irac centroiding · 2014. 7. 1. · irac centroiding nikole k. lewis! sagan postdoctoral fellow!...

IRAC Centroiding

Nikole K. Lewis Sagan Postdoctoral Fellow

MIT/EAPS

The Astrophysical Journal, 766:95 (22pp), 2013 April 1 Lewis et al.

Figure 17. Spitzer IRAC PRFs at 3.6, 4.5, 5.8, and 8.0 µm. The PRFs were generated by the IRAC team from bright calibrations stars observed at several epochs. ThePRFs are displayed using a logarithmic scaling to highlight their differences in each bandpass.

effects may be needed in each of the four IRAC bands. Both the3.6 and 4.5 µm channels of the IRAC instrument are shortwardof the Spitzer 5.5 µm diffraction limit (Gehrz et al. 2007) andexhibit undersampled PRFs whose shape changes as the stellarcentroid moves from the center to the edge of a pixel causingintrapixel sensitivity variations. The PRFs in the 5.8 and 8.0 µmchannels of the IRAC instrument exhibit a more Gaussian-likeshape that is better sampled by the detector resulting in onlysmall intrapixel sensitivity variations.

Ideally, we would like to account for these changes in the PRFin our photometric measurements. We cannot make a directdetermination of the exact shape of the PRF as a function ofpixel position, but we can measure the normalized effective-background area of the PRF (β), which is also called noise pixelsby the IRAC Instrument Team. If we assume the measured fluxin a given pixel (Fi) is given by

Fi = F0Pi (A1)

where F0 is the point source flux and Pi is PRF in the ith pixel.The noise pixel parameter, β, is given by

β = (!

Fi)2

! "F 2

i

# = (!

F0Pi)2!

(F0Pi)2= (

!Pi)2

!"P 2

i

# (A2)

since F0 can be assumed to be constant. The numerator inEquation (A2) is simply the square of the PRF volume (V)and the denominator is the effective background area (β).These quantities are related to the sharpness parameter, S1, firstintroduced by Muller & Buffington (1974) for constraining AOcorrections by

β = βV 2 = 1S1

. (A3)

The S1 parameter describes the “sharpness” of the PRF and canrange from zero (flat stellar image) to one (all the stellar flux inthe central pixel).

As discussed in Mighell (2005), the S1 parameter is relatedto the standard deviation (σ ) of the PRF by

S1 = 1C1σ 2

(A4)

where C1 is a constant that depends on the sampling of the PRFon the detector. From Equations (A4) and (A3) it can be shownthat

σ ∝$

β. (A5)

For both the 3.6 and 4.5 µm data we measure β with the samecircular aperture sizes used to determine the stellar centroidposition.

We find that β can serve two purposes in improving thesignal-to-noise of the 3.6 µm observations. First, using a circularaperture with a radius proportional to

√β reduces the overall

variations in raw unbinned flux from ∼5% to ∼2%. Harris(1990) and Pritchet & Kline (1981) note that the optimalaperture radius for a circularly symmetric Gaussian with astandard deviation of σ is r0 ≈ 1.6 σ , which is similar to ouroptimal aperture radius r0 ≈ σ ≈

√β. Second, we find that

using√

β as an additional spatial constraint in the intrapixelsensitivity correction at 3.6 µm can improve the accuracy, asdefined by the standard deviation of the residuals, in our finalsolution by ∼1%–2%. We find that using β as an additionalconstraint for the 4.5 µm observations does not significantlyimprove the accuracy of our results. This is not surprising sincethe IRAC 4.5 µm channel is closer to the Spitzer diffractionlimit of 5.5 µm (Gehrz et al. 2007). We also find that β doesnot vary with stellar centroid position in the 5.8 and 8.0 µmobservations, which are longward of the Spitzer of the diffractionlimit.

