La Silla - Science Operation Department

Flatfields for the mid infrared: Experiments with TIMMI2
Version: 1.0 2002-08-20 Oliver Schütz

1. Why are conventional flatfields not possible in the mid-IR ?

Different from the optical wavelength range and near-IR (1-5 µm), no flatfields are made for observations at thermal wavelegths (10-20 µm). Techniques used to obtain flatfields at shorter wavelengths will fail in the mid-IR . A mid-IR "dome flat" would saturate the detector because the screen is at room temperature. Simple sky frames in the mid-IR are dominated from fast fluctuations in the sky background and flatfields obtained from these will not be constant in time neither in spatial dimensions. Additional to this, data will not be comparable if taken with different electronical read-out modes. Altogether, any possible TIMMI2 flatfield must comply with the following characteristics: Therefore for almost each science observation an individual flatfield would be required. In first tests however, even within the shortest possible time between science object and calibration data, no good flats could be obtained.

2. The current observing situation with TIMMI2

All data at thermal wavelengths are taken with chopping and nodding corrections to eliminate the thermal background from the sky and telescope (see the TIMMI2 web page for more detailed information on this technique). As a result of this procedure, images reduced by the TIMMI2 pipeline or an equivalent observer's routine are already relatively flat. However, certain inhomogenities persist. A point source often appears fainter in the lower half of the detector and the variations in the count rate over the detector array may amount up to 20%. These patterns show to some level a constant trend but are also variable in time, and therefore cannot be explained only by the individual pixels' response of the detector. Sky background variations and atmospherical influence have a major impact on these features. This is the reason why the question of flatfielding mid-IR data up to now only has been solved for space based observations (Starck J.L. et al. 1999, Astron. Astrophys. Suppl. Ser. 134, 135-148).

3. Basic ideas for creating a detector gain map

For optical detectors, variations along the detector array are mainly caused by dust on the detector or a remaining misalignment within the light beam, due to which the beam may hit the detector not exactly perpendicular. In the infrared, the different response of the detector array results from wide-range variations in the pixel sensitivity. Different from optical detectors each pixel is individually read out and electronics are more difficil than in the optical. To determine the sensitivity of each single pixel, its response is measured in a sequence of images with different levels of illumination. For mid-IR observations this varying illumination can be achieved by the following ways: In the brightness sequence, the median count rate of each single exposure is compared to the registered count rate of the individual pixel. A linear fit to all pairs of the values (median of an image, count for a fixed pixel) creates a response function for the single pixel. Ideally the slope of this function should be close to 1. Pixel with a lower value react less on exposure to light than those with a higher value. Bad pixel are close to 0, hot pixel are much brighter than 1. Applying this to all pixel creates a gain map for the whole detector array. With this, variations in the sensitivity of the detector array can be identified. Dividing a raw image by this gain map results in the corrected image, principally. This works well for the near-IR, but for the mid-IR more sophisticated methods are necessary. The problem is that for TIMMI these gain maps appear to be highly variable with time.

4. Applications to test data

4.1. Observations at different airmasses

Measuring the sky or a target at two or more airmasses leads to a sequence of exposures with different brightness due to the increasing sky contribution with distance from zenit. A flatfield can be created as shown above. Its quality however will strongly depend on temporal and spatial atmospherical fluctuations during the observations. In our tests this flatfield could not successfully be applied to a science exposure made within these observations.

4.2. Clouds

Thin, varying clouds result in a perfect illumination sequence. However, another detector read-out mode has to be used during these observations. A flat obtained from these data is not compatible to the detector mode for science observations.

4.3. Create the flatfield from the science exposure

Due to background fluctuations during a science exposure, also from these data an illumination sequence can be achieved. Long-time exposures or those with larger background fluctuation have better statistics in the fit of the pixel response. Only these exposures will result in a reliable flatfield.

The procedure is complex due to the spatial offsets for chopping and nodding. In the following we describe an example for a stellar point source observed with four nodding positions, i.e. two full nod cycles. For each nod position, the first chopping position is called the ON position (sometimes also called position 0). With OFF (or position 1) we specify the second chop. The number of videoframes per nod is 1 in this case. Each chop and nod position results in an individual flatfield, in our example these are eight totally different flats. We found no correlation of flats from corresponding positions, modifications are statistical and time-dependant.

Dividing the frame for each chop and nod position by the corresponding flatfield appears to be the nearest approach, but will result in corrupted data. The information of the star at its position on the detector is still included in these flatfields, even though not directly visible. When dividing the frame of an individual chop and nod position by the corresponding flat, the resulting image is destroyed at the position where it actually should be corrected, i.e. the stellar surrounding. The following procedure avoids this and is the best approximation up to now for a TIMMI2 flatfield:

This approach can produce a kind of flatfield correction for data taken with high atmospherical variations or fair weather conditions. The calculations can be easily included in a personal pipeline routine. On the other hand this method completely fails to flatfield good data. The worse statistic when calculating the pixel response is the reason for this.

Here are some illustrations of applying the technique from Chapter 4.3 to science data.

5. Conclusions

For all ground-based mid-IR instruments no possibility of a reliable flatfield correction exists up to now. We have shown a method how TIMMI2 data in prinicple could be flatfielded. The resulting final image is not always of better quality, depending on meteorological conditions. No recommendation to observers can be given at the moment to apply the techniques presented here.
Last revision:  2002-08-20 by Oliver Schütz
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