Computation & Data

Computation and Data topics include computer simulations and utilization of Big Data from astronomical surveys to tackle today's largest astrophysical problems.

Research Highlights 

Simulation of the binary black hole merger GW150914.
Simulation of the binary black hole merger GW150914.

Einstein Toolkit

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. 

Links to research groups and facilities: Gabrielle AllenEliu HuertaEd Seidel, NCSA Gravity GroupEinstein Toolkit

Deep Learning

Deep Learning: Gravity Wave Detection
Examples of glitches classified by our deep learning method. This allows better characterization of gravitational wave detectors algorithms.   (From George, Shen, &  Huerta 2017)

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.

Links to research groups and facilities: Gabrielle AllenEliu HuertaEd Seidel, NCSA Gravity GroupLIGONanoGrav,  XSEDEBlue Waters

Adaptive mesh refinement

AMR simulation
Example of AMR simulation of a common envelope binary system from Ricker and Taam 2012, ApJ, 746, 74

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.

Links to research groups and facilities: Paul Ricker, FLASH,  NyxXSEDE Blue Waters

Faculty Interested in Computation and Data

Name Research Interests
Numerical Relativity; High-Performance Computing; Gravitational Wave Astrophysics; STEM Education
Cosmology, extragalactic astronomy, machine and deep learning, especially in large scale structure, galaxy formation and evolution, environmental dependence of galaxy properties and photometric redshift estimation.
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
,Time Domain,Data Science, Astrostatistics, Supernovae
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