AI News, Machine learning in COMET experiment (part II)

Machine learning in COMET experiment (part II)

After ionization, electrons and ions are moving to opposite directions, so we probably can estimate the moment of ionization by drift time (which is of course much greater compared to time of particle flight in detector).

The basic algorithm that was proposed (baseline algorithm) is using the cut on energy deposition.

Nevertheless, it's not enough, since we have around 4400 wires, of which around 1000 gets activated (this number is called occupancy) within each measurement (event), while the signal track usually contains around 80 points.

So the event is represented using 4400 pairs (energy, time), of which most are zeros.

First, let's combine all the information we have about the single wire (distance from center, time and energy deposition), let's call them wire features:

After using GBDT trained onwire features and features ofits neighbours, weare getting quite clean picture with very few false positives.

Once wecomputed Hough transform, weleave only those centers, where hough transform ishigh, applying some nonlinear transformation there and applying inverse hough + some filtering.

It’s ROCAUCis 0.9993 (100 times less probability ofmisordering) When weare comparing ROC curves atthe threshold ofinterest (with very high signal sensitivity), things are bit worse, but still very impressing:

I’ve described how simple machine learning techniques coupled with well-known algorithms can produce avery good result, superior tomany complex approaches.

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