 |
 |
[log in to unmask]" type="cite">Ian, I really do think we are almost saying the same thing. Let me try to clarify.
You say that the Gaussian model is not the "correct" data model, and that the Poisson is correct. I more-or-less agree. If I were being pedantic (me?) I would say that the Poisson is *more* physically realistic than the Gaussian, and more realistic in a very important and relevant way --- but in truth the Poisson model does not account for other physical sources of error that arise from real crystals and real detectors, such as dark noise and read noise (that's why I would prefer a gamma distribution). I also agree that for x>10 the Gaussian is a good approximation to the Poisson. I basically agree with every point you make about the Poisson vs the Gaussian, except for the following.
The Iobs=Ispot-Iback equation cannot be derived from a Poisson assumption, except as an approximation when Ispot > Iback. It *can* be derived from the Gaussian assumption (and in fact I think that is probably the *only* justification it has). It is true that the difference between two Poissons can be negative. It is also true that for moderate # of counts, the Gaussian is a good approximation to the Poisson. But we are trying to estimate Itrue, and both of those points are irrelevant to estimating Itrue when Ispot < Iback. Contrary to your assertion, we are not concerned with differences of Poissonians, only sums. Here is why:
In the Poisson model you outline, Ispot is the sum of two Poisson variables, Iback and Iobs. That means Ispot is also Poisson and can never be negative. Again --- the observed data (Ispot) is a *sum*, so that is what we must deal with. The likelihood function for this model is:
L(a) = (a+b)^k exp(-a-b)
where 'k' is the # of counts in Ispot, 'a' is the mean of the Iobs Poisson (i.e., a = Itrue), and 'b' is the mean of the Iback Poisson. Of course k>=0, and both parameters a>0 and b>0. Our job is to estimate 'a', Itrue. Given the likelihood function above, there is no valid estimate of 'a' that will give a negative value. For example, the ML estimate of 'a' is always non-negative. Specifically, if we assume 'b' is known from background extrapolation, the ML estimate of 'a' is:
a = k-b if k>b
a = 0 if k<=b
You can verify this visually by plotting the likelihood function (vs 'a' as variable) for any combination of k and b you want. The SD is a bit more difficult, but it is approximately (a+b)/sqrt(k), where 'a' is now the ML estimate of 'a'.
Note that the ML estimate of 'a', when k>b (Ispot>Iback), is equivalent to Ispot-Iback.
Now, to restate: as an estimate of Itrue, Ispot-Iback cannot be derived from the Poisson model. In contrast, Ispot-Iback *can* be derived from a Gaussian model (as the ML and LS estimate of Itrue). In fact, I'll wager the Gaussian is the only reasonable model that gives Ispot-Iback as an estimate of Itrue. This is why I claim that using Ispot-Iback as an estimate of Itrue, even when Ispot<Iback, implicitly means you are using a (non-physical) Gaussian model. Feel free to prove me wrong --- can you derive Ispot-Iback, as an estimate of Itrue, from anything besides a Gaussian?
Cheers,
Douglas
On Sat, Jun 22, 2013 at 12:06 PM, Ian Tickle <[log in to unmask]> wrote:
On 21 June 2013 19:45, Douglas Theobald <[log in to unmask]> wrote:
The current way of doing things is summarized by Ed's equation: Ispot-Iback=Iobs. Here Ispot is the # of counts in the spot (the area encompassing the predicted reflection), and Iback is # of counts in the background (usu. some area around the spot). Our job is to estimate the true intensity Itrue. Ed and others argue that Iobs is a reasonable estimate of Itrue, but I say it isn't because Itrue can never be negative, whereas Iobs can.
Now where does the Ispot-Iback=Iobs equation come from? It implicitly assumes that both Iobs and Iback come from a Gaussian distribution, in which Iobs and Iback can have negative values. Here's the implicit data model:
Ispot = Iobs + Iback
There is an Itrue, to which we add some Gaussian noise and randomly generate an Iobs. To that is added some background noise, Iback, which is also randomly generated from a Gaussian with a "true" mean of Ibtrue. This gives us the Ispot, the measured intensity in our spot. Given this data model, Ispot will also have a Gaussian distribution, with mean equal to the sum of Itrue + Ibtrue. From the properties of Gaussians, then, the ML estimate of Itrue will be Ispot-Iback, or Iobs.
Douglas, sorry I still disagree with your model. Please note that I do actually support your position, that Ispot-Iback is not the best estimate of Itrue. I stress that I am not arguing against this conclusion, merely (!) with your data model, i.e. you are arriving at the correct conclusion despite using the wrong model! So I think it's worth clearing that up.
