# Gw Reconstruction Eval

> Evaluates the fidelity of model-agnostic Bayesian waveform reconstruction for unbound binary black hole fly-bys across different detector noise environments and frame function parameterizations. It also quantifies the astrophysical detection sensitivity and expected event rates for current and next-generation interferometers. Use when the user wants to benchmark on Simulated Hyperbolic BBH Encounters, or asks about evaluating this task. Reports overlap.

- Skill: `qhjqhj00/gw-reconstruction-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/gw-reconstruction-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/gw-reconstruction-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/gw-reconstruction-eval

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# gw-reconstruction-eval

> A Model-Independent Framework for Gravitational-Wave Reconstruction of Binary Black Hole Hyperbolic Encounters in Ground-Based Interferometers — Lott et al. (2025) (arXiv:2512.01476, 2025)

## What this evaluates

Evaluates the fidelity of model-agnostic Bayesian waveform reconstruction for unbound binary black hole fly-bys across different detector noise environments and frame function parameterizations. It also quantifies the astrophysical detection sensitivity and expected event rates for current and next-generation interferometers.

## Datasets

- **Simulated Hyperbolic BBH Encounters** — total ?; splits: test (-1)

## Metrics

- `overlap` **(primary)** — range: [0, 1]
  - Normalized inner product (Pearson correlation) between the injected gravitational waveform and the BayesWave reconstruction, reported as a posterior distribution.
- `SNR` — range: other
  - Signal-to-noise ratio of the recovered signal, inversely proportional to the luminosity distance.
- `detection rate` — range: other
  - Expected number of detectable events per year, calculated as the product of the astrophysical rate density and the redshifted sensitivity volume.

## Input / output format

**Input**: Simulated gravitational wave strain signals from hyperbolic encounters injected into idealized or realistic detector noise realizations (LIGO, A+, CE, ET).

**Output**: Reconstructed waveforms, overlap posterior distributions, recovered SNR, luminosity distance limits, and annual detection rates.

## Scoring recipe

```python
overlap = np.dot(injected_waveform, reconstructed_waveform) / (np.linalg.norm(injected_waveform) * np.linalg.norm(reconstructed_waveform))
is_detected = recovered_snr > 8
sensitivity_volume = 4 * np.pi * integrate((1+z) * dVc_dz, 0, z_max)
detection_rate = astrophysical_rate_density * sensitivity_volume
```

## Common pitfalls

- Assumes idealized noise and optimal sky orientation, which overestimates actual detectability and reconstruction fidelity.
- Ignores systematic errors and non-optimal detector orientations that introduce biases not captured under ideal conditions.

## Evidence (verbatim from paper)

> We declare that a detection occurs when the recovered signal SNR exceeds 8, consistent with other similar work [37]. ... Across the parameter space, all frame functions produce overlaps greater than 0.9, indicating they can all reconstruct the hyperbolic encounters well.

## Citation

```bibtex
@misc{lott2025modelindependent,
  title={A Model-Independent Framework for Gravitational-Wave Reconstruction of Binary Black Hole Hyperbolic Encounters in Ground-Based Interferometers},
  author={Lott et al. (2025)},
  year={2025},
  note={arXiv:2512.01476}
}
```

- arXiv: 2512.01476