APPENDIX B

INTRAPIXEL SENSITIVITY CORRECTION

The 3.6 and 4.5 µm channels of the Spitzer IRAC instrumentboth exhibit variations in the measured flux that are stronglycorrelated with the position of the star on the detector (Figures 1and 2). These flux variations are the result of well documentedintrapixel sensitivity variations that are exacerbated by anundersampled PRF (e.g., Reach et al. 2005; Charbonneau et al.2005, 2008; Morales-Calderon et al. 2006; Knutson et al.2008). The most common method used to correct intrapixelsensitivity induced flux variations is to fit a polynomial functionof the stellar centroid position (Reach et al. 2005; Charbonneauet al. 2005, 2008; Morales-Calderon et al. 2006; Knutson et al.2008). This method works reasonably well on short timescales(<10 hr) where the variations in the stellar centroid position aresmall (<0.2 pixels). Recently, studies by Ballard et al. (2010)and Stevenson et al. (2012) have implemented non-parametriccorrections for intrapixel sensitivity variations by creating pixel“maps,” which give a smaller scatter in the residuals comparedwith parametric models in most cases.

The pixel mapping method of Ballard et al. (2010) usesa Gaussian low-pass spatial filter to estimate the weightedsensitivity function given by

W (xi, yi) =!n

j =i Ki(j ) × F0,j!nj =i Ki(j )

(B1)

20

Common Centroiding Methods

• Flux-weighted/Center of Light

!

• Fitting 2D Gaussian/Moffat

!

• Least Asymmetry

2

The scatter in centering results indicates the precision,but not the accuracy, of a centering technique when usedon real data. To assess accuracy (robustness againstsystematic o↵sets) we created synthetic datasets withknown centers, varying S/N and center location within apixel.

2.1. Synthetic Data

We created three synthetic data sets, one based on theIRAC 8.0 µm channel point response function (PRF),oneon the IRAC 3.6 µm channel PRF, and the third ona Gaussian distribution with parameters chosen to ap-proximate the PRF, each oversampled by a factor of onehundred. A PRF is analogous to a PSF, but includes theinherent wavelength-dependent sensitivity of the pixelson the detector as well as the optical distortion. Withthe PRF we are able to model real observations, whilethe Gaussian represents a common, if not perfectly jus-tified, approximation to a PSF that is easy to calculateand su�cient for our purposes. It is important to notethat the values obtained using these distribution func-tions are specific only to the functions used. Other dis-tribution functions, which may vary in shape, degree ofsampling, or detector response per pixel, will necessarilyhave di↵erent quantitative results. We use these threefunctions to look at the qualitative di↵erences and per-formance of each centering routine on idealized and realdistribution functions.We shifted the three oversampled distribution kernels

in a 21⇥21-position grid with shift steps of 5 sub-pixels.This produced 441 di↵erent images. The Gaussian ker-nel was then averaged such that the subpixels fell withinoriginal full-pixel boundaries to simulate light fallingonto the detector in slightly di↵erent locations, while thePRFs were downsampled according to the procedure laidout in the IRAC handbook (IRAC Instrument and In-strument Support Teams 2013). These frames reflect thePSF in di↵ering pixelation senarios. For each of thesebinned kernels, we applied normally distributed noise ona per-pixel basis. In order to determine the per-pixelvariance for each Gaussian distribution, we solved thestandard charge-coupled device equation (Equation 1)for a signal level I, with a given background (bg), de-tector noise (�

d

), and number of pixels (npix

), such thatthe per-pixel intensity and additional sources of errorwhen summed satisfy the equation for a given S/N. Theunits on I and bg are both number of photoelectronsper observation. We make note that in this investigationthe background (bg) encompasses all di↵use sources suchas zodical light and galactic nebulae. This process wasrepeated seventy-five times to generate a statistically sig-nificant sample size. Finally, we varied the S/N to inves-tigate the e↵ect that di↵erent observing conditions mayhave on centering.

S

N

=Ip

I + n

pix

(bg + �

2d

). (1)

Altogether we had three data sets, Gaussian and twoPRFs, with 19 S/Ns, each with 441 subpixel locationscontaining 75 frames with separately drawn randomnoise, for a total of 1,256,850 samples.

