Evaluates the sky localization precision and detection sensitivity of gravitational-wave detector networks for multi-messenger follow-up of compact binary mergers. It quantifies how well a network can identify and pinpoint sources within a specific distance and localization area threshold. Use when the user has predictions and gold and needs to compute fractional follow-up metric.
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---
name: fractional-follow-up-metric
description: Evaluates the sky localization precision and detection sensitivity of gravitational-wave detector networks for multi-messenger follow-up of compact binary mergers. It quantifies how well a network can identify and pinpoint sources within a specific distance and localization area threshold. Use when the user has predictions and gold and needs to compute fractional follow-up metric.
metadata:
skill_kind: metric
source_arxiv: 2308.13103
bibtex_key: [REDACTED]
confidence: high
---
# fractional-follow-up-metric
> Multi-messenger astronomy with a Southern-Hemisphere gravitational-wave observatory — James W. Gardner et al. (arXiv:2308.13103, 2023)
## What this evaluates
Evaluates the sky localization precision and detection sensitivity of gravitational-wave detector networks for multi-messenger follow-up of compact binary mergers. It quantifies how well a network can identify and pinpoint sources within a specific distance and localization area threshold.
## Datasets
- **Simulated compact binary coalescence population (GWTC-3 rate)** — total ?; splits: simulation (-1)
## Metrics
- `fractional follow-up metric` **(primary)** — range: [0, 1]
- Fraction of simulated sources within 500 Mpc that have a network signal-to-noise ratio greater than 25 and are localized within a specified area threshold (10 deg² for loose, 1 deg² for tight).
## Input / output format
**Input**: Simulated gravitational-wave signals from compact binary mergers within 500 Mpc, detector network configurations, and sky localization uncertainties.
**Output**: Fraction of sources meeting the SNR and localization area thresholds, and expected joint observation rates (yr⁻¹).
## Scoring recipe
```python
def compute_metric(simulated_sources, snr_threshold=25, area_threshold=10.0):
count = 0
for source in simulated_sources:
if source.distance <= 500 and source.snr > snr_threshold and source.localization_area <= area_threshold:
count += 1
return count / len(simulated_sources)
```
## Common pitfalls
- Confusing arithmetic improvement (absolute difference in observation rates) with geometric improvement (multiplicative factor).
- Applying the metric outside the 500 Mpc distance bound or ignoring the SNR > 25 requirement.
- Assuming the metric is independent of detector duty cycles and network configurations.
## Evidence (verbatim from paper)
> Fractional follow-up metrics from Table 2, i.e., the fraction of sources within 500 Mpc that have a network signal-to-noise ratio greater than 25 and are localised within 10 deg^2 [panels (a) and (c)] or 1 deg^2 [panels (b) and (d)].
## Citation
```bibtex
@misc{gardner2023southern,
title={Multi-messenger astronomy with a Southern-Hemisphere gravitational-wave observatory},
author={James W. Gardner et al.},
year={2023},
note={arXiv:2308.13103}
}
```
- arXiv: 2308.13103