1. Markus Nielbock (MPIA) – Herschel/PACS DP Workshop Herschel DP Workshop PACS Photometer Flux Calibration and Point Source Photometry Markus Nielbock (MPIA Heidelberg) Thomas Müller (MPE Garching) on behalf of the PACS ICC ESAC, 21 st February 2012

2. PACS Photometer PS Calibration & Photometry Overview Markus Nielbock (MPIA) – Herschel/PACS DP Workshop

3. PACS Photometer PS Calibration & Photometry Flux calibration: General remarks ● flux calibration based on scan map observations of 5 prime fiducial stars

4. PACS Photometer PS Calibration & Photometry Flux calibration: General remarks

5. PACS Photometer PS Calibration & Photometry Flux calibration: General remarks ● flux calibration based on scan map observations of 5 prime fiducial stars ● model uncertainty amounts to 5% throughout ● aperture photometry with aperture correction (PSF template: Vesta, Mars) ● colour corrections: 1.016, 1.033, 1.074 at 70, 100, 160 µm ● all prime standards are in the linear flux regime ● secondary targets (faint stars, asteroids, planets) ● observed for consistency and extension of flux range Markus Nielbock (MPIA) – Herschel/PACS DP Workshop calTree = getCalTree() resp = calTree.refs["photometer"].product.refs["responsivity"].product resp70 = resp["blue"]["Responsivity"] print resp70 calTree = getCalTree() resp = calTree.refs["photometer"].product.refs["responsivity"].product resp70 = resp["blue"]["Responsivity"] print resp70

6. PACS Photometer PS Calibration & Photometry Flux calibration: 70 µm Markus Nielbock (MPIA) – Herschel/PACS DP Workshop

7. PACS Photometer PS Calibration & Photometry Flux calibration: 100 µm Markus Nielbock (MPIA) – Herschel/PACS DP Workshop

8. PACS Photometer PS Calibration & Photometry Flux calibration: 160 µm improved data processing Markus Nielbock (MPIA) – Herschel/PACS DP Workshop

9. PACS Photometer PS Calibration & Photometry Flux calibration: Results ● flux calibration based on scan map observations of 5 prime fiducial stars ● scan-map: 160 µm fluxes might be underestimated by 2% ● fluxes of prime calibrators in PACS-P scan-map observations consistent with models within: ● 3%, 3%, 5% at 70, 100, 160 µm (weighted by calibrator, not by individual observation) ● no NIR leakage; all targets are consistent irrespective of temperature ● extracted fluxes are influenced at a level of a few percent by: ● cross-talk, highpass filtering, source masking, deglitching, etc. Markus Nielbock (MPIA) – Herschel/PACS DP Workshop

10. PACS Photometer PS Calibration & Photometry Non-linearity correction Markus Nielbock (MPIA) – PACS Photometer Flux Calibration ● based on pre-flight ground calibration ● non-linearity corrections of the order of 10% for signals around 60 Jy/pixel ● → small effect (0-15%) for brightest calibration sources (> 100 Jy per point source) ● flux calibrators affected: ● planets: Uranus, Neptune (5-15%) ● bright asteroids: Ceres, Pallas, Vesta (0-5%) ● shifts all objects into a ± 5% consistency range ● independent of gain setting ● is implemented in HIPE 8 (on frames, Level 1): ● photNonLinearityCorrection() calTree = getCalTree() nLin = calTree.refs["photometer"].product.refs["nonLinearCoef"].product nLin70 = nLin[“blue”] calTree = getCalTree() nLin = calTree.refs["photometer"].product.refs["nonLinearCoef"].product nLin70 = nLin[“blue”]

11. PACS Photometer PS Calibration & Photometry Non-linearity correction: Verification 100 µm Markus Nielbock (MPIA) – PACS Photometer Flux Calibration Neptun e

12. PACS Photometer PS Calibration & Photometry Non-linearity correction: Verification 100 µm Markus Nielbock (MPIA) – PACS Photometer Flux Calibration Neptun e

13. PACS Photometer PS Calibration & Photometry Aperture photometry Markus Nielbock (MPIA) – PACS Photometer Flux Calibration ● aperture photometry done with: annularSkyAperturePhotometry() ● GUI version available; script version more useful for parameter control and extraction ● provides source centroiding, however not very reliable → source fitting in previous step ● GUI version: sourceFitting(), but not very robust → cannot be reliably automated ● script version based on SourceFitTask() works very well for single point sources, ● see task definition mapSourceFitter() in Ipipe script L3_pointSourceAperturePhotometry.py ● aperture photometry with background subtraction ● error estimate not trustworthy → correlated noise ● alternative method explained later

