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
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
The paper investigates whether two particle physics foundation models, OmniLearned and ParticleViT, pretrained on high‑energy proton–proton and electron–proton collisions can transfer knowledge to a low‑energy neutrino experiment. Using MINERvA neutrino–nucleus scattering data, the authors evaluate the models on energy regression and charged‑current pion classification tasks, finding that the pretrained models outperform similarly sized models trained from scratch, with OmniLearned excelling in regression and ParticleViT in classification. When the same transformer architecture is initialized from unrelated text pretraining (BERT), the performance advantage is minimal for classification and absent for regression, indicating that particle‑level foundation models capture inductive biases that generalize across energy scales, detector technologies, and physics processes.
By Gregor Krzmanc, Vinicius Mikuni, Benjamin Nachman, Callum Wilkinson
The paper introduces a particle‑level generative model that uses residual‑quantized full‑event data to enable fast, ML‑based surrogate simulation for collider events. It demonstrates conditional generation from detector‑stable particles, explores scaling across dataset and model sizes, and shows that token‑level loss predicts downstream physical fidelity. The work offers an empirical framework for scalable collider full‑event generation using residual‑quantized representations.
By Dan Godi, Dmitrii Kobylianskii, Eilam Gross
arXiv:2606. 14373v1 Announce Type: cross Abstract: The workflow from particle collision to physics analysis passes through a series of reconstruction steps that are traditionally modular and disconnected, with no shared representation linking low-level detector data to high-level analysis tasks.
By Farouk Mokhtar, Joosep Pata, Michael Kagan, Javier Duarte
The paper introduces a model calibration method using optimal transport to address discrepancies between simulation and experimental data in high-dimensional machine learning applications. Applied to jet tagging in particle physics, the technique calibrates a 128‑dimensional latent representation from a general‑purpose classifier, ensuring downstream derived quantities are properly calibrated. This enables more reliable use of foundation models for jet flavor analysis in LHC experiments and offers a general framework for correcting high‑dimensional simulations across scientific fields.
By Malte Algren, Tobias Golling, Francesco Armando Di Bello, Christopher Pollard
The paper presents three deep‑learning models— a gated spatiotemporal graph neural network and two Transformer‑based architectures— designed to identify and tag photons from $^{14}$C beta decay in large liquid scintillator detectors. By applying these models to simulated events containing both a $^{14}$C decay and a low‑energy positron, the authors achieve $^{14}$C recall rates between 25% and 48% while keeping positron misidentification below 1%. This selective tagging improves the total charge resolution for events where the $^{14}$C and positron photon hits overlap in space and time.
By Junle Li, Zhaoxiang Wu, Guanda Gong, Zhaohan Li, Wuming Luo, Jiahui Wei, Wenxing Fang, Hehe Fan