2.2. Methods and Analysis

In aperture photometry, we wish to maximize the num-ber of photons that come from the body of interest, whileminimizing those contributed by the background. Thisnormally involves considering all flux within a certain re-gion to be flux from the object. This leads to the issue ofdetermining the center of the region of interest from thefinite sampling provided by detector pixels. Below wediscuss three methods, all of which solve this problem,but di↵erently.

2.2.1. Center of Light

Determining the center of a distribution using thecenter-of-light method is computationally and conceptu-ally straightforward. The functional form is the same asthat for center of mass,

X =⌃

i

m

i

x

i

⌃j

m

j

, Y =⌃

i

m

i

y

i

⌃j

m

j

, (2)

where m

i

is the “mass” or weight at each point, and y

i

,

and x

i

are the distances of the points away from the ori-gin in the y and x directions, respectively. This performsan average in each dimension weighted by the amount of“mass” present at each location, resulting in the centerlocation about which the mass is distributed. In thiscase, however, the “mass” at each point is the flux con-tained in a given pixel. This method is also commonlyknown as centroiding. However, a center-of-mass calcu-lation is only equivalent to a geometric centroid when theobject has constant density, such as determining the cen-ter for an irregularly shaped but uniformly dense block.Since this is not the case for a stellar image, as theamount of mass (light) per unit volume (pixel) is notconstant, we avoid the term in this paper.The major advantage to this centering routine is that

it assumes no inherent distribution of the light. As aresult, it works for irregular shapes; however, this is alsoits weakness. By assuming nothing about the object’sshape, the routine is very sensitive to the noise in theimage. Where a nearby warm pixel might not seriouslya↵ect a Gaussian fit, it would weight the center-of-lightaway from the true center.

2.2.2. Gaussian Centering

Assuming that the PRF combined with noise is axiallysymmetric, it is possible to derive sensible centers bymodeling it with a two-dimensional (2D) Gaussian,

G = Ae

� 1

2

( (x�µ

x

)

2

�

2

x

+(y�µ

y

)

2

�

2

y

), (3)

where A is a scaling constant, x and y are the positionof a pixel, µ

x

and µ

y

are the true positions of the sourcein x and y, and the variances in x and y are given by�

2x

, �2y

. Because the center is found by fitting an ana-lytic function, this routine is fairly robust against lowvariations, or a small number of larger variations, due tonoise. However, by assuming the distribution functionis symmetric, this routine will produce inaccurate cen-ters when the distribution is asymmetric. This deviationmay arise from insu�cient signal, asymmetric pixelation,speckles, or an inherently asymmetric PRF.

2.2.3. Least Asymmetry

2

The scatter in centering results indicates the precision,but not the accuracy, of a centering technique when usedon real data. To assess accuracy (robustness againstsystematic o↵sets) we created synthetic datasets withknown centers, varying S/N and center location within apixel.

2.1. Synthetic Data

We created three synthetic data sets, one based on theIRAC 8.0 µm channel point response function (PRF),oneon the IRAC 3.6 µm channel PRF, and the third ona Gaussian distribution with parameters chosen to ap-proximate the PRF, each oversampled by a factor of onehundred. A PRF is analogous to a PSF, but includes theinherent wavelength-dependent sensitivity of the pixelson the detector as well as the optical distortion. Withthe PRF we are able to model real observations, whilethe Gaussian represents a common, if not perfectly jus-tified, approximation to a PSF that is easy to calculateand su�cient for our purposes. It is important to notethat the values obtained using these distribution func-tions are specific only to the functions used. Other dis-tribution functions, which may vary in shape, degree ofsampling, or detector response per pixel, will necessarilyhave di↵erent quantitative results. We use these threefunctions to look at the qualitative di↵erences and per-formance of each centering routine on idealized and realdistribution functions.We shifted the three oversampled distribution kernels

in a 21⇥21-position grid with shift steps of 5 sub-pixels.This produced 441 di↵erent images. The Gaussian ker-nel was then averaged such that the subpixels fell withinoriginal full-pixel boundaries to simulate light fallingonto the detector in slightly di↵erent locations, while thePRFs were downsampled according to the procedure laidout in the IRAC handbook (IRAC Instrument and In-strument Support Teams 2013). These frames reflect thePSF in di↵ering pixelation senarios. For each of thesebinned kernels, we applied normally distributed noise ona per-pixel basis. In order to determine the per-pixelvariance for each Gaussian distribution, we solved thestandard charge-coupled device equation (Equation 1)for a signal level I, with a given background (bg), de-tector noise (�

d

), and number of pixels (npix

), such thatthe per-pixel intensity and additional sources of errorwhen summed satisfy the equation for a given S/N. Theunits on I and bg are both number of photoelectronsper observation. We make note that in this investigationthe background (bg) encompasses all di↵use sources suchas zodical light and galactic nebulae. This process wasrepeated seventy-five times to generate a statistically sig-nificant sample size. Finally, we varied the S/N to inves-tigate the e↵ect that di↵erent observing conditions mayhave on centering.

S

N

=Ip

I + n

pix

(bg + �

2d

). (1)

Altogether we had three data sets, Gaussian and twoPRFs, with 19 S/Ns, each with 441 subpixel locationscontaining 75 frames with separately drawn randomnoise, for a total of 1,256,850 samples.

2.2. Methods and Analysis

In aperture photometry, we wish to maximize the num-ber of photons that come from the body of interest, whileminimizing those contributed by the background. Thisnormally involves considering all flux within a certain re-gion to be flux from the object. This leads to the issue ofdetermining the center of the region of interest from thefinite sampling provided by detector pixels. Below wediscuss three methods, all of which solve this problem,but di↵erently.

2.2.1. Center of Light

Determining the center of a distribution using thecenter-of-light method is computationally and conceptu-ally straightforward. The functional form is the same asthat for center of mass,

X =⌃

i

m

i

x

i

⌃j

m

j

, Y =⌃

i

m

i

y

i

⌃j

m

j

, (2)

where m

i

is the “mass” or weight at each point, and y

i

,

and x

i

are the distances of the points away from the ori-gin in the y and x directions, respectively. This performsan average in each dimension weighted by the amount of“mass” present at each location, resulting in the centerlocation about which the mass is distributed. In thiscase, however, the “mass” at each point is the flux con-tained in a given pixel. This method is also commonlyknown as centroiding. However, a center-of-mass calcu-lation is only equivalent to a geometric centroid when theobject has constant density, such as determining the cen-ter for an irregularly shaped but uniformly dense block.Since this is not the case for a stellar image, as theamount of mass (light) per unit volume (pixel) is notconstant, we avoid the term in this paper.The major advantage to this centering routine is that

it assumes no inherent distribution of the light. As aresult, it works for irregular shapes; however, this is alsoits weakness. By assuming nothing about the object’sshape, the routine is very sensitive to the noise in theimage. Where a nearby warm pixel might not seriouslya↵ect a Gaussian fit, it would weight the center-of-lightaway from the true center.

2.2.2. Gaussian Centering

Assuming that the PRF combined with noise is axiallysymmetric, it is possible to derive sensible centers bymodeling it with a two-dimensional (2D) Gaussian,

G = Ae

� 1

2

( (x�µ

x

)

2

�

2

x

+(y�µ

y

)

2

�

2

y

), (3)

where A is a scaling constant, x and y are the positionof a pixel, µ

x

and µ

y

are the true positions of the sourcein x and y, and the variances in x and y are given by�

2x

, �2y

. Because the center is found by fitting an ana-lytic function, this routine is fairly robust against lowvariations, or a small number of larger variations, due tonoise. However, by assuming the distribution functionis symmetric, this routine will produce inaccurate cen-ters when the distribution is asymmetric. This deviationmay arise from insu�cient signal, asymmetric pixelation,speckles, or an inherently asymmetric PRF.

2.2.3. Least Asymmetry

3

Figure 1. Asymmetric image for which the center is to be deter-mined. The asymmetry value will be calculated for each pixel inthe shaded region near the center of the image.

Least asymmetry was originally devised by JamesGunn (Princeton University) for use in radio astronomy.Russell Owen (University of Washington) then used it ina software package1 to drive the pointing of telescopes,but did not explore it much further. In personal com-munications with Owen, he shared the methods, back-ground, and algorithm with us. A non-exhaustive searchthrough the literature did not produce references, so wepresent the full algorithm here with examples.Up to this point, we have discussed determining centers

using a weighted average and minimizing a functionalmodel. Least asymmetry accomplishes centering by firstperforming a transformation on the data to produce anew space with di↵erent properties, similar in idea to a2D cross correlation.We begin with an asymmetric distribution that is a

result of the optical aberrations in the system as well asthe presence of noise in the measured signal. Figure 1depicts such an asymmetric signal. We propose that thecenter is the point about which the distribution is mostsymmetric. We define the asymmetry of the signal as afunction of position as

A(x, y) =RX

0

V ar(�(r)) ⇤N(r), (4)

where the discrete index r indicates the particular ra-dial bin, Var is the variance operator, � is the flux,and N(r) gives the number of pixels at a given radiusfor weighting. Equation 4 produces a new space that ismore normally and symmetrically distributed, as demon-straited by our results when fitting a Gaussian to theflux space vs asymmetry space. In principle, if this werea continuous dataset, we could continue this process un-til the point of minimum asymmetry was absolutely de-termined. Because datasets collected using imaging ar-rays are discretely sampled, we take advantage of theincreased symmetry and use established centering rou-tines to determine the point of minimum asymmetry tosub-pixel accuracy.

1http://www.astro.washington.edu/users/rowen/PyGuide/Manual.html

Figure 2. Asymmetric image with thatched region to indicate theregion of transform for the dotted pixel

To determine the point of minimum asymmetry, wecalculate the value of asymmetry about each pixel in theshaded region in Figure 1. The calculation begins by lay-ing an aperture about a given pixel as indicated in Figure2. This region, which we note extends outside the win-dow undergoing transformation, is used to create a radialprofile, (see Figure 3), with discrete radii correspondingto the pixel centers, as shown in Figure 4. The radialprofile for the particular pixel shown in Figure 2 is givenin Figure 5. Because we only choose radii correspondingto pixel centers, the radial bins are groupings of points atdiscrete distances. As a general example of asymmetry,Figure 3 shows profiles corresponding to low (top) andhigh (bottom) asymmetry.The generated radial profile is used in conjunction with

Equation 4, to calculate the value of asymmetry. We re-peat this process for each pixel in the conversion windowto produce the asymmetry values depicted in Figure 6.Finally, we use a traditional centering method in asym-metry space to determine the center with sub-pixel ac-curacy. In our cursory tests, we determined Gaussiancentering performed better than center of light.It is improper to do photometry on Figure 6 as the

values correspond to sums of variances and not flux val-ues. The figure does not represent a cleaned up imagebut is merely an array representation of the distributionof asymmetry values of the original image. The center asdetermined from this distribution must be used in fluxspace to determine photometric measurements.

3. RESULTS

We applied the three methods to our three syntheticdatasets to determine which performed the best underdi↵erent circumstances. Although specific values di↵eredbetween the two PRFs, qualitatively the results are sim-ilar. As we are more concerned with the qualitative re-sults of the centering routines we will present only the 8µm results in this section for comparison. Similar figuresfor 3.6 µm are available in Appendix 6.To test the precision and accuracy of these routines, we

ran each method against the synthetic data described inSection 2.1 to produce calculated x and y values for eachframe, a total of over 3.6 million centering calculations.

