# Bbh Host Identification Eval

> bbh-host-identification-eval

- Skill: `qhjqhj00/bbh-host-identification-eval` (Agent Skill)
- Install (CLI): `npx skillmds@latest add qhjqhj00/bbh-host-identification-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/bbh-host-identification-eval/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/qhjqhj00/bbh-host-identification-eval

---


# bbh-host-identification-eval

> Identifying Host Galaxies of Binary Black Hole Mergers with Next-Generation Gravitational Wave Detector Networks — Biswas et al. (2026) (arXiv:2602.08459, 2026)

## What this evaluates

Evaluates the feasibility of identifying host galaxies for binary black hole mergers using next-generation gravitational wave detector networks by comparing estimated localization volumes against theoretical stellar mass and metallicity thresholds.

## Datasets

- **Grid I: Galaxy Catalogue Injections** — total ?; splits: test (-1)
- **Grid II: Maximum & Minimum Sky Sensitivity Injections** — total ?; splits: test (-1)

## Metrics

- `localization_volume` **(primary)** — range: other
  - Comoving volume containing 50% or 90% of the posterior probability distribution ($V_{50}$, $V_{90}$), computed via Fisher Information Matrix parameter estimation from injected signals.
- `chance_alignment_probability` — range: [0, 1]
  - Probability that a random galaxy falls within the localization volume, used as a diagnostic for unique host association.
- `mass_fraction` — range: other
  - Ratio of the localization volume to the minimum comoving volume required to contain one galaxy of a given stellar mass or metallicity threshold.

## Input / output format

**Input**: Injected binary black hole parameters (component masses, luminosity distance, sky location) and gravitational wave detector network configuration (e.g., HLVKIEC, EC, HLV).

**Output**: Estimated 50% and 90% credible localization volumes ($V_{50}$, $V_{90}$), theoretical and observed mass fractions, and chance alignment probability ($p_c$).

## Scoring recipe

```python
# Compute localization volumes from Fisher matrix
V_50, V_90 = compute_credible_volumes(fisher_matrix, posterior_samples)
# Compute chance alignment probability from volume and galaxy density
p_c = 1 - exp(-lambda_gal * V_90)
# Compute mass fraction relative to theoretical threshold
M_frac = V_90 / V_min(stellar_mass_threshold)
return V_50, V_90, p_c, M_frac
```

## Common pitfalls

- Localization volumes strongly depend on binary total mass and SNR, not just distance; low-mass binaries at large distances yield larger volumes despite fixed distance.
- Theoretical volume thresholds ($V_{\mathrm{min}}$, $V_{\mathrm{min}}^{Z}$) assume average galaxy distributions and do not account for local cosmic variance or specific catalogue incompleteness.
- Chance alignment probability ($p_c$) varies significantly with sky position due to detector antenna pattern sensitivity, requiring separate evaluation for maximum and minimum sensitivity regions.

## Evidence (verbatim from paper)

> Figure 5 presents the 3D localization volumes, quantified by the $V_{50}$ and $V_{90}$ credible regions, for each BBH injection in Grid I (Table 2), as inferred for the three GW detector networks we consider: HLVKIEC, HLV, and EC.

## Citation

```bibtex
@misc{biswas2026identifying,
  title={Identifying Host Galaxies of Binary Black Hole Mergers with Next-Generation Gravitational Wave Detector Networks},
  author={Biswas et al. (2026)},
  year={2026},
  note={arXiv:2602.08459}
}
```

- arXiv: 2602.08459