NICMOS count-rate dependent non- linearity tests ... - Semantic Scholar

Instrument Science Report NICMOS 2006-001

NICMOS count-rate dependent nonlinearity tests using flatfield lamps Roelof S. de Jong, Eddie Bergeron, Adam Riess, Ralph Bohlin February 15, 2006

ABSTRACT We investigate the recently discovered NICMOS count-rate dependent non-linearity (Bohlin et al. 2005) using the flatfield lamps to artificially increase the count rate. A star cluster field was imaged in a lamp off-on-off sequence in all cameras in a selected set of filters, followed by a series of darks to investigate persistence and to clean the images from any remaining charge for the next orbit. Subtracting the lamp-off images from the lamp-on images clearly shows residual ADUs at the star positions, indicating that a higher background (and thus total) count rate increases the number of ADUs registered from an object. We model the non-linearity with a power law (count-rate ∝ fluxα) and fit this model to the data. Both NIC1 and NIC2 (NIC3 was not tested in this program) show non-linearity, becoming stronger at shorter wavelengths, but with larger amplitude than predicted by the Bohlin et al. NIC3 measurements. The non-linearity in NIC1 and NIC2 amounts to 0.06-0.10 mag offset per factor ten change in incident flux for the shortest wavelength (F090M and F110W), about 0.03 mag/dex at F160W, and less at longer wavelengths. Archival data from Cycle 7 are also analyzed, showing that the nonlinearity has not changed in NIC2 F110W, and suggesting that this effect is independent of detector temperature.

Introduction In a recent analysis of NICMOS, STIS and ACS spectral data Bohlin et al. (2005) have shown that NICMOS shows a systematic count rate dependent non-linearity, primarily at the shorter wavelengths. The same spectra show a similar non-linearity when compared

1 Operated by the Association of Universities for Research in Astronomy, Inc., for the National Aeronautics and Space Administration

to ACS photometry. However, the NICMOS spectra in general show good agreement with the NICMOS photometry of the same objects, indicating that the NICMOS system is internally consistent and that it is not the spectral data reduction that is at fault. A few more indications have been found that NICMOS suffers from a non-linearity dependent on the incoming flux: 1) narrowband filters at the shorter wavelengths required larger corrections from their ground-based determined throughputs than the broadband filters, 2) high redshift supernova fluxes are slightly fainter in F110W than expected based on their ACS fluxes and well tested SN models (Adam Riess, private communication), and 3) galaxies in the HUDF are slightly fainter than expected based on ACS and groundbased J&K magnitudes combined with SED modeling (Mobasher & Riess 2005; Coe et al. 2005). However, all these lines of evidence rely on modeling of filter throughputs and/or spectral modeling of sources. Here we describe a test that depends on the change in incoming flux on the detector alone. NICMOS is a shutterless instrument and observes the sky while obtaining calibration flatfields using its internal lamps. The same object can be observed with an artificially increased flux and the count rate for objects can be compared with and without extra lamplight. For a fully linear system adding a background flux should not enhance the flux in the object, but any flux dependent nonlinearity is revealed immediately when subtracting lamp-off images from lamp-on images.

Data Normal lamp flatfield observations are obtained in empty fields and are not suitable for the current analysis. Because the non-linearity effect is rather small (~5% extra ADU per dex in incoming flux), observations have to be carefully planned to match the count rates from the objects to the count rate from the lamps. Furthermore, to measure such a 5% offset from linearity the lamp background will be 10x brighter than the object flux per pixel, and many pixels and observations must be averaged to obtain the signal-to-noise required to accurately measure the ~5% effect on such a high background. During Cycle 7 a few observations were obtained in focus monitoring program 7901 that happened to be useful for this analysis. These observations were obtained at the end of a focus sweep observation in NIC2 to determine the position of the coronagraphic hole (this program was discontinued because software was developed to determine the hole position during coronographic acquisition). These observations were only obtained in F110W and have very short exposure times (1: cron-croff ∝ (fobj+fsky+flamp)α-(fobj+fsky)α ~ (fobj+flamp)α-(fobj)α, where it is assumed that the sky flux is small compared to the other fluxes. Such image residuals are shown in Figure 1. The absolute boost in measured count rate is largest for bright objects, but the relative increase in measured count rate is larger for lower object fluxes, because the relative increase in flux by switching on the lamp is much larger. However, at low count rates the noise dramatically increases and we have to average many points to see the effect. This is shown in Figure 2, where we plot both the absolute count rate increase

