arXiv:2609.20868v1 Announce Type: cross
Abstract: Transfer and multi-nucleon transfer reactions are essential tools for probing nuclear structure and reaction dynamics, requiring precise determinatio...
By M. Rejmund, A. Lemasson, P. Morfouace, D. Ramos, J. Taieb, J. D. Frankland
arXiv:2606. 20437v1 Announce Type: cross Abstract: Charged-particle tracking -- reconstructing trajectories from sparse detector measurements -- is a fundamental high-energy-physics inference problem and a canonical example of learning under extreme combinatorial ambiguity.
By Siqi Miao, Shitij Govil, Jack P. Rodgers, Mia Liu, Javier Duarte, Shih-Chieh Hsu, Yuan-Tang Chou, Pan Li
The paper presents candela, a differentiable SIREN neural field that learns the photon Green's function for the IceCube Neutrino Observatory. It predicts photon yield and full arrival-time distribution for point-like energy deposits, enabling complete event simulation by superposing responses from multiple deposits. Trained on Monte‑Carlo data, candela produces events 50–100× faster than existing methods while maintaining median yields within 2% of MC expectations and timing distributions at the MC statistical floor across six photon‑count decades.
By Felix J. Yu, Berthy T. Feng, Nicholas Kamp, Carlos A. Arg\"{u}elles
Panda Diplomacy introduces a point‑cloud self‑distillation framework that enables a single foundation‑model architecture and objective to be pre‑trained across three distinct particle‑detector modalities—liquid argon time‑projection chambers, collider TPCs, and water Cherenkov detectors—without extensive modification. Using only 1,000 labeled images for downstream adaptation, the resulting Panda V2 model matches or surpasses specialized baselines that require orders of magnitude more supervision, achieving state‑of‑the‑art particle‑clustering performance with 70× fewer labeled events on sPHENIX and up to 1,000× fewer labels on LArTPC data. Linear probes further demonstrate that the model’s latent space captures physically meaningful structures such as particle causality and track curvature.
By Samuel Young, C\'esar Jes\'us-Valls, Kazuhiro Terao
BEAM3R is a dose estimation framework that operates in beam’s-eye-view (BEV) for both photon and proton beamlet calculations. It uses a Mamba‑3 state‑space depth‑sequence core combined with physics‑based transport conditioning, sharing a 2D CNN encoder‑decoder architecture to process per‑plane BEV slices. The system achieves high accuracy on the DoseRAD2026 challenge, with local gamma pass rates above 96% for CT‑based models and runtimes under 25 s for photon and 18 s for proton predictions.
By Chen Cheng, Michael Ferraro, James Grover, David E J Waddington, Emily Hewson
arXiv:2604. 01313v2 Announce Type: replace Abstract: High-fidelity simulations and complex inverse problems, such as detector modeling and unfolding, are computationally intensive bottlenecks across subatomic physics, yet essential for accurate physical interpretation.
By Zeyu Xia, Tyler Kim, Trevor Reed, Judy Fox, Geoffrey Fox, Adam Szczepaniak