Artificial Intelligence and Machine Learning

Astronomy is in the midst of a data revolution. Current and upcoming surveys — including the Rubin Observatory's Legacy Survey of Space and Time, DESI, and the Roman Space Telescope — are generating datasets so vast that traditional analysis techniques cannot fully exploit them. At the same time, rapid advances in artificial intelligence and machine learning are opening new possibilities for extracting physical insights from complex, high-dimensional data. KIPAC researchers are working at this intersection, advancing the frontiers of astrophysics through AI/ML while simultaneously pushing the frontiers of AI/ML methods in pursuit of discovery.

This work is anchored by the Center for Decoding the Universe @ Stanford (C4DU), launched in 2024 as a joint initiative of Stanford Data Science and KIPAC. C4DU brings together astrophysicists, statisticians, and computer scientists to develop cutting-edge methodologies for extracting insights from vast, multi-modal astronomical datasets. The Center builds on KIPAC's deep connections to SLAC's AI/ML initiative, Stanford Data Science, and the Stanford Institute for Human-Centered AI (HAI).

KIPAC researchers apply AI/ML across a broad range of astrophysical problems. For example:

Simulation-Based Inference and Emulation

Modern cosmological analyses require comparing observations to theoretical predictions across high-dimensional parameter spaces. KIPAC researchers develop machine learning emulators that can rapidly predict observables — galaxy clustering, weak lensing signals, halo mass functions — as a function of cosmological parameters, enabling rigorous statistical inference that would otherwise be computationally intractable.

Hierarchical Inference

Many astrophysical problems involve inferring population-level properties from noisy observations of individual objects. KIPAC researchers develop hierarchical neural network approaches for problems including strong gravitational lensing, where the goal is to constrain cosmology and dark matter properties from ensembles of lens systems while properly propagating uncertainties.

Photometric Redshifts and Classification

Measuring distances to billions of galaxies from photometry alone is a defining challenge for Stage IV dark energy experiments. KIPAC researchers develop machine learning techniques to estimate photometric redshifts and their uncertainties, with particular emphasis on identifying where predictions are unreliable and quantifying systematic errors.

Anomaly Detection and Discovery

The volume of data from modern surveys creates opportunities for machine-assisted discovery. KIPAC researchers develop methods to identify novel phenomena — rare transients, unusual spectral signatures, unexpected signals in dark matter detectors — that traditional targeted searches would miss.

Real-Time Classification

Time-domain surveys like Rubin LSST will generate millions of transient alerts per night. KIPAC researchers contribute to ML-based classification systems that rapidly characterize transients and prioritize targets for spectroscopic follow-up, enabling science from rare phenomena including gravitational wave counterparts and tidal disruption events.

Foundation Models

KIPAC researchers are developing principled foundation models for astrophysics that can learn representations across spatial scales — from individual galaxies to large-scale structure — enabling transfer learning and more data-efficient inference for downstream tasks.

Instrumentation and Detector Enhancement

AI/ML methods are increasingly important for instrument optimization and real-time data processing. Applications include enhancing signal-to-noise and energy resolution for X-ray detectors aboard satellites, where on-board algorithms must separate astrophysical signals from cosmic ray backgrounds under strict telemetry constraints.

AI-Augmented Research

KIPAC researchers are exploring how AI agents and large language models can accelerate scientific discovery — from literature synthesis and hypothesis generation to automated analysis pipelines — while developing benchmarks and methodologies to ensure reliability and reproducibility.