Smashing Discoveries

April 29, 2020

Using computers housed at the MGHPCC, UMass Dartmouth graduate student develops new model to aid gravitational wave discoveries.

Reporting by Helen Hill for MGHPCC
Gravitational waves, ‘ripples in the fabric of space-time’ predicted by Einstein over a century ago, were directly observed by the US LIGO detector in 2015 — a major discovery that resulted in the founders of the project receiving the 2017 Nobel Prize in Physics. Now, LIGO routinely makes multiple detections of these waves every month, thus opening a new window onto fascinating astrophysical objects like black holes and neutron stars.
Gravitational waves are produced when two black holes or neutron stars violently smash into each other, causing a major disturbance in space-time.  To reliably search for them in their data, LIGO scientists need to know what the gravitational wave signal should look like.
Nur Rifat, currently a masters student in the Department of Physics, at UMass Dartmouth works on computational models used to simulate so-called “template” signals from systems like binary neutron stars or black holes using Einstein’s General Relativity theory.
Working with UMass Dartmouth researchers Gaurav Khanna and  Scott Field together with Vijay Varma at CalTech, Rifat has recently developed a new model that will help extend the range of the black hole binary systems that LIGO can reliably search for. The model was recently published as a Rapid Communication in Physical Review.
“Until now, it was difficult to make predictions of what the gravitational wave signal from the merger of two black holes would look like, especially if one was much larger than the other,” Khanna explains. “Rifat’s new model allows scientists to generate precise template signals from such systems at an impressively fast rate.”
“MGHPCC systems (in particular, the UMass Shared Cluster and MIT’s Satori) were used to run hundreds of GPU-accelerated simulations of black hole systems; data that was used as the input to “train” the new model for speed and accuracy,” says Khanna.
This research was supported by the National Science Foundation (NSF), UMass Dartmouth’s Center for Scientific Computing & Visualization Research (CSCVR) and the Massachusetts Green High-Performance Computing Center (MGHPCC).
Story image courtesy: LIGO

About the Researcher

Nur Rifat

Nur Rifat a graduate student at UMass Dartmouth interested in Quantum Computing and Numerical Relativity. His advisor is Guarav Khanna a Professor of Physics, and the Co-Director of the Center for Scientific Computing at the University of Massachusetts Dartmouth


Nur E. M. Rifat, Scott E. Field, Gaurav Khanna, and Vijay Varma (2020), Surrogate model for gravitational wave signals from comparable and large-mass-ratio black hole binaries, Phys. Rev. D, doi: 10.1103/PhysRevD.101.081502

Related Links

UMass Shared Cluster
MIT Satori
UMass Dartmouth’s Center for Scientific Computing & Visualization Research

Research projects

Dusty With a Chance of Star Formation
Checking the Medicine Cabinet to Interrupt COVID-19 at the Molecular Level
Not Too Hot, Not Too Cold But Still, Is It Just Right?​
Smashing Discoveries​
Microbiome Pattern Hunting
Modeling the Air we Breathe
Exploring Phytoplankton Diversity
The Computer Will See You Now
Computing the Toll of Trapped Diamondback Terrapins
Edging Towards a Greener Future
Physics-driven Drug Discovery
Modeling Plasma-Surface Interactions
Sensing Subduction Zones
Neural Networks & Earthquakes
Small Stars, Smaller Planets, Big Computing
Data Visualization using Climate Reanalyzer
Getting to Grips with Glassy Materials
Modeling Molecular Engines
Forest Mapping: When the Budworms come to Dinner
Exploring Thermoelectric Behavior at the Nanoscale
The Trickiness of Talking to Computers
A Genomic Take on Geobiology
From Grass to Gas
Teaching Computers to Identify Odors
From Games to Brains
The Trouble with Turbulence
A New Twist
A Little Bit of This… A Little Bit of That..
Looking Like an Alien!
Locking Up Computing
Modeling Supernovae
Sound Solution
Lessons in a Virtual Test Tube​
Crack Computing
Automated Real-time Medical Imaging Analysis
Towards a Smarter Greener Grid
Heading Off Head Blight
Organic Light-Harvesting Antennae
Art and AI
Excited by Photons
Tapping into an Ocean of Data
Computing Global Change
Star Power
Engineering the Human Microbiome
Computing Social Capital
Computers Diagnosing Disease
All Research Projects

Collaborative projects

ALL Collaborative PROJECTS

Outreach & Education Projects

See ALL Scholarships
100 Bigelow Street, Holyoke, MA 01040