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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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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

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


Starfinder

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


Why a new code

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)


Guidelines

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)


Psf extraction

PSF extraction

select PSF stars

subtract background

center

normalize

median of stack

post-process


Main loop flowchart

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


Analysis of an object 1

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


Analysis of an object 2

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


Analysis of an object 3

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


Analysis of an object 4

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


Examples of blends

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)


De blending strategies

De-blending strategies

  • Main loop iteration

  • Others

    • thresholding

    • subtraction

-

=

vs. PSF

-

=


Correlation coefficient

Correlation coefficient

  • Definition

  • Maximize correlation to

    • measure similarity between object and reference

    • improve positioning accuracy


Astrometry and photometry

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


Fitting procedure derivatives

Fitting procedure: derivatives

See also Véran & Rigaut (1998)


Background estimation

Background estimation

1

2

3

6

5

4


Saturated stars

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


Noise estimation

Noise estimation

  • Photon noise + instrumental noise

  • Histogram fitting to estimatenormally-distributed noise


Gui interface

GUI interface

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


Code structure

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


Results on simulated data

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


Results on real data galactic center

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


Trapezium adonis

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


Results on trapezium

Results on Trapezium

Image

Reconstruction

PSF stars


Classes of targets

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


Trapezium

Trapezium


Eso papao approach

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


Absolute reference

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


Stellar cal procedures

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


Performance viewgraph

Performance ViewGraph


Anisoplanatism

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


Spatial variations of psf trapezium cluster

SPATIAL VARIATIONS OF PSFTrapezium Cluster

  • Main features

    • UHAO - K’ band

    • High Strehl

    • 1'×1' field

    • PSF variation at the frame border


How to solve the problem

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

  • ...


Anisoplanatism1

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


Anisoplanatic kernel

Anisoplanatic kernel

  • Long exposure PSF(Fusco et al. (2000), Voitsekhovich et al. (1999))

  • Kernel shape

off-axis star

guide star

kernel


Trapezium1

Trapezium


Iso planatic modelling

ISO-PLANATIC MODELLING


Anisoplanatic testing

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


Data sets for stellar analysis of anisoplanatism

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


Papao data sets

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


Conclusions

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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