Project Information

astrophysics, conda, cuda, gpu, parallelization, python
Project Status: Complete
Project Region: CAREERS
Submitted By: Gaurav Khanna
Project Email: robcoyne@uri.edu
Project Institution: University of Rhode Island
Anchor Institution: CR-University of Rhode Island

Mentors: Rob Coyne, Gaurav Khanna
Students: Christopher Nadeau

Project Description

Despite the groundbreaking detection of gravitational waves (GWs) by the ground-based LIGO and Virgo interferometers, there remain many open questions in the burgeoning field of gravitational wave astronomy. In particular, the nature of the compact remnant of binary neutron star (NS) mergers that drive gamma-ray bursts (GRBs) is a subject of continued debate, and a key question left open by the multi-messenger observations of GW170817 and GRB170817A. While GRB afterglow observations have suggested that long-lived magnetized NSs (magnetars) may form in at least some binary NS mergers, direct proof is still missing. Direct observation of GWs from a GRB merger remnant could go a long way toward settling this uncertainty, but existing analysis techniques are not suited to detecting such signals.

Historically, searches for GWs have fallen into two coarsely defined categories: those that leverage accurate modeling to produce searches that are extremely sensitive, and those that require minimal assumptions about the nature of the signal enabling searches that are highly robust. Most GRB remnant emission mechanisms are too complex to model with sufficient accuracy for our most sensitive searches, yet the GWs they produce are too weak to be detected by the more robust, unmodeled techniques. To bridge the gap, a generalized data analysis pipeline dubbed the Cross-Correlation Algorithm (CoCoA) has been developed.

CoCoA is a tunable GW analysis technique that can leverage source modeling to improve the sensitivity of a search, without sacrificing robustness against deviations from the expected waveform. At one extreme, CoCoA can function with minimal signal assumptions and produce results comparable to traditional unmodeled searches. At the other extreme, with a sufficiently accurate signal model, CoCoA approaches our most sensitive modeled searches. But unlike traditional techniques, CoCoA is able to leverage partial modeling to span the gap between these two extremes. The results are impressive, with preliminary estimates resulting in a factor of ~4 improvement over the sensitivity of comparable GRB remnant searches.

Due to the vastness of the physical parameter space over which we must search, and the dependence on a discrete bank of template waveforms, CoCoA is computationally expensive. As a result it is well-suited to deployment in a highly-parallelized distributed computing environment. It is already constructed to run on “typical” high-performance computing (HPC) clusters, though it is currently limited to execution on standard CPUs. But there are several aspects of CoCoA’s core functionality that are well-suited for deployment on graphics processing units (GPUs), which are becoming increasingly essential to modern HPC platforms.

The goals of this project are three-fold: update CoCoA’s codebase for compatibility with the most recent versions of Python, run CoCoA on a medium-scale HPC resource and then port core components for deployment on GPUs.

Additional Resources

Launch Presentation:
Wrap Presentation: 6