First off, I can assure you that there is no assumption, either implicit or explicit, that Ispot and Iback come from a Gaussian distribution. They are both essentially measured photon counts (perhaps indirectly), so it is logically impossible that they could ever be negative, even with any experimental error you can imagine. The concept of a photon counter counting a negative number of photons is simply a logical impossibility (it would be like counting the coins in your pocket and coming up with a negative number, even allowing for mistakes in counting!). This immediately rules out the idea that they are Gaussian. Photon counting where the photons appear completely randomly in time (essentially as a consequence of the Heisenberg Uncertainly Principle) obeys a Poisson distribution. In fact we routinely estimate the standard uncertainties of Ispot & Iback on the basis that they are Poissonian, i.e. using var(count) = count. That is hardly a Gaussian assumption for the uncertainty!
Here is the correct data model: there is a true Ispot which is (or is proportional to) the diffracted energy from the _sum_ of the Bragg diffraction spot and the background under the spot (this is not the same as Iback). This energy ends up as individual photons being counted at the detector (I know there's a complication that some detectors are not actually photon counters, but the result is the same: you end up with a photon count, or something proportional to it). However photons are indistinguishable (they do not carry labels telling us where they came from), so quantum mechanics doesn't even allow us to talk about photons coming from different places: all we see are indistinguishable photons arriving at the detector and literally being counted. Therefore the estimated Ispot being the total number of photons counted from Bragg + background has a Poisson distribution. There will be some experimental error associated with the random-in-time appearance of photons and also instrumental errors (e.g we might simply fail to count some of the photons, or we might count extra photons coming from somewhere else), but whatever the source of the error there is no way that the measured count of photons can ever be negative.
Now obviously we want to estimate the background under the spot but we can't do that by looking at the spot itself (because the photons are indistinguishable). So completely independently of the Ispot measurement we look at a nearby representative (hopefully!) area where there are no Bragg spots and count that also: there is a true Iback associated with this and our estimate of it from counting photons. Again, being a photon count it is also Poissonian and will have some experimental error associated with it, but regardless of what the error is Iback, like Ispot, can never be negative.
Now we have two Poissonian variables Ispot & Iback and traditionally we perform the calculation Iobs = Ispot - Iback (whatever meaning you want to attach to Iobs). Provided Ispot and Iback are 'sufficiently' large numbers a Poisson distribution can be approximated by a Gaussian with the same mean and standard deviation, but with the proviso that the variate of this approximate Gaussian can never be negative. In fact you only need about 10 counts or more in _both_ Ispot and Iback for the approximation to be pretty good. (As an aside, 10 counts used to be a small number, nowadays detectors are becoming much more sensitive and the backgrounds are now so low that maybe the assumption that typical counts are > 10 is no longer tenable.). This of course means that the difference of 2 approximate Gaussians is also an approximate Gaussian, with mean equal to the difference of the means and variance equal to the sum of the variances. Importantly, as a consequence of the experimental errors (including the fact that Iback is probably not an accurate estimate of the background in Ispot), this Gaussian _can_ have negative values of the variate. F-W indeed makes the explicit assumption that Ispot - Iback is Gaussian and therefore can be negative.
Your observation that the sum of 2 (or indeed any number of) Poissons is also Poissonian is of course completely correct (we can arbitrarily separate the photons into any number of groups each of which is Poissonian, and then adding the groups together at the end must give exactly the same result as having kept the photons in a single group). However this point is irrelevant to the present discussion: we are not concerned with sums of Poissonians, only differences.
Your previous statement that "the case when Iback>Ispot, where the Gaussian approximation to the Poisson no longer holds" is not correct. The Gaussian approximation to the Poisson holds regardless of whether or not Iback > Ispot: the only assumption is that _both_ Ispot and Iback are "sufficiently large".
My point about integrated intensities being required for estimating the Wilson distribution parameter in order to correct the intensities using F-W was that it's easy to iterate inside a single program. It's much harder to iterate when it has to be done over several programs (in this case the integration program, the sorting/scaling/outlier rejection/merging program and the I->F conversion program), since not all the information required may be available at the same time (this is essentially Phil's point). Also dealing with non-Gaussian values that would be generated by your algorithm in the outlier rejection/merging program will be tricky, and probably would require a radical overhaul of that program (a point I made previously).
Sorry this got so long, but I felt it was important that you start out with the correct data model!
Cheers
-- Ian