Astronomy, born from humanity’s innate curiosity about the stars, has long been a catalyst for revolutionary discoveries. As AI technology advances, intelligent agents powered by large language models (LLMs) are opening up opportunities for astronomers to explore the universe more effectively and unlock its mysteries.
This technological shift is shaping a discipline that, while specialized, has long driven scientific progress. While astronomy occasionally captures public attention through Nobel Prize-winning discoveries, much of astronomers’ work unfolds far from the spotlight. At its core, the discipline seeks to explain the universe’s vast array of phenomena—from the behavior of hydrogen atoms to the dynamics of entire galaxies.
Two distinct challenges set astronomy apart from other sciences, shaping how astronomers approach discovery:
- Extreme physical conditions make direct experimentation impossible, as replicating the environments of celestial bodies in a laboratory is infeasible. This limitation leaves many astronomical theories subject to debate, with even small questions often giving rise to numerous competing hypotheses that may remain unresolved for decades.
- Because astronomy has few practical applications, its impact is often indirect. Unlike fields like biotechnology or material science, astronomy rarely yields direct, tangible benefits. Instead, its value often lies in providing foundational insights that support progress in other disciplines. For example, observations of the Sun’s spectral lines were pivotal to the development of quantum mechanics, while studies of Mercury’s orbit offered the first confirmation of Einstein’s theory of general relativity.
These constraints make explainability crucial in astronomical research, often limiting the use of opaque, black-box AI models. Fortunately, LLMs, pretrained on vast amounts of text, offer a promising solution. They not only possess extensive astronomical knowledge but also demonstrate strong logical reasoning, enabling them to construct causal models to explain observed phenomena.
Against this backdrop, Microsoft Research Asia is pleased to introduce Mephisto, (opens in new tab) an LLM agent designed to analyze high-redshift galaxies observed by the James Webb Space Telescope (JWST). This approach has led to potential explanations for the little red dot (opens in new tab) (LRD) from the early universe, demonstrating how LLMs can function as logical inference engines for scientific discovery and establishes a new paradigm where LLMs act as logical inference engines for discovery.
Did you know: High-redshift galaxies are located very far from Earth. As their light travels through the expanding universe, and their wavelengths are stretched, shifting toward the red end of the spectrum—a phenomenon known as redshift. The redshift value (z) quantifies this effect, representing the ratio of the galaxy’s speed of recession relative to the speed of light. A higher redshift value indicates a galaxy that is farther away and older.
Mephisto assists with galaxy data analysis
Analyzing the physical properties of individual galaxies is a fundamental skill in astronomy. It requires a thorough understanding of galaxy formation theories and the ability to interpret vast amounts of observational data. However, even for seasoned astronomers, this process can be time-consuming and labor-intensive.
To streamline this process, Mephisto assists astronomers by analyzing photometric data from distant galaxies, proposing physical models and interacting with CIGALE (opens in new tab) (Code Investigating Galaxy Emission), a commonly used galaxy spectral simulation program. Mephisto detects discrepancies between models and observational data, identifies potential instrumental errors or limitations in the models, iteratively adjusts parameters, and generates multiple explanations for the data.e multiple possible explanations for the observational data.
Beyond its analytical capabilities, Mephisto stands out for its natural language knowledge base and memory. Its domain-specific body of knowledge grows through reinforcement learning as it interacts with astronomers and observational data. This not only enhances Mephisto’s accuracy but also broadens its role in scientific discovery. The knowledge it extracts has real physical significance, reflecting the strengths and limitations of various physical models under different conditions.
Mephisto only inspires new research ideas for experienced astronomers but also serves as a valuable reference for newcomers. By mimicking scientific reasoning processes, Mephisto formalizes hypothesis generation and optimization into a tree-search framework, facilitating more in-depth and systematic analysis.
Mephisto analyzes JWST LRD data to refine models and hypotheses
Evaluations using diverse data sets on cutting-edge scientific problems demonstrate that Mephisto can continually refine physical models to better align with observational data refining its hypotheses.
Mephisto’s modeling of Spectral Energy Distributions (SEDs)—simplified physical frameworks used to interpret the composition of galaxies—is shown in Figure 3, demonstrating its iterative process of refining its output. Mephisto begins with a basic SED model using data on the light flux emitted by galaxies across different wavelengths to interpret their composition. It then iteratively explores and evaluates possibilities, offering explanations that more closely align with observational data. During this exploration, Mephisto not only narrows the set of hypotheses for current observations but also tests the robustness of its conclusions against different model options.
Astronomers can use these reports to revise observations, refine theoretical models, and deepen their scientific understanding. Figure 4 shows Mephisto’s process for refining its hypotheses.
Mephisto’s parameter-space exploration of LRD origins
When addressing cutting-edge scientific challenges, such as the LRDs observed by the JWST—an enigmatic class of celestial objects that could reshape astronomers’ understanding of the universe—Mephisto excels, often matching or even exceeding experts in hypothesis exploration. By systematically evaluating all potential explanations for these objects, Mephisto helps astronomers uncover insights that extend beyond the current theoretical framework.
One such example is JADES LRD 79803, a mysterious «little red dot» identified by the JWST, as shown in Figure 5. Named for their red color and compact morphology, these galaxies have a distinct V-shaped SED. The scientific community debates two main hypotheses: these objects could be dusty star-forming galaxies or supermassive black holes with possible dust obscuration.
To investigate their origins, Mephisto systematically explores a range of physical properties—including star formation history, dust content, and black hole activity—within a three-dimensional parameter space of galaxy stellar mass, dust extinction, and black hole influence. Its conclusions closely align with those of astronomers—indicated by the red dots in Figure 5—while also offering a more comprehensive analysis.
Abundant in the early universe, these galaxies pose challenges to existing cosmological theories. By continuously analyzing such data, Mephisto helps astronomers refine their models and expand our understanding of the cosmos.
AI: A new collaborative paradigm for astronomical discovery
Mephisto is changing the way astronomers interact with AI. They can directly communicate with it using natural language, sharing their domain knowledge and research requirements without the need to repeatedly train the LLM, which is resource-intensive. The AI, in turn, delivers its findings in the same accessible format. This approach enables seamless knowledge transfer between LLMs and different galaxy spectral simulations, eliminating redundant training.
Mephisto’s reasoning process is grounded in current galaxy formation theories, maintaining a transparent, white-box approach to problem-solving. This interpretability ensures that Mephisto integrates smoothly into the existing scientific research paradigm.
It continuously analyzes vast datasets, adapting and improving while mitigating biases in scientific research. Its ability to autonomously refine hypotheses allows it to challenge conventional models and expand scientific inquiry.
Mephisto can also run on supercomputers, analyzing underutilized photometric galaxy data and delivering valuable insights. Its accessibility extends beyond professional astronomers, enabling amateur researchers and citizen scientists to contribute to discoveries in meaningful ways.
Looking ahead, LLMs will evolve into even more sophisticated reasoning engines, automating complex scientific analysis across all areas of astronomy. This approach promises to accelerate progress in the field, enabling AI systems to collaborate with astronomers in pushing the frontiers of our understanding of the universe.
Artificial intelligence