IRAC CH1 - HD 149026

IRAC CH1 - HAT-P-2b

5285.0 5285.2 5285.4 5285.6 5285.8 5286.0 5286.2BJDUTC-2450000

14.4

14.6

14.8

15.0

15.2

Y Po

sitio

n (p

ix)

Flux-weighted

IRAC CH1 - HAT-P-2b

5285.0 5285.2 5285.4 5285.6 5285.8 5286.0 5286.2BJDUTC-2450000

14.4

14.6

14.8

15.0

15.2Y

Posi

tion

(pix

)

gcntrd

Supplementary Figure 1. Three centring methods track the vertical position of GJ

436b for a small portion of the 3.6 Pm data. For this dataset, the Gaussian centring

method most precisely tracks the spacecraft pointing. Small pointing oscillations occur

on a ~5-second timescale. Gaps occur every 64 frames as the camera transfers data to the

spacecraft's data system.

www.nature.com / nature 3

IRAC CH1 - GJ 436

Stevenson et al. (2010)

4

Figure 3. Stellar radial profiles. Top: When the profile is cen-tered on the star, the variance in each radial bin is small, indicatinglow asymmetry. Bottom: When the profile is centered five pixelsfrom the stellar center, the variance in each bin is high becausethe light is asymmetrically distributed about this point. The leastasymmetry method works by minimizing the sum of the variancesin all of the radial bins.

Figure 4. Radii used to generate radial profile in the region oftransformation.

To compare results we create images, see Figure 7, inwhich the axes correspond to the sub-pixel location andthe values correspond to the figure of merit: the meandistances of the position residuals.Tables 1 and 2 summarize these data. The values pre-

Figure 5. Radial profile corresponding to our region of transfor-mation. Red numbers correspond to the labels of radii shown inFigure 4

Figure 6. Image of asymmetry space for the region convertedfrom Figure 1.

sented are the average over all subpixel locations, indi-cating the total expected error. The optimum method ateach S/N is shaded gray.Table 1 gives the results for centering the Gaussian ker-

nel. Least asymmetry performs the best in the noisiestconditions, and Gaussian centering performs best at highS/N. This is expected given that the distribution is con-structed from a Gaussian kernel. At low S/N, Gaussianfitting and center of light are more easily thrown o↵. Inthe case of least asymmetry, however, the transformationconverts to a space that is more normal, so the signal inthe asymmetry space is stronger. As the signal continuesto increase in strength, fitting a Gaussian becomes moreaccurate at a rate faster than that of asymmetry. Centerof light not only performs the worst in all cases, but itsaccuracy improves at a rate much slower than that of theother two routines.The deviation of the Spitzer 8 µm PRF from normal

is evident in Table 2. For the lowest-S/N scenarios thedistribution deviates furthest from normal and fitting aGaussian in flux or asymmetry space is out-performed

Lust et al., ApJ submitted

Testing Centroiding Methods

IRAC CH1& CH4 - Method Compare8

6. FIGURES AND TABLE OF IRAC 3.6 µM ANALYSIS

Table 33.6 µm PRF Kernel Mean Positional Error (Pixels)

S/N Asymmetry Gaussian Center of Light1.0 0.606 68.9433 0.44662.0 0.203 0.8195 0.43553.0 0.1556 0.2031 0.42494.0 0.1427 0.1599 0.41475.0 0.1369 0.1476 0.4056.0 0.1337 0.1426 0.39577.0 0.1318 0.1395 0.38688.0 0.1305 0.139 0.37839.0 0.1295 0.1371 0.370110.0 0.1288 0.1362 0.361820.0 0.1264 0.1334 0.297230.0 0.1259 0.1325 0.250940.0 0.1256 0.1324 0.216250.0 0.1255 0.1326 0.189460.0 0.1254 0.1322 0.168270.0 0.1254 0.1321 0.151180.0 0.1253 0.132 0.137190.0 0.1253 0.132 0.1255100.0 0.1253 0.1319 0.1255

Figure 10. Goodness of centering in sub-pixel space. Results for Least Asymmetry (top left), Gaussian (top right), and center of light(bottom left) for the 3.6 µm PRF at S/N 10. Blue represents better centering, red worse. The cross pattern arises from a combination ofa pixelation e↵ect and the non-uniformity of the PRF.

