The paper introduces a physics-informed self‑supervised learning framework that jointly calibrates wire gains and reconstructs interaction positions in Multi‑Wire Parallel Plate Avalanche Counters (MWPPACs) without labelled data or dedicated calibration runs. By treating calibration as a latent optimization problem, the method simultaneously estimates global wire gains and event‑wise positions using only detector geometry and charge‑energy consistency constraints. A detector‑independent neural network learns sub‑wire position reconstruction from local charge distributions, enabling continuous self‑calibration and improved spatial homogeneity and resolution, as demonstrated on the VAMOS++ spectrometer’s entrance MWPPACs.
By Antoine Lemasson, Maurycy Rejmund
arXiv:2606. 04033v1 Announce Type: new Abstract: The validation of advanced nuclear reactor designs and fuel concepts requires critical experiments with high neutronic similarity to the target technology.
By Will Savage, Logan Burnett, Dean Price
arXiv:2601.12971v2 Announce Type: replace
Abstract: Physics-informed neural networks (PINNs) can be limited by coordinate representations and conflicting gradients from heterogeneous physical constra...
By Pancheng Niu, Jun Guo, Qiaolin He, Yongming Chen, Yanchao Shi
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.
By Omar Alterkait, C\'esar Jes\'us-Valls, Ryo Matsumoto, Patrick de Perio, Kazuhiro Terao
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:2512. 24116v3 Announce Type: replace-cross Abstract: Parton Distribution Functions (PDFs) play a central role in describing experimental data at colliders and provide insight into the structure of nucleons.
By Amedeo Chiefa, Luigi Del Debbio, Richard Kenway
arXiv:2507. 07109v2 Announce Type: cross Abstract: The VAMOS++ magnetic spectrometer is a multi-parametric system that integrates ion optical magnetic elements with a multi-detector stack.
By M. Rejmund, A. Lemasson
arXiv:2606. 03355v1 Announce Type: new Abstract: Physics models are inherently imperfect due to misspecified or missing mechanisms, resulting in systematic discrepancies between model predictions and real-world observations.
By Aishwarya Venkataramanan, Sai Karthikeya Vemuri, Joachim Denzler
arXiv:2601.11716v2 Announce Type: replace-cross
Abstract: Accurate and efficient detector simulation is essential for modern collider experiments. To reduce the high computational cost, various fast...
By Thorsten Buss, Henry Day-Hall, Frank Gaede, Gregor Kasieczka, Katja Kr\"uger
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
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
Physics-Informed Error Field Learning (PIEFL) is a post‑training optimization framework for Physics‑Informed Neural Networks (PINNs). After a primary network reaches satisfactory accuracy, PIEFL introduces an auxiliary error network that learns the discrepancy between the current approximation and the exact solution by deriving error control equations under physical constraints. The learned error correction is then combined with the primary prediction, improving solution accuracy without modifying the primary network architecture and focusing computational resources on correcting existing prediction errors.
By Jiuyun Sun, Yong Zhang