AI/ML for Astronomical Discovery
The field of astronomical discovery is being fundamentally reshaped by the integration of Artificial Intelligence (AI) and Machine Learning (ML). The sheer volume, velocity, and complexity of data generated by modern telescopes necessitate advanced computational techniques to extract meaningful scientific insights. AI/ML algorithms are proving indispensable for tasks ranging from the classification of celestial objects and the detection of rare cosmic phenomena to the interpretation of intricate spectral data and the exploration of fundamental cosmological parameters.
This research area encompasses a broad spectrum of methodologies, including deep learning for image analysis, generative models for anomaly detection, unsupervised techniques for pattern recognition, and specialized natural language processing for knowledge synthesis. These tools enable astronomers to move beyond traditional manual analysis, facilitating the identification of novel objects, the construction of comprehensive catalogs, and the acceleration of scientific hypothesis testing. Specific applications include improving image quality through deconvolution, dissecting galaxy morphology, identifying strong gravitational lenses, and estimating crucial astrophysical properties like redshifts and peculiar velocities.
A burgeoning frontier in this domain involves the development of Large Language Models (LLMs) tailored for scientific reasoning and data interpretation. These models hold the promise of transforming how astronomers interact with vast knowledge bases, synthesize research findings, and even interpret complex observational data such as spectroscopy, thereby democratizing access to high-level scientific expertise and accelerating the pace of discovery.
My work has been dedicated to pioneering and advancing these AI/ML applications for astronomical discovery. I have developed specialized Large Language Models, as demonstrated in the AstroMLab series, which achieve benchmark-topping performance in astronomy Q&A and rival models like GPT-4o, specifically by specializing 8B and 70B parameter models for the domain. Furthermore, I have focused on teaching LLMs to interpret and “speak” spectroscopy, enhancing their utility for direct scientific data analysis.
Beyond language models, my research encompasses robust image and spectral analysis. I have contributed to advanced methods for anomaly detection in astronomical images, utilizing Generative Adversarial Networks (GANs) to identify peculiar galaxies in datasets like Hyper Suprime-Cam. My contributions also include developing deep learning pipelines for detecting and modeling galaxy-scale strong gravitational lenses, exploring galaxy morphology using unsupervised machine learning to move “Beyond the Hubble Sequence,” and employing neural networks for Point Spread Function deconvolution to improve image fidelity. Additionally, I have developed machine learning synthetic spectra for probabilistic redshift estimation (SYTH-Z) and deep neural networks for peculiar velocity estimation from the kinetic Sunyaev-Zel’dovich effect, significantly enhancing our ability to probe the universe’s large-scale structure.
