arXiv:2602. 24129v3 Announce Type: replace-cross Abstract: Large-scale homogeneous detectors with optical readouts are widely used in particle detection, with Cherenkov and scintillator neutrino detectors as prominent examples.
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...
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.
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.
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.
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.
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.
arXiv:2606. 04165v1 Announce Type: cross Abstract: High-precision calorimeter simulation at current and future colliders imposes rapidly growing computational demands, motivating the development of machine-learning surrogates for traditional Monte Carlo tools such as Geant4.
VyPER is a new geometric learning framework that reconstructs particle collider events by representing them as hypergraphs with a physics-inspired topology. It tackles two key tasks: assigning measured jets and leptons to their parent particles through supervised hyperedge classification, and predicting neutrino kinematics using a diffusion model, all optimized jointly with a shared loss function. The authors evaluate VyPER on various proton‑proton collision processes, showing improved performance over existing analytical and machine‑learning methods and enabling more precise measurements in Higgs, electroweak, and top‑quark studies.
arXiv:2610. 20870v1 Announce Type: cross Abstract: We present a generative surrogate for charged-current \(\nu_\mu\)-nucleus scattering that can be calibrated to measured differential cross sections without event-level experimental samples.
arXiv:2606. 16035v1 Announce Type: cross Abstract: Modern particles physics experiments have demonstrated an increasing need for fast, high-fidelity detector simulation as detector components have improved and subsequent computational requirements approach the limits of available resources.
arXiv:2609.23385v1 Announce Type: cross Abstract: Data acquisition (DAQ) systems at future particle physics experiments stand to benefit from the extremes of AI/ML development: large-scale foundation...
arXiv:2604. 22160v2 Announce Type: replace-cross Abstract: Human visual perception offers valuable insights for understanding computational principles of motion-based scene interpretation.