Version: 1.0 PACS Photometer - Point-Source Flux...

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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).


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).


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).


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.


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).


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


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’):


else:


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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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."



else:

mapMask=obsCont.refs["level2"].product.refs["HPPPMAPR"].product.refs[0].product

rawimage=Double2d(mapMask.image)





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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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

PACSHerschel



# Define parameters

filter=filt

fitsmask = False

srcmodel=’objectmask.fits’

if ( (filter == ’blue’) and (obsid1 > 0) and (obsid2 > 0) ):


HP_width=hpwidth

pixsize=outsize

pix=str(pixsize).replace(’.’,’p’)

maskthreshold=0.0025

grow=1

elif ( (filter == ’green’) and (obsid3 > 0) and (obsid4 > 0) ):


HP_width=hpwidth

pixsize=outsize


maskthreshold=0.0025

grow=1

elif ( (filter == ’red’) and (obsid1 > 0) and (obsid2 > 0) and (obsid3 == 0) and (obsid4 == 0) ):


HP_width=hpwidth

pixsize=outsize


maskthreshold=0.01

grow=2

elif ( (filter == ’red’) and (obsid1 == 0) and (obsid2 == 0) and (obsid3 > 0) and (obsid4 > 0) ):


HP_width=hpwidth

pixsize=outsize


maskthreshold=0.01

grow=2

else:

OBSID=[obsid1,obsid2,obsid3,obsid4]

HP_width=hpwidth

pixsize=outsize


maskthreshold=0.01

grow=2

if (fitsmask):

# Create Highpass Mask

mask=fitsReader(srcmodel)

maskwcs=mask.getWcs()

# Set source area to 1

rawimage=mask.image




rawimage=growRegion(rawimage,grow)

obsCont=getObservation(long(OBSID[0]),useHsa=True,verbose=False)



mapMask.setWcs(maskwcs)

calTree=getCalTree()

PACSHerschel



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) ):


HP_width=hpwidth

pixsize=outsize

pix=str(pixsize).replace(’.’,’p�

Version: 1.0 PACS Photometer - Point-Source Flux...

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