{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/two-dimensional-total-absorption-spectroscopy","title":"Two-dimensional total absorption spectroscopy with conditional generative adversarial networks","arxiv_id":"2206.11792","date":"2022-06-23","proceeding":null,"authors":["Cade Dembski","Michelle P. Kuchera","Sean Liddick","Raghu Ramanujan","Artemis Spyrou"],"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.","url_abs":"https://arxiv.org/abs/2206.11792v3","url_pdf":"https://arxiv.org/pdf/2206.11792v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"two-dimensional-total-absorption-spectroscopy","repo_url":"https://github.com/alpha-davidson/sun_cgan","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"image-to-image-translation","task_name":"Image-to-Image Translation"},{"task_slug":"two","task_name":"Vocal Bursts Valence Prediction"}],"methods":[{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"patchgan","method_name":"PatchGAN"},{"method_slug":"pix2pix","method_name":"Pix2Pix"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}