In a small amount of research time today, I read various bits of writing from students. Leslie Rogers's (Caltech) student Ellen Price (Caltech) has written a very nice paper about Fisher Information in exoplanet transit observations. I am going to recommend to them that they make the paper more useful to experimental design by specifically accounting for the differences between photon noise and read noise, the latter penalizing you for taking shorter exposures. I say this because their main conclusion is (naively) "take shorter exposures".
My own student MJ Vakili has written some nice short papers on noise bias and PSF bias in weak-lensing surveys. We are trying to understand these biases to make stronger our arguments that probabilistic reasoning (read: Bayes) will solve all problems. What is hard to understand specifically is under what conditions the current plans (coming from the point-estimation proponents) to measure and then correct for biases will work and under what conditions they will fail. If we can understand that, we can make a very strong argument for probabilistic reasoning. Of course, all methods are destined to fail if they make bad assumptions, so the key thing for any weak lensing program is to test the assumptions as thoroughly as possible.
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