arXiv (All)
open access
Machine-learning-based neutrino-event generation constrained by experimental measurements
high-energy-physics-experimenthigh-energy-physics-phenomenologymachine-learningnuclear-experimentnuclear-theory
We present a generative surrogate for charged-current \(ν_μ\)-nucleus scattering that can be calibrated to measured differential cross sections without event-level experimental samples. Starting from a theoretical interaction model, we fine-tune the surrogate to a MicroBooNE measurement and show tha
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Testing coherent elastic neutrino-nucleus scattering N dependence#1296635
- Event Generation for Neutrino and Muon DIS at the LHC#1341719
- First Measurement of the Superscaling Variable as a Function of Visible Kinematics in Charged-Current Quasi-Elastic Neutrino--Nucleus Scattering#1141659
- Flow-Based Surrogates for High-Dimensional Likelihoods in Experimental Neutrino Physics#629547
- Neutrino Tridents and $W$-boson production at the Muon Collider#1342139
Record · ID 1356206
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive