Page History

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The data from experiment xcs06016 and run 37 were used in this analysis. Only sparse events were used so multiple photon peaks or pixels shared by multiple photons are extremely rare; events with less than 3000 peaks found by each peak finder were used. A total of 13 events were used. The purpose of these analyses is to determine which (if any) of the following methods discussed below have an appreciable effect on the RMS of the distribution of the energy. Through the entirety of this study, the parameters were held constant unless otherwise stated. For the square peak finder, an initial threshold of 15 ADU was used. For the V1 and V4 algorithms, a rank of 1, a low threshold of 10 and a high threshold of 30 were used. The other parameters were set so as to not any noise subtraction in the calculation of the peak energies. The maximum peak size was set at 4 pixels since a single photon peak of greater than 4 pixels is unreasonable.

Comparing Peak Finders

Effect of Neighbor Thresholding

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Since the square peak finder only find 4 pixel peaks, only the data for the V1  and V4 peak finder is shown below.  This data consists of 29,342 peaks found from the 13 different events.

Lining Up and Recombining Pixel Distributions

As can be seen in the table above, there is a noticeable difference in the means of the energy distributions for each number of pixels which, ideally, shouldn't exist. Furthermore, the errors on the mean cannot account for this difference. One theory is that the larger peaks (the ones with more pixels) end up adding in more noise to the total energy of the photon thus shifting it to a slightly higher energy. If such is the case, one remedy would be to shift each distribution so that their peaks fall on the same bin. This was done by using the average of the four bins where the peaks existed as the new bin for the peaks. Below is the result compared to the normal distribution shown in the first graph on this page. As can be seen, the difference is very slight; the distribution is slightly sharper for the shifted data, but not by very much.

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