PHY: Accelerated Always-On Fully-Coherent Network Analysis for Gravitational Wave Searches

The University Of Texas Rio Grande Valley, Edinburg TX

Investigators

Abstract

The direct detection of Gravitational Waves (GWs) by the LIGO detectors in 2015 marks the dawn of a new era in astronomy. Now, a completely new way of observing the Universe, especially extreme phenomena such as the merger of black holes, is available to scientists that will bring revolutionary insights in astrophysics and fundamental Physics. The detection of gravitational waves is enabled by advances in instrumentation as well as the computer algorithms used to analyze the copious amounts of data produced by them. The degree to which a data analysis method can dig into the highly noisy data to reliably extract an astrophysical signal -- its sensitivity -- underlines the science that can be done with GW detectors. One of the most challenging data analysis problem at present is the implementation of the optimal method, namely, fully-coherent all-sky (FCAS) search, for analyzing data from the worldwide network of GW detectors. While statistical theory suggests that this method has the highest sensitivity, its high computational cost limits it at present to a sporadic rather than always-on mode of use. Under this project, a fast FCAS search will be implemented that will overcome this computational barrier. This will allow data from a detector network to be analyzed in its entirety with the highest achievable sensitivity. Implementing this project in a frontier area of science at the University of Texas Rio Grande Valley (UTRGV), a minority serving institution, will have far-reaching broader impacts in terms of attracting students from under-represented groups to STEM. Through this project, students will acquire advanced data analysis and computing skills that are transferable across a wide variety of careers and relevant to national needs. There are two main ingredients involved in the acceleration of the FCAS search: a nature-inspired optimization algorithm called Particle Swarm Optimization (PSO), and a massively parallel implementation using Graphics Processing Units (GPUs). For the latter, the PI will use a GPU cluster under acquisition that is partially supported by an NSF-MRI grant. PSO is modeled on the behavior of a bird flock trying to find the best source of food. Here, it is being used to find the best-fit GW signal to the data. PSO by itself leads to a 10-fold reduction in the computational cost of an FCAS search while GPU acceleration provides an additional similar factor in speedup. A key aspect of the project will be the development of novel vetoes for rejecting instrumental non-GW signals ("glitches") that dominate the background rate of false alarms. These vetoes are enabled by coherent network data analysis and the exploration of unphysical sectors of signal parameter space by PSO. In addition, novel data conditioning approaches will be used and tested in the search, such as an adaptive spline fit based glitch estimation and subtraction algorithm. The combination of an intrinsically more sensitive method and better vetoes could potentially uncover new signals in open GW data and confirm marginal events. This will create a more complete sample of the CBC source population that will help astrophysicists achieve a better understanding of the formation channels for the unusual binary systems that are being discovered. New sectors of signal parameter space, such as binaries with sub-solar mass components, can be efficiently explored with the accelerated code and tighter rate constraints will be set in the absence of detections. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.