Computation and Data topics include computer simulations and utilization of Big Data from astronomical surveys to tackle today's largest astrophysical problems.
We are developing and supporting open community software for relativistic astrophysics that takes advantage of emerging petascale computers and advanced cyberinfrastructure. The toolkit combines a core set of components needed to simulate astrophysical objects such as black holes, compact objects, and collapsing stars, as well as a full suite of analysis tools.
Deep Learning, i.e, machine learning, based on deep artificial neural networks, is one of the fastest growing fields of artificial intelligence (AI) research today. We are applying deep learning with artificial neural networks, in combination with HPC numerical relativity simulations, in a variety of multimessenger astrophysics applications. Our current focus is on signal processing for gravitational wave detectors (LIGO, VIRGO, NANOGrav), analyzing data from telescopes (DES, LSST), and modeling waveforms from gravitational wave sources using algorithms that learn from numerical relativity simulations. This allows for real-time detection and parameter estimation of gravitational wave signals in LIGO, for denoising LIGO data contaminated with non-Gaussian noise, and for classification and unsupervised clustering of glitches (anomalies) in the LIGO detectors. We are now also developing fast automated transient search algorithms based on deep learning using raw image data from telescopes (e.g., DES and LSST) to rapidly classify electromagnetic counterparts to gravitational wave events.
Adaptive mesh refinement
We participate in the development and use of adaptive mesh refinement (AMR) techniques for astrophysical hydrodynamics simulations in the FLASH and Nyx codes. FLASH is a widely used, freely available package employed for simulations ranging from core-collapse supernovae and high-energy laser experiments to galaxy cluster evolution and large-scale structure. Nyx is a publicly available cosmological simulation code originally developed for simulations of the Lyman alpha forest. Our current development efforts focus on new physics solvers for these codes and new sub-resolution modeling techniques to better incorporate physical effects due to unresolved scales.
Faculty Interested in Computation and Data
|Numerical Relativity; High-Performance Computing; Gravitational Wave Astrophysics; STEM Education|
|Black Holes; Formation of the Moon; Planet Formation; Star Formation; Cosmic-ray Transport; Interstellar Turbulence|
|analytical and numerical relativity; machine and deep learning; multimessenger astrophysics|
|Survey Astronomy and Data Science; Gravitational Lensing; Theory of Interferometry; Astrophysical Masers|
|Astronomical Survey and Data Science; Origin and Cosmic Evolution of Galaxies and Galactic Nuclei; The Nature of Black Holes and Gravity|
|Observational Cosmology; Clusters of Galaxies and Sunyaev-Zeldovich Effect; Large Surveys; Data Analysis Pipelines for Surveys; Algorithms for Data Mining; Galaxy Formation and Evolution|
|Cosmic Magnetic Fields; Formulation of Theory of Star Formation Accounting for Role of Magnetic Fields; Astrophysical Analytical and Numerical Magnetohydrodynamics; Diffuse Matter Astrophysics|
|Computational Astrophysics; Cosmological Structure Formation; Clusters of Galaxies; Binary Stars; Supernovae|
|Multimessenger Astronomy; Numerical Relativity; Gravitational Waves; Scientific Computing; Data Science|
|General Relativity; Numerical Relativity; Gravitational Wave Astrophysics; Computational Magnetohydrodynamics and Stellar Dynamics; Cosmology|
|Observational Cosmology; Quasars and Active Galactic Nuclei; Galaxy Formation and Evolution; Surveys and Time-Domain Science|
|Formation of the First Stars and Galaxies; Primordial Chemistry; High-performance Computing and Computational Simulations; Analysis and Visualization of Astrophysical Data|
|Cosmology; Extragalactic Surveys; Galaxy Evolution; Instrumentation; Observation|