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(cron-croff-crlamp) vs croff Figure 1: (Left) NIC1 F110W lamp-off image of NGC1850. (Right) NIC1 F110W lamp-on minus lamp-off image. Bright stars are clearly not well subtracted and leave residual flux as expected for count rate dependent non-linearity. The worm-like structures are due to residual flat field problems.

as well as the relative count rate increase (cron-croff-crlamp)/croff vs croff . To determine α for a particular camera and filter we have to fit the equation that links the flux measurements in all pixels in the lamp-on image to the lamp-off image: F(x,y)=cron= (fobj+fsky+flamp)α=(croff1/α+ flamp)α. We use a Levenberg-Marquard fitting routine to determine α and the lamp flux, which requires the derivatives of the fitting function: ∂F(x,y)/∂α = (croff1/α+ flamp)α[log(croff1/α+ flamp)- croff1/αlog(croff)/(α( croff1/α+ flamp))] ∂F(x,y)/∂flamp = α(croff1/α+ flamp)α-1 An error weighted fit was performed, excluding from the fits the points in the (poor quality) bottom 20 rows and in the NIC2 choronographic hole region. The σ values in the ERR images of NICMOS pipeline imsets are incorrect, and proper ERR images were calculated by taking the ERR image of the pipeline after the FLATCORR step and add the appropriate photon noise derived from the SCI image. We used the σ values of the lamp-on image for weights, as we are minimizing the differences between the model and the lamp-on image. However, the independent variable, the count rates from the lamp-off image, has also significant noise (mainly read noise at low count rates), and therefore a 5

minimum signal-to-noise cutoff was imposed on the lamp-off input values. Even the improved σ values are actually not completely realistic due to the poor flat fields. We are therefore also determining a σ estimate directly from the data, calculating the r.m.s. in the lamp-on minus lamp-off image. We have been using both σ values to select pixels in the lamp-off image to use in the fitting process. The resulting fits show some dependence on the imposed cutoff using cutoffs between 2 to 3σ, but one would expect that this dependence decreases when the noise is reduced with better flat fields. The results vary by a few percent depending on whether the initial estimate of α fed into the fit routine was lower or higher than the final fit value. Therefore, to find the best α and to determine the uncertainty in the fit, we first made a pass to approximately determine α, and then made fits with a range of initial values of α around this first estimate and with low signal cutoffs in the lamp-off images of 2, 2.5, and 3σ. The α values listed in Table 1 are the median values of all α values thus determined; the errors are the 1-sigma deviations in the distribution of α. As these systematic uncertainties are probably larger than the sampling uncertainties, we did not perform a full bootstrap resampling error analysis of the data. The fitted non-linearity functions are overplotted in Figure 2 and the measured α values are tabulated in Table 1. A number of points can immediately be taken from the Table. The effect becomes more severe at shorter wavelengths, similar as predicted by the analysis of Bohlin et al. (2005). However, NIC1 and NIC2 still have significant nonlinearity at F160W, unlike the Bohlin et al. prediction for NIC3. NIC1 and NIC2 may not have the same non-linearity at all wavelengths, with NIC1 and NIC2 differing by 2σ at F110W and NIC1 being more affected. We do not see any statistical significant difference between the non-linearity in NIC2 F110W as measured in Cycle 7/7n and in the current test taken during Cycle 14. In some of the observations there was some drift in pointing of the telescope. This will add somewhat to the uncertainty of the measurement. While at first sight one would think that binning the data on the array would mitigate this problem. Unfortunately this does not work, because the expected average increase in count rate in the individual pixels combined in a bin is not equal to the expected increase in count rate for the average flux in the binned pixels. This is also the reason why aperture photometry cannot be used to measure α, as within an aperture there will be a range in flux increases, which will not be equal to the increase expected for the average flux. We also investigated the lamp-off images taken before and after the lamp-on images for signs of charge trapping and non-linearity. The lamp-off images taken after the lamp-on images show a decrease in overall background count rate level consistent with persistence. The set of lamp-off images taken of the star cluster after the initial darks generally but before the lamp-on images show a small gradual increase in the background count rate level. Furthermore, the very first image taken after the initial darks seems to