5

Figure 7. Centering quality in sub-pixel space for least asymme-try (top), Gaussian (center), and center of light (bottom) for the8 µm PRF at S/N 10. Blue represents better centering, red worse.The cross pattern arises from a combination of a pixelation e↵ectand the non-uniformity of the PRF.

by center of light, though the centering still has large er-rors. Once the signal is discernible over the background,asymmetry is the preferred choice for all of the test cases.Each of the routines tested all show improvement withincreased S/N, as would be expected, each converging atdi↵erent rates in both precession and accuracy.Gaussian centering is noticeably lacking throughout

Table 2 because the asymmetric light distribution skewsthe Gaussian centering consistently away from the cor-

Table 1Gaussian Kernel Mean Positional Error (Pixels)


Table 28 µm PRF Kernel Mean Positional Error (Pixels)


rect answer. The Gaussian centering routine was veryprecise, but not accurate, giving it a larger sum ofsquared residuals. This e↵ect is illustrated in Figure8, where the residuals of Gaussian centering are moretightly packed than the residuals from asymmetry. If itcould be accurately explored for a given distribution oflight at particular gains and S/Ns, it might be possibleto develop corrections to be used with Gaussian center-ing to make it more accurate. These corrections would,however, only be good for the particular PRF, S/N, andsub-pixel location. In the lowest-S/N cases, least asym-metry proves to be both more precise and more accuratethan Gaussian centering

4. CONCLUSIONS AND FUTURE WORK

Tables 1 and 2 show which centering methods per-form better under particular circumstances and are notintended as a comprehensive guide for which centeringmethod is the best. A di↵erent PRF will necessarilyhave di↵erent results, such as can be seen in Appendix6.However, these tables are useful for gaining a general

understanding of these centering routines. First, we notethat centering with asymmetry performs well under a va-

Lust et al., ApJ submitted

IRAC CH1 - Method Comparempfit2dpeak.pro

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Figure Credit: Syler Wagner



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IRAC CH1 - Centroid Errors

Figure Credit: Ken Mighell

box centroider

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IRAC CH1 - Compare Results

Flux Weighted

Gaussian Fit

Concluding Thoughts• Choice of centroiding method generally affects error budget at the few percent level !

• Choice of centroiding method generally does not affect retrieved system parameters (within errors) !

• Flux-weighted methods appear most ‘stable’ over a variety datasets (centroid location, S/N, etc.) !

•Implementation between groups varies ...The Astrophysical Journal, 766:95 (22pp), 2013 April 1 Lewis et al.

Figure 17. Spitzer IRAC PRFs at 3.6, 4.5, 5.8, and 8.0 µm. The PRFs were generated by the IRAC team from bright calibrations stars observed at several epochs. ThePRFs are displayed using a logarithmic scaling to highlight their differences in each bandpass.

effects may be needed in each of the four IRAC bands. Both the3.6 and 4.5 µm channels of the IRAC instrument are shortwardof the Spitzer 5.5 µm diffraction limit (Gehrz et al. 2007) andexhibit undersampled PRFs whose shape changes as the stellarcentroid moves from the center to the edge of a pixel causingintrapixel sensitivity variations. The PRFs in the 5.8 and 8.0 µmchannels of the IRAC instrument exhibit a more Gaussian-likeshape that is better sampled by the detector resulting in onlysmall intrapixel sensitivity variations.

