Two-dimensional total absorption spectroscopy with conditional generative adversarial networks

• URL: http://arxiv.org/abs/2206.11792v3
• Date: Thu, 14 Dec 2023 16:14:17 GMT
• Title: Two-dimensional total absorption spectroscopy with conditional generative adversarial networks
• Authors: Cade Dembski, Michelle P. Kuchera, Sean Liddick, Raghu Ramanujan, Artemis Spyrou
• Abstract summary: We use conditional generative adversarial networks to unfold $E_x$ and $E_gamma$ data in TAS detectors. Our model demonstrates characterization capabilities within detector resolution limits for upwards of 93% of simulated test cases.
• Score: 0.22499166814992444
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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