# Neural-Network Surrogate

The authors performed ab initio continuum distorted-wave eikonal initial-state (CDW-EIS) calculations of atomic ionization for targets from H to Kr under bare-ion and antiproton impact, released a public database of 6239 subshell-resolved total ionization cross sections across about 130 projectile-target systems, and trained a five-member neural-network surrogate, CDWEIS-NN, to interpolate and predict projectile-target combinations and impact energies not calculated directly.

The authors performed ab initio continuum distorted-wave eikonal initial-state (CDW-EIS) calculations of atomic ionization for targets from H to Kr under bare-ion and antiproton impact, released a public database of 6239 subshell-resolved total ionization cross sections across about 130 projectile-target systems, and trained a five-member neural-network surrogate, CDWEIS-NN, to interpolate and predict projectile-target combinations and impact energies not calculated directly.

Astrophysics research must juggle fast-growing literature, multiwavelength observations and complex physical models, and AstroGenesis places these in one multi-agent environment where a Supervisor Agent and Planner/Replanner coordinate literature retrieval, observational data analysis, theoretical modeling and research ideation. The current implementation focuses on blazar research, and its Theoretical Modeling Agent uses pretrained neural-network surrogate models to support broadband and multimessenger modeling through natural language. On single-paper and multi-paper literature benchmarks, at least one relevant publication appeared in the top five results for 76.6% and 79.2% of questions.

Astrophysics research must juggle fast-growing literature, multiwavelength observations and complex physical models, and AstroGenesis places these in one multi-agent environment where a Supervisor Agent and Planner/Replanner coordinate literature retrieval, observational data analysis, theoretical modeling and research ideation. The current implementation focuses on blazar research, and its Theoretical Modeling Agent uses pretrained neural-network surrogate models to support broadband and multimessenger modeling through natural language. On single-paper and multi-paper literature benchmarks, at least one relevant publication appeared in the top five results for 76.6% and 79.2% of questions.