The paper presents a self‑supervised learning approach for reconstructing the mass of a heavy resonance in cascade decays with missing energy. By pre‑training a transformer encoder with VICReg to learn corruption‑invariant embeddings and then fine‑tuning for mass regression, the authors demonstrate sharper resonance peaks and more stable performance compared to a supervised model trained from scratch on the same data. The study focuses on resonances with masses between 2.5 and 6.5 TeV decaying into an eleven‑body final state.
By Ho Fung Tsoi, Alex Yang, Luis Felipe Gutierrez Zagazeta, Shion Chen, Dylan Rankin
arXiv:2608. 00090v1 Announce Type: cross Abstract: The extraction of $\beta$-feeding distributions in Total Absorption $\gamma$-ray Spectroscopy constitutes a challenging inverse problem, particularly in nuclei with complex decay schemes involving a large number of excited states.
By J. Balibrea-Correa, E. N{\'a}cher, C. Fonseca-Vargas, J. L. Tain
arXiv:2608.23750v1 Announce Type: cross
Abstract: Reconstructing scattering amplitudes from finite, noisy, and mutually inconsistent measurements is an ill-posed inverse problem common to many reacti...
By Wyatt A. Smith, Arkaitz Rodas, Marius D. Thomas, C\'esar Fern\'andez-Ram\'irez, Giorgio Foti, Lin Qiu, Adam P. Szczepaniak, Alessandro Pilloni
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.
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
The paper presents deep‑learning trigger algorithms for the Hyper‑Kamiokande water Cherenkov detector, targeting low‑energy neutrino events below 7 MeV. It compares a supervised neural‑network classifier with two anomaly‑detection methods—an autoencoder and a Manifold Projection‑Diffusion Recovery model—showing the supervised model achieves a 76.7 % signal efficiency for 3 MeV electrons, far surpassing the 26.4 % efficiency of a traditional hit‑count trigger. GPU‑based runtime tests indicate per‑window inference latencies well below one millisecond.
By Katharina Lachner, Sa\'ul Alonso-Monsalve, Benjamin Richards, Davide Sgalaberna
arXiv:2606. 14874v1 Announce Type: cross Abstract: High-purity germanium gamma spectra often require time-consuming analyses from subject matter experts.
By Samuel Emmons, Kelly Truax, Maurice Lonsway, Bruce Pierson, Brian Archambault
TetrisCNN is a convolutional neural network that uses parallel branches of differently shaped filters to learn sparse, interpretable latent representations directly from spin correlators. Applied to experimental snapshots of two-dimensional Ising and XY quantum simulators, it detects phase transitions and crossovers while expressing its decision boundaries as symbolic formulas built from measurable spin correlators. This approach bridges the gap between black‑box neural networks and physically interpretable models, enabling automated discovery of new phases of matter from realistic, noisy experimental data.
arXiv:2602. 10330v3 Announce Type: replace-cross Abstract: Context: The characterization of exoplanetary atmospheres has been transformed by the James Webb Space Telescope (JWST), whose infrared sensitivity enables transmission spectroscopy at unprecedented precision.
By David S. Duque-Casta\~no, Lauren Flor-Torres, Jorge I. Zuluaga
The paper presents NuCLR, a multi-task neural network that learns nuclear data representations to predict charge radii and electric‑quadrupole transition strengths across hundreds of nuclides. Using held‑out ensembles, the model achieves a charge‑radius RMS deviation of 0.0147 fm and a B(E2) RMS deviation of 0.192 e²b², comparable to leading nuclear models. The authors provide error bars indicating where additional experimental data could improve predictions, positioning NuCLR as a data‑driven surveyor of nuclear structure.
By Giuliano Giacalone, Sokratis Trifinopoulos, Mike Williams
TetrisCNN is a convolutional neural network that uses parallel branches of differently shaped filters to learn sparse, interpretable latent representations directly in terms of spin correlators. Applied to experimental snapshots from two-dimensional Ising and XY quantum simulators measured in multiple bases, the network detects phase transitions and crossovers while expressing its decision boundaries as symbolic formulas built from experimentally measurable spin correlators. This approach bridges the gap between black‑box neural network methods and physically interpretable models, enabling automated detection of phases of matter from realistic, noisy experimental data.
By Kacper Cybi\'nski, Bj\"orn van Zwol, James Enouen, Guillaume Bornet, Thierry Lahaye, Antoine Browaeys, Antoine Georges, Anna Dawid
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.
By Joshua Ho, Benjamin Ryan Roberts, Shuo Han, Haichen Wang