Simple neutral particle detectors consisting of organic scintillators, sensitive photodetectors, and readout systems have been used to great effect in space- and Earth-based science for observing gamma rays and particle radiation from solar weather, astrophysical phenomena, and even lightning storms on Earth. The calibration of simple gamma ray detectors was improved through kernel density estimation (KDE) in a user-friendly Jupyter notebook running a Python script. When calibrating organic particle detectors, a gamma ray source is applied and the distribution of raw voltages produced by the detector are represented in a histogram including a distinct Compton edge. The location of the edge in the histogram can be associated with the energy of the gamma ray source to convert the histogram of voltage measurements into a representation of the gamma ray energies interacting with the detector. The process of estimating the location of the edge on a histogram of real measurements with imperfect resolution is often done by hand or with a half gaussian fit, but we propose an alternative technique where KDE of the raw voltages replaces the histogram. Using KDE, Gaussian kernels are built up to a representation of the underlying energy distribution which can be easily numerically differentiated. The minimum of the modified distribution identifies the location of the Compton edge, regardless of the resolution of the detector. To demonstrate the technique on a model of a Compton edge, we calculated the theoretical Klein-Nashina Compton distribution of Cesium-137 gamma ray interactions, created a model of the edge with a piecewise 2nd order polynomial, and convolved the piecewise function with a Gaussian to approximate the experimental response. The response was numerically differentiated and the location of the minimum was consistent with the energy of the Compton edge, as the inflection point of the approximated Gaussian distribution occurs at the Compton edge. We also demonstrate the technique with data from a MEGAlib simulation of a simple block particle detector. For projects with repeated adjustment and testing of particle detectors, this method should improve consistency and accuracy of calibration while reducing analysis time.
