# An Application of CNNs to Time Sequenced One Dimensional Data in Radiation Detection

arXiv: https://arxiv.org/abs/1908.10887 · HTML: https://mlsanity.com/paper/1908.10887/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2019.

## Built on by (most cited first, 2 of 2)

- [Sim-to-real supervised domain adaptation for radioisotope identification](https://mlsanity.com/paper/2412.07069/) (2024)
- [Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy](https://mlsanity.com/paper/2603.05719/) (2026)

## Abstract (arXiv)

A Convolutional Neural Network architecture was used to classify various isotopes of time-sequenced gamma-ray spectra, a typical output of a radiation detection system of a type commonly fielded for security or environmental measurement purposes. A two-dimensional surface (waterfall plot) in time-energy space is interpreted as a monochromatic image and standard image-based CNN techniques are applied. This allows for the time-sequenced aspects of features in the data to be discovered by the network, as opposed to standard algorithms which arbitrarily time bin the data to satisfy the intuition of a human spectroscopist. The CNN architecture and results are presented along with a comparison to conventional techniques. The results of this novel application of image processing techniques to radiation data will be presented along with a comparison to more conventional adaptive methods.