Physics-Informed Self-Supervised Learning for Joint Wire Calibration and Interaction Position Reconstruction in Multi-Wire Parallel Plate Avalanche Counters
Read the original on arXiv Machine Learning →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.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.