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have lower count rates in the objects compared to the rest of the lamp-off images, fractionally somewhat larger in the fainter objects than the brighter objects. These early lower count rates are suggestive of a charge trapping effect. The count rates stabilize within about one minute, even at the low count rates, and we have to recommend that no low count rate observations should be taken with exposure times less than one minute. However, this small amount of count rate buildup, that has some of the characteristics of charge trapping, does not provide the explanation for the non-linearity measured in this ISR. The amount of non-linearity measured between the lamp-on and lamp-off images is independent from whether all lamp-off images are used, or only the lamp-of images before the lamp-on, or the images after the lamp-on (Table 2). A similar test as presented here using the flatfield lamps to artificially increase the count rates on grism exposures is described in Bohlin et al. (2006). They find a similar wavelength dependent non-linearity in NIC3 as described here, but with a lower amplitude.

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Conclusions NICMOS has a significant count rate dependent non-linearity that also depends on wavelength. This is a different non-linearity from the well-known total count dependent non-linearity. The non-linearity in NIC1 and NIC2 amounts to 0.06-0.10 mag offset per dex change in incident flux for the shortest wavelength (F090M and F110W), about 0.03 mag/dex at F160W and less than that at longer wavelengths. These corrections are larger than predicted from the Bohlin et al. (2005) NIC3 grism results, which may point to intrinsic detector differences or might be the result of a different analysis method. The non-linearity seems to have changed very little from Cycle 7/7n to Cycle 14 (in F110W NIC2), and hence is unlikely to depend on detector temperature. The fact that there is a wavelength dependence to the effect in the lamp off/on/off test and that this trend quantitatively agrees with the grism observations strongly argues against this being the result of a data reduction error and that the cause is intrinsic to the measurement. To what extend NICMOS photometry is affected by the non-linearity depends on the wavelength of the observations (i.e. the α parameter), whether the object is a point source or extended, and on the count rate of the sky background (as the count rate will never go below the sky level and hence the non-linearity will level off, even if the sources have lower count rates). Given that the NICMOS standard stars are of about the 12th magnitude, the maximum expected offset for the UDF for example is about 0.15-0.2 mag at 22 F110W AB-mag, where the objects are comparable to or below the sky count level.

Recommendations Further tests are necessary to measure the effect more accurately, but the NICMOS community should be made aware of these results and their significance for the different kind of observations as soon as possible. Some off the necessary steps to improve the calibrations of NICMOS data are: - Improve the current flat fields in the NICMOS pipeline, probably by taking temperature dependence into account, - Perform lamp-on/off tests for all regularly used passbands to determine the α parameters, at least at the shorter wavelengths. The images should be taken outof-focus to mitigate the effect of drift during the observations, - Include NIC3 in the imaging lamp-on/off non-linearity testing, - Similar tests should be performed on the ground for detectors in future space programs (e.g. WFC3 and JWST), - Develop software to correct fluxes of observations based on these α parameters. It will be impossible to implement on a pixel-to-pixel basis without modifying the properties of the read-noise somewhat (negative pixels cannot be corrected with a

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fractional power law) and without having to derive the calibration zeropoints from scratch. In a forthcoming ISR we will describe how one can calculate corrections for the countrate non-linearity for aperture photometry of point sources. Accurate corrections for extended sources are more complicated to calculate and will have to await modifications of the NICMOS data reduction pipeline.

