In our last post, we showed some really promising average classifications – after removing obvious outliers. In this post, we want to illustrate the reasons that we need such a high number of classifications before we retire an image.
The above image is an example of a frame where there is one antinode region in the upper left corner that has not been identified enough times to be included in the analysis. While it is possible that this antinode may not ultimately be important to our overall analysis, we would like to see as many of these marked as possible. Our hope is that by getting more volunteers to classify this image, enough people would see that antinode to mark it with an accurate ellipse that we can include in an average.
The above image has two missing antinode regions – one is the strike note on the lower left side of the image where all the fringes have merged together and the other antinode is centered at position (200,150). What has become obvious is that the most often missed antinode is the strike note. Often the vibration amplitude of the strike note is so high that there are no distinctly visible fringes. In those cases, the fringe count should be marked as 11, which is our way of saying “more than 10 fringes present”. It is quite apparent to us that the strike note CAN be found by many of our volunteers, so we believe that having more classifications will allow for all the antinodes on all the frames to be marked.
Additionally, the average ellipse that we do see is possibly not the best representation for that particular antinode. We are hopeful that with more classifications, the average ellipse would be a better representation.
In the above image, we are really happy to see the two antinodes identified by the classifications, but again, the strike note (on the left side) has not been marked enough times to be included in this analysis.
Here is an example of an image that has been seen at least 5 times, but there was not enough agreement on the position of the antinodes to include either of them in the analysis.
All of these frames are still in the project and waiting for more classifications to be made. Thank you for all your help and please consider sharing our project with others!