14. PACS Photometer PS Calibration & Photometry Aperture correction Markus Nielbock (MPIA) – PACS Photometer Flux Calibration ● based on PSF templates Vesta and Mars ● correction factors from “encircled energy fraction” ● depends on version of flux calibration ● applied by HIPE task: ● photApertureCorrectionPointSource() ● Caution! Aperture photometry product is overwritten. calTree = getCalTree() apCorr = calTree.refs["photometer"].product.refs["apertureCorrection"].product apCorr_r = apCorr["fm6ApertureRadius"].data apCorr70 = apCorr["fm6BandBlue"].data apCorr100= apCorr["fm6BandGreen"].data apCorr160= apCorr["fm6BandRed"].data p=PlotXY(batch=1) p.getLegend().setVisible(1) p.setTitleText("PACS Aperture Correction") l1=LayerXY(apCorr_r,apCorr70) l1.setColor(java.awt.Color.blue) l1.setName("blue") p.addLayer(l1) l2=LayerXY(apCorr_r,apCorr100) l2.setColor(java.awt.Color.green) l2.setName("green") p.addLayer(l2) l3=LayerXY(apCorr_r,apCorr160) l3.setColor(java.awt.Color.red) l3.setName("red") p.addLayer(l3) p.setXrange([0,60]) p.setXtitle('Radius ["]') p.setYrange([0,1]) p.setYtitle('Encircled Energy Fraction') p.batch=0 calTree = getCalTree() apCorr = calTree.refs["photometer"].product.refs["apertureCorrection"].product apCorr_r = apCorr["fm6ApertureRadius"].data apCorr70 = apCorr["fm6BandBlue"].data apCorr100= apCorr["fm6BandGreen"].data apCorr160= apCorr["fm6BandRed"].data p=PlotXY(batch=1) p.getLegend().setVisible(1) p.setTitleText("PACS Aperture Correction") l1=LayerXY(apCorr_r,apCorr70) l1.setColor(java.awt.Color.blue) l1.setName("blue") p.addLayer(l1) l2=LayerXY(apCorr_r,apCorr100) l2.setColor(java.awt.Color.green) l2.setName("green") p.addLayer(l2) l3=LayerXY(apCorr_r,apCorr160) l3.setColor(java.awt.Color.red) l3.setName("red") p.addLayer(l3) p.setXrange([0,60]) p.setXtitle('Radius ["]') p.setYrange([0,1]) p.setYtitle('Encircled Energy Fraction') p.batch=0

15. PACS Photometer PS Calibration & Photometry PSF and Encircled Energy Fraction Markus Nielbock (MPIA) – PACS Photometer Flux Calibration ● derived from scan map observations ● PSF core: (nearly) point sources Vesta and α Tau ● PSF wings: unsaturated areas of Mars WARNING! There is no single general PSF! A PSF is influenced by: ● fast scan PSF smearing ● reduction methods: ● highpass filtering, map pixel size, drizzling ● source SED ● straylight and ghost effects ● pointing quality

16. PACS Photometer PS Calibration & Photometry Error estimate Markus Nielbock (MPIA) – PACS Photometer Flux Calibration ● pixel-to-pixel noise in final map is affected by correlated noise → measured too low ● error map in L2 product unreliable → highpass filter modifies noise spectrum ● alternative method to derive error map from data is close to final stage ● documentation almost finished: ● noise behaviour as function of HPF, source masking, pixel size, drizzling drop size ● in the meantime: ● photometric error for PS aperture photometry can be estimated from empty background ● Method: multiple aperture photometry of background and use r.m.s. as estimate ● see: L3_pointSourceAperturePhotometry.py

17. PACS Photometer PS Calibration & Photometry What about PSF photometry? Markus Nielbock (MPIA) – PACS Photometer Flux Calibration ● no task or method provided (yet?) ● How to do this: ● get PSF template observation (e.g. Vesta) ● process to L2 like your science data ● normalise and rotate PSF map ● use a convenient PSF photometry tool

18. PACS Photometer PS Calibration & Photometry Non-linearity correction: Influence on model PSFs and EEF ● deviations found: ● ~ 10% in peak flux, ~ 5% in total flux ● flux calibration at 70 µm affected ● aperture photometry of point sources is self-consistent ● and not affected (responsivity ↔ aperture correction) ● extended emission currently measured too high at 70 µm ● flux calibration and aperture correction will be updated ● (goal: HIPE 9) ● surface brightness at 70 µm will drop by a few percent Markus Nielbock (MPIA) – Herschel/PACS DP Workshop