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STARFINDER & ESO PAPAO PROGRAM

STARFINDER & ESO PAPAO PROGRAM. by Professor Douglas G. Currie University of Maryland and ESO and CfAO presented to the Gemini NOAO Workshop on 27 February 2001. Stellar Photometry Issues. Aspects to be Addressed Photometry and Deconvolution Algorithms

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STARFINDER & ESO PAPAO PROGRAM

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  1. STARFINDER&ESO PAPAO PROGRAM by Professor Douglas G. Currie University of Maryland and ESO and CfAO presented to the Gemini NOAO Workshop on 27 February 2001

  2. Stellar Photometry Issues • Aspects to be Addressed • Photometry and Deconvolution Algorithms • Spatial and Temporal Variations in PSF • High and Low Strehl Ratios • Shack-Hartman and Curvature Systems • Collect Calibrated Data Sets Using ADONIS • Globular and Other Stellar Clusters • Nebular Structures • Astrometrically Calibratable Imagery • Select Examples from Existing Data Sets • AO Conditions Not Available in Calibrated Data • Analysis by International Set of Groups • Evaluation and Selection of Optimal Algorithms • Provide “ToolKit” for General Observers

  3. STARFINDER • Joint Project between UoB & ESO • University of Bologna and Bologna Observatory • Emiliano Diolaiti - Author • GianLuigi Parmeggiani • Orizo Bendinelli • European Southern Observatory • Doug Currie • Laird Close • Domenico Bonaccini • Francois Rigaut

  4. Why a new code? • Main features of AO images: • complicated PSF • good sampling • anisoplanatism • Possible troubles: • false detections • loss of accuracy ADONIS PSF (SQRT stretch)

  5. Guidelines • stellar field = stars + background • The analysis involves • detection of stellar sources • astrometry and photometry • Overlapping of stellar images • Main guidelines: • Keep track of bright sources (synthetic field) • No analytic modeling of the PSF (PSF array)

  6. PSF extraction select PSF stars subtract background center normalize median of stack post-process

  7. Main loop flowchart INPUT: stellar field, background search sort analyze YES repeat? background re-fit NO OUTPUT: stellar field model, background estimate, list of stars

  8. Analysis of an object (1) • Let’s consider a sub-image centered on a presumed starThe sub-image includes a previously detected starand PSF features of bright stars outside • The presumed star may be a PSF feature of a brighter source. To assess it ... Fig. 1 image Fig. 2 synthetic field

  9. Analysis of an object (2) • … we subtract Fig. 2 (synthetic field) from Fig. 1 (image) and check if the peak is still there • The correlation coefficient of the residual peak with the PSF represents an objective measure of similarity

  10. Analysis of an object (3) • If the correlation check is successful, we determine the position and flux of the star with a 2-components fit • The fitting process must take into account the (fixed) halo contribution of the stars outside the sub-image, derived from the synthetic field

  11. Analysis of an object (4) • If the fit is acceptable, we store the parameters of the new detected starand update those of the adjacent (re-fitted) source • We update also the synthetic field, which includes now an updated copy of all the sources detected so far

  12. Examples of blends 1.5 FWHM 1.0 FWHM 0.75 FWHM 0.5 FWHM Flux ratio 1:1 Flux ratio 2:1 PSF (centered and off-centered)

  13. De-blending strategies • Main loop iteration • Others • thresholding • subtraction - = vs. PSF - =

  14. Correlation coefficient • Definition • Maximize correlation to • measure similarity between object and reference • improve positioning accuracy

  15. Astrometry and photometry • A sub-image is fitted with the model • parameters to optimize • Optimization by means of Newton-Gauss algorithm • Sub-pixel astrometry requires interpolation of the PSF array fixed additive term sum of shifted PSFs local background

  16. Fitting procedure: derivatives See also Véran & Rigaut (1998)

  17. Background estimation 1 2 3 6 5 4

  18. Saturated stars • Repaired core of a saturated star • Positioning by cross-correlation, scaling factor by LS fit • Example on real data 1.5% error unsaturated star saturated reconstructed

  19. Noise estimation • Photon noise + instrumental noise • Histogram fitting to estimatenormally-distributed noise

  20. GUI interface Left: main widget. Right: task for stars detection, astrometry and photometry

  21. Code structure XStarFinder Data(I/O and storage) XNoise, XNoise_StDev(noise estimation) XReplace_Pix(bad pixels repair) XPsf_Extract(PSF extraction) XImage_Support, XPsf_Smooth(PSF post-processing) XStarFinder_Run(stars detection, astrometry and photometry) XCompare_Lists(lists comparison) XDisplay_Opt (modify display options) Low-level IDL code

  22. Results on simulated data • Image features • 1000 stars • K-band PSF • Strehl ratio ~40% • 0.035 arcsec/pixel • Results • ~ 90% detected stars •  1% false detections • ~ 80% detected stars with‘very accurate’ astrometry and photometry

