arXiv Machine Learning By M. Rejmund, A. Lemasson

Analysis of Atomic Charge State and Atomic Number for VAMOS++ Magnetic Spectrometer using Deep Neural Networks and Fractionally Labelled Events

Read the original on arXiv Machine Learning →

arXiv:2507. 07109v2 Announce Type: cross Abstract: The VAMOS++ magnetic spectrometer is a multi-parametric system that integrates ion optical magnetic elements with a multi-detector stack.

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

arXiv Machine Learning
Jul 1

Seven-dimensional Trajectory Reconstruction for VAMOS++

arXiv:2503. 18959v1 Announce Type: cross Abstract: The VAMOS++ magnetic spectrometer is characterized by a large angular and momentum acceptance and highly non-linear ion optics properties requiring the use of software ion trajectory reconstruction methods to measure the ion magnetic rigidity and the trajectory length between the beam interaction point and the focal plane of the spectrometer.

By M. Rejmund, A. Lemasson
arXiv Machine Learning
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arXiv:2606. 29466v1 Announce Type: new Abstract: Calibration remains one of the principal obstacles to the deployment of machine learning in scientific instrumentation because it typically relies on expert intervention, dedicated procedures, and manually labelled data.

By M. Rejmund (GANIL, CEA/DRF - CNRS/IN2P3, Bd Henri Becquerel, BP 55027, F-14076, Caen Cedex 5, France), A. Lemasson (GANIL, CEA/DRF - CNRS/IN2P3, Bd Henri Becquerel, BP 55027, F-14076, Caen Cedex 5, France)
arXiv Machine Learning
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Machine Learning for Charge State Characterization of Isolated Double Quantum Dots

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By Hyma Vallabhapurapu, Marco Candido, Krishna Choudhary, Paul Steinacker, Ensar Vahapoglu, Chris Escott, Wee Han Lim, Andre Saraiva, Nard Dumoulin Stuyck, MengKe Feng
arXiv Machine Learning
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ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification Across the Periodic Table

arXiv:2606. 30961v1 Announce Type: cross Abstract: Advances in deep learning architectures and representations have enabled ML-driven chemical property prediction, but state-of-the-art (SOTA) models have remained largely confined to independent codebases and lack support for diverse chemical species.

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