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WFC3 Data Handbook > Chapter 8: Persistence > 8.3 Mitigating the Effects of Persistence

8.3
Basically, there are two ways to mitigate persistence. One can exclude the regions affected by your data from your analysis, or one can try to subtract the persistence signal from the data and use the modified data in data analysis.
About 1% of the pixels in the IR array have data quality problems (e.g. IR blobs), and if one needs to exclude another 1% of the pixels due to persistence this may well be the best course. Simple procedures (available, for example, in IRAF) can be used to mark compromised pixels and modify the data quality extensions of the flt files. Typically one would define based upon an inspection of the persistence images, to a level, say 0.01 e-/sec to characterize as bad. The choice of level would be based the science objective and on the fraction of pixels impacted. Once the pixels are flagged, down-stream analysis proceeds as it normally would, assuming the tools that are used take data quality into account. Persistence is a property of the pixel. As long as the observer who planned the original observations created a set of dithered images, using this procedure to flag bad pixels and then using AstroDrizzle to recombine images result a combined image that is largely cleansed of the effects of persistence.
An alternative is to use the persistence-subtracted files in down-stream analysis. If one adopts this approach, one should carefully inspect the _flt_cor.fits to evaluate how well persistence has been subtracted from the image. It is important to inspect all of the images in a particular science observation as the external persistence will decline with time. Typically in situations in which there is persistence, about 90% of the persistence signature is removed by the model, but there are variations in persistence which are still being studied, and so persistence can be either under- or over-subtracted.
The persistence-subtracted image of our example is shown in Figure 8.5. The image is much cleaner, especially at the center of the image where the γ-ray burst had occurred. However, a careful inspection shows some residual signatures of the persistence, particularly due to the set of early images that produced the horizontal set of blobs in the original image. Sometimes one can do better by scaling the persistence model and producing one's own persistence-subtracted image. It is also, of course, possible to combine these approaches as well, flagging the worst pixels in the persistence-corrected images, and then using AstroDrizzle to produce a combined image.
Figure 8.5: Image with Persistence subtracted.
The image shown in Figure 8.1 but with persistence subtracted, using set of parameters in Table 7.1. Much, but not all of the persistence has been removed
At some point, we expect to release the tools we have for general use. In the meantime, if persistence remains a significant problem in the analysis of your images, after considering the approaches suggested above, please contact the help desk (help@stsci.edu). It is possible that by changing some of the parameters in the model, we can provide better estimates of the persistence in an flt file, particularly in situations where several earlier visits have affected a science image. Work on characterizing persistence is on-going. As mentioned earlier, some of the variations in persistence are known to be due to the fact that the traps that cause persistence have finite trapping times, and thus longer exposures cause more persistence than shorter exposures for objects that reach the same fluence level. This will likely allow us to improve the model of persistence incorporated into the tools over time (see e. g. Long 2013, WFC3 ISR 2013-08).
In all cases in dealing with persistence, as is true of most problems associated with data analysis, first assess the severity of the problem and then choose a method of handling the issue that is consistent with the science one is trying to carry out.
Users concerned about the effects of persistence on their data should check for updates on the WFC3 webpages, in the periodically released STANs, as well as the Instrument Science Reports.

WFC3 Data Handbook > Chapter 8: Persistence > 8.3 Mitigating the Effects of Persistence

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