# Two-dimensional total absorption spectroscopy with conditional generative adversarial networks

arXiv: https://arxiv.org/abs/2206.11792 · HTML: https://mlsanity.com/paper/2206.11792/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 51 references that are in the map.
- First posted on arXiv 23 Jun 2022.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Quantum State Tomography with Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2008.03240/) (2020)
- [Efficient Machine Learning Approach for Optimizing the Timing Resolution of a High Purity Germanium Detector](https://mlsanity.com/paper/2004.00008/) (2020)

## Built on by (most cited first, 1 of 1)

- [Rethinking Total Absorption Gamma Spectroscopy Deconvolution: Supervised Machine Learning vs Response-Matrix Methods](https://mlsanity.com/paper/2608.00090/) (2026)

## Abstract (arXiv)

We explore the use of machine learning techniques to remove the response of large volume γ-ray detectors from experimental spectra. Segmented γ-ray total absorption spectrometers (TAS) allow for the simultaneous measurement of individual γ-ray energy (Eγ) and total excitation energy (Ex). Analysis of TAS detector data is complicated by the fact that the Ex and Eγ quantities are correlated, and therefore, techniques that simply unfold using Ex and Eγ response functions independently are not as accurate. In this work, we investigate the use of conditional generative adversarial networks (cGANs) to simultaneously unfold Ex and Eγ data in TAS detectors. Specifically, we employ a textttPix2Pix cGAN, a generative modeling technique based on recent advances in deep learning, to treat rawmatrix~ matrix unfolding as an image-to-image translation problem. We present results for simulated and experimental matrices of single-γ and double-γ decay cascades. Our model demonstrates characterization capabilities within detector resolution limits for upwards of 93% of simulated test cases.