Two-dimensional total absorption spectroscopy with conditional generative adversarial networks

TL;DR

This paper introduces a novel application of conditional generative adversarial networks (cGANs) to simultaneously unfold excitation and gamma-ray energy data in total absorption spectroscopy, improving accuracy over traditional methods.

Contribution

It pioneers the use of cGANs, specifically Pix2Pix, for unfolding correlated spectral data in gamma-ray detectors, treating the problem as image-to-image translation.

Findings

Achieves over 93% accuracy in simulated test cases

Demonstrates effectiveness on both simulated and experimental data

Improves spectral unfolding accuracy compared to traditional methods

Abstract

We explore the use of machine learning techniques to remove the response of large volume $\gamma$-ray detectors from experimental spectra. Segmented $\gamma$-ray total absorption spectrometers (TAS) allow for the simultaneous measurement of individual $\gamma$-ray energy (E$_\gamma$) and total excitation energy (E$_x$). Analysis of TAS detector data is complicated by the fact that the E$_x$ and E$_\gamma$ quantities are correlated, and therefore, techniques that simply unfold using E$_x$ and E$_\gamma$ response functions independently are not as accurate. In this work, we investigate the use of conditional generative adversarial networks (cGANs) to simultaneously unfold $E_{x}$ and $E_{\gamma}$ data in TAS detectors. Specifically, we employ a \texttt{Pix2Pix} 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-$\gamma$ and double-$\gamma$ decay cascades. Our model demonstrates characterization capabilities within detector resolution limits for upwards of 93% of simulated test cases.