AI for Astrophysics: Enhancing Scientific Discovery with Large Language Models

— *Authored by ****Diksha Shrivastava ***(diksha-shrivastava13.github.io)

Introduction

Astrophysics is a field rich in complex mathematical modeling, large-scale simulations, and intricate theoretical frameworks. While modern Large Language Models (LLMs) have shown promise in scientific reasoning, they still struggle with core aspects of astrophysical research, such as symbolic reasoning, theorem proving, and interpretable decision-making. Their limitations in rigorous mathematical modeling and domain-specific computational tools hinder their application in high-stakes scientific discovery.

This project aims to bridge that gap by developing an open-source AI framework that integrates LLMs with astrophysical datasets, simulation tools, and theorem-proving systems. By leveraging advances in neurosymbolic AI, curriculum learning, and reinforcement learning, the framework will enable LLMs to:

• Generate executable astrophysical simulations and analysis scripts with tool-specific correctness.
• Engage in interactive, self-improving scientific reasoning through a structured reinforcement learning approach.
• Ensure mathematical rigour and interpretability by integrating automated theorem proving with astrophysical knowledge.

Through this, the project will not only enhance the ability of LLMs to assist in astrophysical discovery but also push the boundaries of AI-driven scientific research by making complex models more interpretable, reliable, and aligned with established physical laws.

Research Objectives

1. Develop Tool-Specific Code Generation: Implement a module that enables LLMs to generate executable scripts for astrophysical tools (e.g., Morpheus, yt, and Lux Supercomputer environments) while ensuring correctness and efficiency.
2. Formalise Interactive Astrophysical Reasoning: Model astrophysical simulations as an interactive reasoning environment, where LLMs generate and refine hypotheses through self-play and reinforcement learning.
3. Enhance Interpretability and Mathematical Rigour: Integrate automated theorem proving, symbolic-numeric hybrid models, and neurosymbolic AI to ensure that generated hypotheses adhere to fundamental astrophysical laws.
4. Refine LLMs for Domain-Specific Expertise: Employ curriculum learning and dataset-driven fine-tuning using YSE data, cosmic simulations, and high-energy astrophysics observations.
5. Ensure Scientific Alignment: Validate AI-generated insights by comparing them against existing research, expert evaluations, and experimental data.

Methodology

1. Tool-Specific Code Generation

Existing LLMs can generate SQL queries for databases but lack domain-specific adaptation for astrophysical tools. This project will fine-tune models on API documentation, scientific papers, and tool-specific workflows to enable code generation for Morpheus and yt.

Code Generation Algorithm

Let $Q$ be a natural language query, $T$ be the target tool, and $\mathcal{M}$ be the fine-tuned LLM. The generated code $C$ is given by: $C = \mathcal{M}(Q, T)$ where $\mathcal{M}$ is trained on paired datasets $\{(Q_i, T_i, C_i)\}_{i=1}^{N}$.

Training involves reinforcement learning with a correctness function $\mathcal{L}_c$, measuring execution success: