Optical astronomy and cosmology

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Lawrence Livermore researchers leverage synoptic surveys, statistical modeling and machine learning, and high-performance computing to close gaps in our understanding of how the universe works.

Telescopes and data science

Astrophysicists strive to understand fundamental characteristics of the solar system, our galaxy, and the universe to push the frontiers of our understanding of physics and the origin and evolution of the cosmos. They use telescopes and theoretical and numerical modeling to resolve some of the most pressing unanswered questions in physics, such as the unknown nature of dark energy.

In this field, data science helps researchers catalog and interpret objects of astronomical origin orbiting Earth as well as process huge volumes of data captured by ground- and space-based telescopes. Research in these areas leverages the substantial computational resources available at LLNL to apply image processing and inference algorithms—such as Bayesian statistical methods and machine learning—to optical telescope images and simulations.

Bayesian and mathematical models
From left, Kerianne Pruett, Michael Schneider, Eddie Schlafly, and Bob Armstrong discuss Bayesian and mathematical models for data analysis.

Using these techniques, our researchers seek to:

Understand the nature of dark energy and dark matter using weak gravitational lensing of galaxies or investigating massive astrophysical compact halo objects (MACHOs)

Investigate Earth Trojan asteroids using image detection and orbit determination techniques

Explore adaptive optics capabilities and their applications for exoplanet discovery

Capabilities and collaborations for research

Remote observing room
Astronomers working in the remote observing room. From left: Will Dawson, Carmen Carrano, Bob Armstrong, and Ryan Dana.

Processing high volumes of astronomical survey data is a major computational feat and requires executing large-scale simulations and developing scientifically meaningful machine learning algorithms. LLNL houses world-ranking supercomputers and advanced high-performance computing infrastructure, making it possible for our scientists to carry out such computational feats. The requirement for computational resources, coupled with the many applications between time-domain astronomy and space security, make LLNL scientists uniquely situated to perform cosmology and astronomy research.

In addition to advanced computing solutions, strategic collaborations and partnerships allow us to carry out meaningful and far-reaching astronomy and cosmology research. LLNL researchers play a central role in the Rubin Observatory Legacy Survey of Space Time (LSST) data processing pipeline, including leading working groups within the LSST Dark Energy Science Collaboration. We are also active in several other astronomy survey collaborations, including the Zwicky Transient Facility, and the Hyper Suprime-Cam survey.

As a result of LLNL’s extensive collaborations and computational resources, our researchers have been able to:

Research for national security

The immense volumes of data coming from new and improved astronomical instruments has necessitated development of novel methods to realize the potential of these surveys. The novel data science techniques developed by LLNL scientists for astronomy are also helping to push forward national security capabilities in space domain awareness and applications of artificial intelligence to the breadth of LLNL missions.

Image gallery

People

Name Title Discipline
Team

Collaborators

California Institute of Technology

Dark Energy Survey (DES)

HRL Laboratories

Hyper Suprime-Cam survey (HSC)

LSST Dark Energy Science Collaboration (DESC)

SLAC National Accelerator Laboratory

Space Telescope Science Institute

University of Arizona

University of California, Berkeley

University of California, Davis

University of California, Riverside

University of Michigan

Zwicky Transient Facility (ZTF)

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