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.
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.
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.
arXiv:2511. 22246v2 Announce Type: replace-cross Abstract: Unsupervised learning has been widely applied to various tasks in particle physics.
arXiv:2607. 20871v1 Announce Type: cross Abstract: Scaling semiconductor quantum dot arrays toward fault-tolerant quantum computing requires efficient tuneup of spin qubits, a process that depends on the analysis of charge stability maps (CSMs) and remains largely manual.
arXiv:2412. 10665v3 Announce Type: replace-cross Abstract: We introduce a foundation model for event classification in high-energy physics, built on a Graph Neural Network architecture and trained on 120 million simulated proton-proton collision events spanning 12 distinct physics processes.
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.
arXiv:2608. 14764v1 Announce Type: new Abstract: With the increasing integration of renewable energy sources, energy storage systems have become essential, making the accurate estimation of their State of Health (SOH) and degradation behavior critical.
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.
arXiv:2607. 19597v1 Announce Type: cross Abstract: We present FlareEUV, a multimodal deep learning framework for predicting daily extreme ultraviolet (EUV) irradiance at 6.
arXiv:2607. 28079v1 Announce Type: new Abstract: Chemical property prediction plays a critical role in accelerating scientific discovery in chemistry, materials science, and drug development.
arXiv:2606. 14813v1 Announce Type: cross Abstract: Jet tagging at the Large Hadron Collider increasingly relies on deep learning models trained on massive simulated datasets, leading to high computational costs and limited robustness to detector mismodeling.
Precise knowledge of nuclear structure is essential across fundamental physics, yet probing these structures is notoriously difficult. To address this challenge, ultra-peripheral collisions (UPCs) provide a femtoscopic tomography for imaging the atomic nucleus.