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We explore the use of machine learning techniques to remove the response oflarge volume $\gamma$-ray detectors from experimental spectra. Segmented$\gamma$-ray total absorption spectrometers (TAS) allow for the simultaneousmeasurement of individual $\gamma$-ray energy (E$_\gamma$) and total excitationenergy (E$_x$). Analysis of TAS detector data is complicated by the fact thatthe E$_x$ and E$_\gamma$ quantities are correlated, and therefore, techniquesthat simply unfold using E$_x$ and E$_\gamma$ response functions independentlyare not as accurate. In this work, we investigate the use of conditionalgenerative 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 deeplearning, to treat \rawmatrix~ matrix unfolding as an image-to-imagetranslation problem. We present results for simulated and experimental matricesof single-$\gamma$ and double-$\gamma$ decay cascades. Our model demonstratescharacterization capabilities within detector resolution limits for upwards of93% of simulated test cases.

Two-dimensional total absorption spectroscopy with conditional generative adversarial networks