  23. Results on real data (Galactic Center) • Image features • ~ 1000 stars • K-band, 15 min exposure • Strehl ratio ~45% • 0.035 arcsec/pixel • Results of synthetic stars test • ~ 77% detected stars • ~2% false detections (mag > 8) • ~ 58% detected stars with‘very accurate’ astrometry and photometry Image Reconstruction Image kindly provided by F.Rigaut

  24. Trapezium (ADONIS) • Image features: • K’ band, 4 different pointing • Two data sets (long and short exposure time) • 0.050 arcsec/pixel • Purpose of the analysis • Internal accuracy evaluation • Photometric error on saturated stars Image Common stars

  25. Results on Trapezium Image Reconstruction PSF stars

  26. CLASSES OF TARGETS • Unresolved Targets • Isolated Unresolved Sources • Astronomical - e. g. Stellar Clusters • Linear Features • Solar System Targets • Extended Targets • Diffuse, with no sharp edges • Nebular and Extra-Galactic

  27. Trapezium

  28. ESO PAPAO APPROACH • Select Specific Targets • Collect AO Data • Very Well Calibrated • Different Conditions • Seeing • Filters • Uniform PreProcessing • Apply Different Algorithms & Implementations • Define “Universal” Figure of Merit

  29. ABSOLUTE REFERENCE • Has Assumed We Know “God’s Truth” • Never Available for our Area - IR • Therefore Need Calibration Procedures • Multiple “Independent” Exposures of Same RoI • Simultaneous Information on PSF • Information on IsoPlanatic Patch Parameters • Discuss PAPAO Procedures

  30. STELLAR CAL. PROCEDURES • Dense Globular Clusters of Stars - 47 TUC • Large Number of Stars in FoV • Contains Sample of Very Close Stars • Bright Stars Available for NGS • Four Overlapping (Series of) Exposures • Slightly Different Pointings • Process Each Exposure Independently • Compare Differences in the Magnitudes

  31. Performance ViewGraph

  32. Anisoplanatism Guide Star Target High altitude layer Guide Star Off-axis star  40 arcsec SRon-axis / SRoff-axis  3 FWHMoff-axis / FWHMon-axis  2 Telescope aperture

  33. SPATIAL VARIATIONS OF PSFTrapezium Cluster • Main features • UHAO - K’ band • High Strehl • 1'×1' field • PSF variation at the frame border

  34. How to solve the problem? • Partition the field and obtain a local PSF estimate for eachsub-region • Reconstruct the PSF on the basis of control-loop data(Fusco et al. 2000) • Approximate the anisoplanatic kernel with a parametricmodel to be calibrated • ...

  35. ANISOPLANATISM • Have Assumed Same PSF • Observe Through Different Atmosphere • Then Mirror not Corrected for Off-Axis Effects • PSF Changes with Distance from NGS • Problem with PSF Photometry • Existing Procedures are Patch by Patch • Need PSF & PMS in Each Patch • STARFINDER - E. Diolaiti

  36. Anisoplanatic kernel • Long exposure PSF(Fusco et al. (2000), Voitsekhovich et al. (1999)) • Kernel shape off-axis star guide star kernel

  37. Trapezium

  38. ISO-PLANATIC MODELLING

  39. ANISOPLANATIC TESTING • Demonstrated that SV STARFINDER Works • Asymmetric Gaussian Convolution Describes Data • Integrated into STARFINDER Code • Effective Removal of AnIsoPlanatic “Binaries” • Need Demonstration of Precision • Rigaut Data • Need Demonstration of Accuracy • Systematic Effects • ADONIS Trapezium Data • Need GUI Interface

  40. DATA SETS FOR STELLARANALYSIS OF ANISOPLANATISM • Select RoI • Three Natural Guide Stars • Very Bright for High Strehl Ratios • Within 25 arc seconds of RoI • Observe RoI with Each NGS • Correct Using STARFINDER • Define Next Generation STARFINDER

  41. PAPAO DATA SETS • Crowded Field Stellar Photometry • High Density – Dual Field – 99 Nov • High Density – Quad Field • Intermediate Density – Quad Field • Extended Target – Blind/Myopic Deconvolution • Single Field – 01 Jan • Dual Field • Extended Target – Lucy Richardson • AnIsoPlanatism – Stellar Targets • AnIsoPlanatism – Extended Targets

  42. CONCLUSIONS • PAPAO Program • Collection of Many Well-Calibrated Data Sets • STARFINDER • Intercomparison of Stellar Photometry Programs • Publically Distributed and Available • STARFINDER • Spatially Invariant – Public Release – GUI • Spatially Variant – Concept Demonstrated • Extended Targets (eta Carinae) • Data Sets for Deconvolution (Distributed) • Data Sets for Spatially Variant Deconvolution

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