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.
By Lining Mao, Yvonne Peters, Ethan Simpson, Zihan Zhang
arXiv:2606. 03745v1 Announce Type: cross Abstract: Determining the neutrino mass ordering remains a central open problem in particle physics.
By T. J. C. Bezerra, L. Asquith, E. Bannister, W. Shorrock
arXiv:2608. 15042v1 Announce Type: cross Abstract: We perform a global search for values of the Yukawa matrices and Majorana masses in the Type-I seesaw mechanism.
By Haruto Kitagawa, Satsuki Nishimura, Hajime Otsuka
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.
By Cole Granger, James Giroux, Richard Tyson, Maurizio Ungaro, Cristiano Fanelli
arXiv:2509. 08048v4 Announce Type: replace-cross Abstract: Generative networks are perfect tools to enhance the speed and precision of LHC simulations.
By Henning Bahl, Sascha Diefenbacher, Nina Elmer, Tilman Plehn, Jonas Spinner
The article reviews recent progress in applying machine learning to high‑energy nuclear physics, highlighting a shift from basic pattern recognition to physics‑integrated workflows. It covers calibrated Bayesian extraction of QCD matter properties, equation‑of‑state inference from heavy‑ion and neutron‑star data, generative event modeling, neural unfolding of weak signals, differentiable inverse solvers, gauge‑equivariant lattice‑field samplers, and holographic QCD reconstructions. The focus is on how ML models incorporate physical constraints—symmetries, conservation laws, causality, thermodynamic stability, and topology—and how uncertainty quantification and validation ensure reliable physics conclusions.
By Xun Chen, Weiyao Ke, Yu-Gang Ma, Long-Gang Pang, Kai Zhou