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Persistence!
07.15.2009 6:04 PM
Parker Fagrelius

By Parker Fagrelius
Astronomy and Physics Missions Concepts

In my first blog entry I wrote a bit about some of the characteristics of the measurements we will be making for the Dark Energy Observatory mission concept I am working on. (See “Detecting Darkness”) Recently, I have been paying special attention to a specific characteristic of the Infrared detectors we plan to use called Persistence. The detector, in this case an Infrared detector with three filters extending from 960nm – 2500nm, collects light from distant galaxies and closer stars for an established period of time (integration time). At the end of the integration time the photons, which are subsequently converted to electrons, are read-out by the detector electronics, and then the telescope moves to look at another section of the sky. What we would like is for all of the charge in each of the pixels of the detector (there are 72 million pixels total!) are drained out and we start our next integration time with a fresh slate. However, due to the composition of the Infrared detectors (made of Mercury-Cadmium-Telluride) some of the charge remains and appears as a latent or persistent image. This is potentially an issue, as these latent images don’t reflect what is actually in the sky but rather a ghost image from the previous integration time. In our case, this would be an issue if we were to lose a number of galaxies we wanted to measure because they overlapped with the latent image of a bright star or other galaxy.

The approach I took was multi-step: 1) Determine how many bright stars would be in the field of view on average. 2) Determine how many photons would be collected during the integration time (i.e. the flux). 3) Calculate the brightness of the latent image generated by the bright stars (# of charge remaining in pixel due to persistence affect is ~0.2% of the initial image). 4) Determine whether the latent image was bright enough to differentiate from a measured galaxy or could be ignored as being noise. 5) And finally, calculating the probability of a galaxy landing on top of one of these latent images. What I found, after endless hours with Excel and Matlab, was that it likely isn’t a problem because only ~2% of the total pixels will have a latent image with similar brightness, as the galaxies and the rest of the latent images are small enough to “blend in” with the noise. Also, the probability of a galaxy coinciding with one of these latent images is very low.


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