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