Machine Learning for Scientific Applications

Data taken in scientific context often forms an image. Whether this is a reconstruction of a physical space or if it is simply a graphical representation of data, machine learning and image processing tachniques can have some use for a scientist.

In one context, energy sharing between two detectors forms lines of a constant sum.

In this case it was not possible to calibrate the y-axis directly. Only the x-axis and the sum could be accurately calibrated. Instead of recursively scanning and re-calibrating the data, one can use a clustering algorithm called k-lines means. The slope of these lines is the negative reciprocal of the slope of the calibration. 

In this case, rapid convergence even for k=3, fewer than the k=9 which is closer to the number of calibration points which is physically meaningful.

This fact allowed for a fast running algorithm which can run online for stability monitoring.

Author: Calcumore

Physicist, Programmer, Calcumore and calcunow.

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