Ideally, we would like to account for these changes in the PRFin our photometric measurements. We cannot make a directdetermination of the exact shape of the PRF as a function ofpixel position, but we can measure the normalized effective-background area of the PRF (β), which is also called noise pixelsby the IRAC Instrument Team. If we assume the measured fluxin a given pixel (Fi) is given by

Fi = F0Pi (A1)

where F0 is the point source flux and Pi is PRF in the ith pixel.The noise pixel parameter, β, is given by

β = (!

Fi)2

! "F 2

i

# = (!

F0Pi)2!

(F0Pi)2= (

!Pi)2

!"P 2

i

# (A2)

since F0 can be assumed to be constant. The numerator inEquation (A2) is simply the square of the PRF volume (V)and the denominator is the effective background area (β).These quantities are related to the sharpness parameter, S1, firstintroduced by Muller & Buffington (1974) for constraining AOcorrections by

β = βV 2 = 1S1

. (A3)

The S1 parameter describes the “sharpness” of the PRF and canrange from zero (flat stellar image) to one (all the stellar flux inthe central pixel).

As discussed in Mighell (2005), the S1 parameter is relatedto the standard deviation (σ ) of the PRF by

S1 = 1C1σ 2

(A4)

where C1 is a constant that depends on the sampling of the PRFon the detector. From Equations (A4) and (A3) it can be shownthat

σ ∝$

β. (A5)

For both the 3.6 and 4.5 µm data we measure β with the samecircular aperture sizes used to determine the stellar centroidposition.

We find that β can serve two purposes in improving thesignal-to-noise of the 3.6 µm observations. First, using a circularaperture with a radius proportional to

√β reduces the overall

variations in raw unbinned flux from ∼5% to ∼2%. Harris(1990) and Pritchet & Kline (1981) note that the optimalaperture radius for a circularly symmetric Gaussian with astandard deviation of σ is r0 ≈ 1.6 σ , which is similar to ouroptimal aperture radius r0 ≈ σ ≈

√β. Second, we find that

using√

β as an additional spatial constraint in the intrapixelsensitivity correction at 3.6 µm can improve the accuracy, asdefined by the standard deviation of the residuals, in our finalsolution by ∼1%–2%. We find that using β as an additionalconstraint for the 4.5 µm observations does not significantlyimprove the accuracy of our results. This is not surprising sincethe IRAC 4.5 µm channel is closer to the Spitzer diffractionlimit of 5.5 µm (Gehrz et al. 2007). We also find that β doesnot vary with stellar centroid position in the 5.8 and 8.0 µmobservations, which are longward of the Spitzer of the diffractionlimit.

APPENDIX B

INTRAPIXEL SENSITIVITY CORRECTION

The 3.6 and 4.5 µm channels of the Spitzer IRAC instrumentboth exhibit variations in the measured flux that are stronglycorrelated with the position of the star on the detector (Figures 1and 2). These flux variations are the result of well documentedintrapixel sensitivity variations that are exacerbated by anundersampled PRF (e.g., Reach et al. 2005; Charbonneau et al.2005, 2008; Morales-Calderon et al. 2006; Knutson et al.2008). The most common method used to correct intrapixelsensitivity induced flux variations is to fit a polynomial functionof the stellar centroid position (Reach et al. 2005; Charbonneauet al. 2005, 2008; Morales-Calderon et al. 2006; Knutson et al.2008). This method works reasonably well on short timescales(<10 hr) where the variations in the stellar centroid position aresmall (<0.2 pixels). Recently, studies by Ballard et al. (2010)and Stevenson et al. (2012) have implemented non-parametriccorrections for intrapixel sensitivity variations by creating pixel“maps,” which give a smaller scatter in the residuals comparedwith parametric models in most cases.

The pixel mapping method of Ballard et al. (2010) usesa Gaussian low-pass spatial filter to estimate the weightedsensitivity function given by

W (xi, yi) =!n

j =i Ki(j ) × F0,j!nj =i Ki(j )

(B1)

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irac centroiding · 2014. 7. 1. · irac centroiding nikole k. lewis! sagan postdoctoral fellow!...

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