# The Fast and the Fiducial: Augmented kludge waveforms for detecting extreme-mass-ratio inspirals

## TL;DR

The paper introduces an improved augmented analytic kludge (AAK) waveform model for EMRIs, achieving high accuracy and speed, facilitating detection and data analysis for future space-based gravitational wave observatories like LISA.

## Contribution

The paper presents a new version of the AAK model with enhanced accuracy and efficiency, suitable for EMRI detection and data analysis in LISA mock data challenges.

## Key findings

- Waveform overlaps exceed 0.97 with fiducial models
- Waveform generation is 5-15 times faster than previous models
- Potential viability for EMRI detection with semi-coherent template banks

## Abstract

The extreme-mass-ratio inspirals (EMRIs) of stellar-mass compact objects into massive black holes are an important class of source for the future space-based gravitational-wave detector LISA. Detecting signals from EMRIs will require waveform models that are both accurate and computationally efficient. In this paper, we present the latest implementation of an augmented analytic kludge (AAK) model, publicly available at github.com/alvincjk/EMRI_Kludge_Suite as part of an EMRI waveform software suite. This version of the AAK model has improved accuracy compared to its predecessors, with two-month waveform overlaps against a more accurate fiducial model exceeding 0.97 for a generic range of sources; it also generates waveforms 5-15 times faster than the fiducial model. The AAK model is well suited for scoping out data analysis issues in the upcoming round of mock LISA data challenges. A simple analytic argument shows that it might even be viable for detecting EMRIs with LISA through a semi-coherent template bank method, while the use of the original analytic kludge in the same approach will result in around 90% fewer detections.