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.

翻译：我们探索利用机器学习技术从实验谱中移除大体积$\gamma$射线探测器的响应。分段式$\gamma$射线总吸收谱仪（TAS）能够同时测量单个$\gamma$射线能量（E$_\gamma$）和总激发能量（E$_x$）。由于E$_x$与E$_\gamma$两个量之间存在相关性，独立使用E$_x$和E$_\gamma$响应函数进行反解的技术精度不足，这使得TAS探测器数据的分析变得复杂。本研究探讨使用条件生成对抗网络（cGANs）同时反解TAS探测器中的E$_{x}$和E$_{\gamma}$数据。具体而言，我们采用基于深度学习最新进展的生成建模技术\texttt{Pix2Pix} cGAN，将\rawmatrix~矩阵反解视为图像到图像的翻译问题。我们展示了单$\gamma$和双$\gamma$衰变级联的模拟与实验矩阵结果。在模拟测试案例中，本模型在探测器分辨率极限内对93%以上的案例展现出表征能力。