Acknowledgements The NICMOS team, the non-linearity working group members, and Rodger Thompson are gratefully acknowledged for their help in this investigation into the NICMOS linearity and calibration.

References Bohlin, R., Lindler, D. & Riess, A. 2005, NICMOS ISR 2005-002 Bohlin, R., Riess, A. & de Jong, R.S. 2006, NICMOS ISR 2006-002

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Table 1. Measured α and Δm values in Cycle 7/7n and 14 with their 1 sigma errors. The NIC2, F110W observations have been shaded gray. Date

Camera

Filter

1998/02/18 1998/04/17 1998/06/04 1998/08/06 1998/09/24

2 2 2 2 2

F110W F110W F110W F110W F110W

2005/11/17 2005/11/17 2005/11/17 2005/11/17 2005/11/17 2005/11/17

1 1 1 2 2 2

F090M F110W F160W F110W F160W F187W

α Cycle 7/7n 1.022 +/- 0.001 1.025 +/- 0.004 1.023 +/- 0.001 1.024 +/- 0.001 1.022 +/- 0.001 Cycle 14 1.040 +/- 0.003 1.030 +/- 0.003 1.012 +/- 0.002 1.025 +/- 0.002 1.012 +/- 0.006 1.005 +/- 0.004

Δ m/dex

lamp

0.055 +/- 0.003 0.063 +/- 0.010 0.059 +/- 0.002 0.061 +/- 0.002 0.054 +/- 0.002

2152.9 2102.8 2129.5 2119.9 2177.1

0.101 +/- 0.008 79.8 0.074 +/- 0.009 316.8 0.031 +/- 0.006 148.3 0.063 +/- 0.006 1206.5 0.029 +/- 0.015 535.8 0.013 +/- 0.009 209.5

Table 2. Measured α and Δm values for Cycle 14 data, using for the lamp-off reference data either only the lamp-off data taken before the lamp-on data, only the lamp-off data taken after the lamp-on data, or both. Date 2005/11/17 both 2005/11/17 before 2005/11/17 after 2005/11/17 both 2005/11/17 before 2005/11/17 after 2005/11/17 both 2005/11/17 before 2005/11/17 after

Camera 1 1 1 1 1 1 2 2 2

Filter F110W F110W F110W F160W F160W F160W F110W F110W F110W

α 1.030 +/ 0.003 1.029 +/ 0.003 1.029 +/ 0.003 1.012 +/ 0.002 1.014 +/ 0.002 1.012 +/ 0.002 1.025 +/ 0.002 1.025 +/ 0.003 1.024 +/ 0.003

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Δ m/dex 0.074 +/ 0.009 0.074 +/ 0.009 0.072 +/ 0.009 0.031 +/ 0.006 0.035 +/ 0.005 0.030 +/ 0.005 0.063 +/ 0.006 0.063 +/ 0.006 0.059 +/ 0.006

lamp 316.8 317.3 317.9 148.3 147.4 148.7 1206.5 1205.3 1218.5

Figure 2: The absolute and relative difference in lamp-on minus lamp-off count rates as function of the lamp-off count rates on a pixel-by-pixel basis. The yellow +’s are for all data, the blue +’s for the data used in the fit (bad and low S/N points filtered). The green circles are the averages in bins of 50 pixels in ascending lamp-off count rate for all pixels. The red circles are binned averages in 30 equal logarithmic steps in lamp-off count rate for the selected pixels. The green lines are the fitted non-linearity functions, with the fitted α parameter labeled at the top. Left is the absolute difference (cron-croff-crlamp), right is the relative difference (cron-croff-crlamp)/ croff. While the brightest points have the largest flux change in absolute sense and are easy to measure above the noise (and are not due to background subtraction errors), the fainter points change relatively the most and have larger calibration errors relative to bright standard stars.

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