsmask=False)
# b) providing an external FITS (single extension)
(fitsmask=True)
# Note that HIPE expects an format that contains the three FITS
extensions
# that are produced at the level 2 stage. For this reason, the
standard
# pipeline level 2 product is loaded that provides the correct
data
# structure. The manually defined source mask is inserted
there.
# The parameter "maskthreshold" is the lower flux level per
pixel that defines
# what should be considered as the source area.
#
from herschel.pacs.spg.phot import PhotReadMaskFromImageTask
photMaskFromImageHighpass = PhotReadMaskFromImageTask()
from java.util import Date
import os
dir = os.getcwd()+"/"
###############################################################################
# Modify accordingly
POOLDIR=poold
POOLNAME=pool
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Document: PICC-ME-TN-037Date: April 12, 2011Version: 1.0
PACS Photometer - Point-Source Flux Calibration Page 52
# Define parameters
filter=filt
fitsmask = False
srcmodel=’objectmask.fits’
if ( (filter == ’blue’) and (obsid1 > 0) and (obsid2 > 0)
):
OBSID=[obsid1,obsid2]
HP_width=hpwidth
pixsize=outsize
pix=str(pixsize).replace(’.’,’p’)
maskthreshold=0.0025
grow=1
elif ( (filter == ’green’) and (obsid3 > 0) and (obsid4 >
0) ):
OBSID=[obsid3,obsid4]
HP_width=hpwidth
pixsize=outsize
pix=str(pixsize).replace(’.’,’p’)
maskthreshold=0.0025
grow=1
elif ( (filter == ’red’) and (obsid1 > 0) and (obsid2 > 0)
and (obsid3 == 0) and (obsid4 == 0) ):
OBSID=[obsid1,obsid2]
HP_width=hpwidth
pixsize=outsize
pix=str(pixsize).replace(’.’,’p’)
maskthreshold=0.01
grow=2
elif ( (filter == ’red’) and (obsid1 == 0) and (obsid2 == 0) and
(obsid3 > 0) and (obsid4 > 0) ):
OBSID=[obsid3,obsid4]
HP_width=hpwidth
pixsize=outsize
pix=str(pixsize).replace(’.’,’p’)
maskthreshold=0.01
grow=2
else:
OBSID=[obsid1,obsid2,obsid3,obsid4]
HP_width=hpwidth
pixsize=outsize
pix=str(pixsize).replace(’.’,’p’)
maskthreshold=0.01
grow=2
if (fitsmask):
# Create Highpass Mask
mask=fitsReader(srcmodel)
maskwcs=mask.getWcs()
# Set source area to 1
rawimage=mask.image
idx=rawimage.where(rawimage>maskthreshold).toInt1d()
rawimage=rawimage*0.
rawimage[Selection(idx)]=1.
rawimage=growRegion(rawimage,grow)
obsCont=getObservation(long(OBSID[0]),useHsa=True,verbose=False)
mapMask=obsCont.refs["level2"].product.refs["HPPPMAPB"].product.refs[0].product
mapMask.setImage(rawimage)
mapMask.setWcs(maskwcs)
calTree=getCalTree()
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Document: PICC-ME-TN-037Date: April 12, 2011Version: 1.0
PACS Photometer - Point-Source Flux Calibration Page 53
framescube=[]
for obsid in OBSID:
frames =
FitsArchive().load(dir+str(obsid)+’_’+filter+’_level1Frames.fits’)
if (fitsmask):
frames.removeMask(’HighpassMask’)
frames =
photMaskFromImageHighpass(frames,si=mapMask,maskname=’HighpassMask’,calTree=calTree)
print ’Resetting the on target status to true: ’, Date()
frames.setStatus(’OnTarget’,Bool1d(frames.dimensions[2],True))
frames =
highpassFilter(frames,HP_width,maskname=’HighpassMask’,interpolateMaskedValues=True)
framescube.append(frames)
del(frames)
pass
System.gc()
frames_joined=framescube[0]
for i in range(len(framescube)-1):
frames_joined.join(framescube[i+1])
pass
del(framescube)
frames=frames_joined.copy()
print ’Removing the slew: ’, Date()
frames = filterSlew(frames)
frames.removeMask(’HighpassMask’)
frames = frames.select(frames.getStatus("BBID") ==
215131301l)
System.gc()
object=frames.meta[’object’].value
TARGET=object.replace(’ ’,’’)
OD=frames.meta[’odNumber’].value
print ’Creating the final map: ’, Date()
print ’ Number of readouts in the cube:
’,frames[’Signal’].data.dimensions[2]
l2 =
photProject(frames,calibration=True,calTree=calTree,outputPixelsize=pixsize)
if ( (filter == ’blue’) and (obsid1 > 0) and (obsid2 > 0)
):
OBSID=[obsid1,obsid2]
HP_width=hpwidth
pixsize=outsize
pix=str(pixsize).replace(’.’,’p