arXiv Machine Learning August 4, 2026 By J. Balibrea-Correa, E. N{\'a}cher, C. Fonseca-Vargas, J. L. Tain

Rethinking Total Absorption Gamma Spectroscopy Deconvolution: Supervised Machine Learning vs Response-Matrix Methods

Read the original on arXiv Machine Learning →

arXiv:2608. 00090v1 Announce Type: cross Abstract: The extraction of $\beta$-feeding distributions in Total Absorption $\gamma$-ray Spectroscopy constitutes a challenging inverse problem, particularly in nuclei with complex decay schemes involving a large number of excited states.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 6

Learning to Resolve Neutron Resonances with Fully Convolutional Neural Networks

arXiv:2608. 04027v1 Announce Type: new Abstract: This work investigates the feasibility of augmenting traditional R-Matrix codes with a robust machine learning framework for automatically detecting neutron resonances in transmission spectra.

By Nataly R. Panczyk, Athanasios Stamatopoulos, Josef Svoboda, Majdi I. Radaideh

benchmarks safety

arXiv Machine Learning

Jun 16

Peak-Based Nuclide Identification in HPGe $\gamma$-Spectrometry with Machine Learning and SHAP

arXiv:2606. 14874v1 Announce Type: cross Abstract: High-purity germanium gamma spectra often require time-consuming analyses from subject matter experts.

By Samuel Emmons, Kelly Truax, Maurice Lonsway, Bruce Pierson, Brian Archambault

arXiv AI

5d ago

Simulation-to-real transfer learning for infrared spectroscopic chemical sensing and analysis from molecules to complex samples

arXiv:2608. 13341v1 Announce Type: cross Abstract: Infrared (IR) spectroscopy is widely used for chemical sensing, but extracting reliable chemical information from spectra remains challenging.

By Yusen Tan, Yixuan Chen, Zheng Fang, Pan Liu, Yifan Li, Qinyu Guo, Zhedong Lin, Yuqiang Li, Xiangxiang Zeng, Tong Wang, Jun Xia

fine-tuning safety

Hugging Face Trending Papers

6d ago

Simulation-to-real transfer learning for infrared spectroscopic chemical sensing and analysis from molecules to complex samples

Infrared (IR) spectroscopy is widely used for chemical sensing, but extracting reliable chemical information from spectra remains challenging. Conventional interpretation is labor-intensive, relies on prior knowledge and reference spectra, and is difficult to scale, whereas most machine-learning methods are tailored to individual tasks or datasets, require large labeled training sets, and transfer poorly across analytical objectives and experimental datasets.

fine-tuning safety

arXiv Machine Learning

Aug 4

RamanPFN: learning from Raman spectral structure with a tabular foundation model

arXiv:2608. 02157v1 Announce Type: new Abstract: Raman spectroscopy enables non-destructive, label-free molecular characterization across materials science, biomedicine and process monitoring.

By Xingyu Pan, Huan Wang, Jinjia Guo, Zhenlin Zhao, Siming Dong, Jixi Lu

Hugging Face Trending Papers

Aug 3

RamanPFN: learning from Raman spectral structure with a tabular foundation model

Raman spectroscopy enables non-destructive, label-free molecular characterization across materials science, biomedicine and process monitoring. Predictive Raman datasets often contain few labelled spectra and thousands of ordered wavenumbers, with informative variation within bands and across distant spectral regions.

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Learning to Resolve Neutron Resonances with Fully Convolutional Neural Networks

Peak-Based Nuclide Identification in HPGe $\gamma$-Spectrometry with Machine Learning and SHAP

Simulation-to-real transfer learning for infrared spectroscopic chemical sensing and analysis from molecules to complex samples

Simulation-to-real transfer learning for infrared spectroscopic chemical sensing and analysis from molecules to complex samples

RamanPFN: learning from Raman spectral structure with a tabular foundation model

RamanPFN: learning from Raman spectral structure with a tabular foundation model