AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
Deep learning models trained with domain adaptation strategies and mixed datasets from multiple event generators demonstrate stable performance under limited and partially decorrelated detector information and extend the previously developed Deep Reconstruction Neural Network model to estimate the number of spectators.
The Neutrino Observatory in the Nanhai (NEON) is a proposed deep-sea neutrino telescope deployed in the South China Sea. Accurate reconstruction of shower-like events is crucial for neutrino energy measurements and multi-messenger astronomy, yet it poses significant challenges due to seawater optical attenuation, irreg...
We present the capabilities of the Fluorescence detector Array of Single-pixel Telescopes (FAST) observatory in the single telescope configuration as an important step towards the possible future large-field observatory for detecting ultra-high-energy cosmic rays. Reconstruction of main shower physics parameters are ex...
Physics-Informed Neural Networks (PINNs) continue to suffer from limited training efficiency in predicting electromagnetic scattering from three-dimensional (3D) targets, resulting in particularly high retraining costs when target geometries or incident fields vary. To address this issue, we propose a transferable PINN...
Transfer and multi-nucleon transfer reactions are essential tools for probing nuclear structure and reaction dynamics, requiring precise determination of the identity, energy, and emission angles of reaction products. The increasing granularity of modern silicon telescope arrays enhances experimental capabilities but c...
In the recent years, high energy physics discoveries have been driven by the increasing of luminosity and/or detector granularity. This evolution gives access to bigger statistics and data samples, but can make it hard to process the detector outputs with current methods and algorithms. Graph Neural Networks (GNNs), ha...
Selection efficiency connects observed counts to physical rates and rare-event search limits. With scarce calibration data, strong smoothing can erase sharp physical changes, while highly flexible models can overfit the data and mistake statistical fluctuations for physical structure. We introduce a density-guided cond...
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