Related papers: Particle Identification at VAMOS++ with Machine Le…

The dynamical mechanism of multinucleon transfer (MNT) reactions has been investigated within the dinuclear system (DNS) model, in which the sequential nucleon transfer is described by solving a set of microscopically derived master…

Recent experiments [Phys. Rev. Lett. 123, 092503(2019); Phys. Rev. Lett. 118, 222501 (2017)] have made remarkable progress in measurements of the isotopic fission-fragment yields of the compound nucleus $^{239}$U, which is of great…

Multi-instance multi-label (MIML) learning is a challenging problem in many aspects. Such learning approaches might be useful for many medical diagnosis applications including breast cancer detection and classification. In this study subset…

We compare the observed charged particle multiplicity distributions in the MicroBooNE liquid argon time projection chamber from neutrino interactions in a restricted final state phase space to predictions of this distribution from several…

Effective recognition of acute and difficult-to-heal wounds is a necessary step in wound diagnosis. An efficient classification model can help wound specialists classify wound types with less financial and time costs and also help in…

We present an effective application of quantum machine learning in the field of healthcare. The study here emphasizes on a classification problem of a histopathological cancer detection using quantum transfer learning. Rather than using…

We investigate the application of deep learning to the retrieval of the internuclear distance in the two-dimensional H$_2^{+}$ molecule from the momentum distribution of photoelectrons produced by strong-field ionization. We study the…

To address the issues of limited samples, time-consuming feature design, and low accuracy in detection and classification of breast cancer pathological images, a breast cancer image classification model algorithm combining deep learning and…

The challenge of isotopic identification over a wide angular distribution has limited the measurement of neutron-rich nuclei produced via the multinucleon transfer (MNT) process. To investigate the optimal detection angles for the N=126…

State-of-the-art neural language models represented by Transformers are becoming increasingly complex and expensive for practical applications. Low-bit deep neural network quantization techniques provides a powerful solution to dramatically…

The characteristics and performance of a Dual Position Sensitive Multi-Wire Proportional Counter (DPS-MWPC) used to measure the scattering angle, interaction position on the target and the velocity of reaction products, detected in the…

We refine the OrbNet model to accurately predict energy, forces, and other response properties for molecules using a graph neural-network architecture based on features from low-cost approximated quantum operators in the symmetry-adapted…

The MUGAST-AGATA-VAMOS set-up at GANIL combines the MUGAST highly-segmented silicon array with the state-of-the-art AGATA array and the large acceptance VAMOS spectrometer. The mechanical and electronics integration copes with the…

A major challenge in quantum information is characterizing entanglement, for which entanglement witnesses offer effective means of detecting quantum correlations. We introduce a hybrid quantum-classical framework that learns a nonlinear…

Quantum--Mechanical methods that are both computationally fast and accurate are not yet available for electronic excitations having charge transfer character. In this work, we present a significant step forward towards this goal for those…

The detection of phase transitions in quantum many-body systems with lowest possible prior knowledge of their details is among the most rousing goals of the flourishing application of machine-learning techniques to physical questions. Here,…

A challenge preventing successful inverse kinematics measurements with heavy nuclei that are not fully stripped is identifying and tagging the beam particles. For this purpose, the HEavy ISotope Tagger (HEIST) has been developed. HEIST…

Deep learning has emerged as a prominent field in recent literature, showcasing the introduction of models that utilize transfer learning to achieve remarkable accuracies in the classification of brain tumor MRI images. However, the…

The study of the electronic properties of charged defects is crucial for our understanding of various electrical properties of materials. However, the high computational cost of density functional theory (DFT) hinders the research on large…

AMS-02 is a detector currently in operation onboard the International Space Station (ISS). One of the main scientific goals of the spectrometer is the measurement of charged particle fluxes. The detector design makes possible the…