# Search strategies for long gravitational-wave transients: hidden Markov model tracking and seedless clustering

## TL;DR

This paper compares hidden Markov model tracking and seedless clustering algorithms for detecting long-transient gravitational waves, highlighting their performance, sensitivity, and computational efficiency in unmodeled searches.

## Contribution

It introduces a comparative analysis of two unmodeled search methods for long-duration gravitational-wave signals, emphasizing the advantages of hidden Markov models.

## Key findings

- Hidden Markov model tracking performs well for low SNR signals.
- HMM tracking can outperform seedless clustering in certain scenarios.
- HMM tracking is more computationally efficient than seedless clustering.

## Abstract

A number of detections have been made in the past few years of gravitational waves from compact binary coalescences. While there exist well-understood waveform models for signals from compact binary coalescences, many sources of gravitational waves are not well modeled, including potential long-transient signals from a binary neutron star post-merger remnant. Searching for these sources requires robust detection algorithms that make minimal assumptions about any potential signals. In this paper, we compare two unmodeled search schemes for long-transient gravitational waves, operating on cross-power spectrograms. One is an efficient algorithm first implemented for continuous wave searches, based on a hidden Markov model. The other is a seedless clustering method, which has been used in transient gravitational wave analysis in the past. We quantify the performance of both algorithms, including sensitivity and computational cost, by simulating synthetic signals with a special focus on sources like binary neutron star post-merger remnants. We demonstrate that the hidden Markov model tracking is a good option in model-agnostic searches for low signal-to-noise ratio signals. We also show that it can outperform the seedless method for certain categories of signals while also being computationally more efficient.