Two-dimensional total absorption spectroscopy with conditional generative adversarial networks
This addresses a specific bottleneck in nuclear spectroscopy analysis for researchers, offering an incremental improvement over existing unfolding techniques.
The paper tackles the problem of accurately unfolding correlated energy spectra in gamma-ray detectors by using conditional generative adversarial networks (cGANs) to treat it as an image-to-image translation, achieving characterization within resolution limits for over 93% of simulated test cases.
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 (E$_γ$) and total excitation energy (E$_x$). Analysis of TAS detector data is complicated by the fact that the E$_x$ and E$_γ$ quantities are correlated, and therefore, techniques that simply unfold using E$_x$ and E$_γ$ 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 $E_γ$ 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-$γ$ and double-$γ$ decay cascades. Our model demonstrates characterization capabilities within detector resolution limits for upwards of 93% of simulated test cases.