Advancements in the GravAD Pipeline: Template Reduction and Testing Simulated Signals for Black Hole Detection

TL;DR

This paper presents improvements to the GravAD pipeline, including template reduction and simulated signals, which enhance gravitational wave detection efficiency, accuracy, and resource optimization.

Contribution

The paper introduces a more efficient GravAD pipeline with template reduction, simulated signals, and adaptive termination, improving detection speed and resource management.

Findings

Increased detection accuracy and efficiency.

Reduced computational resource usage.

Enhanced adaptive termination procedures.

Abstract

This paper introduces significant improvements to the GravAD pipeline, a Python-based system for gravitational wave detection. These advancements include a reduction in waveform templates, implementation of simulated signals, and optimisation techniques. By integrating these advancements, GravAD exhibits increased performance, efficiency, and accuracy in processing gravitational wave data. This leads to more efficient detection and freeing computational resources for further research. This pipeline also applies adaptive termination procedures for resource optimisation, enhancing gravitational wave detection speed and precision. The paper emphasises the importance of robust, efficient tools in gravitational wave data analysis, particularly given the finite nature of computational resources. Acknowledging system limitations such as dependency on the ripple python library capabilities and suggests future enhancements in waveform generation and differentiation.