# Deepgrav Gw Detection Eval

> Binary classification capability to distinguish background noise from gravitational-wave signals (specifically BBH and SGLF classes) in time-series data. It probes the model's ability to generalize to unseen gravitational wave anomalies using deep latent features. Use when the user wants to benchmark on HDR A3D3 gravitational-wave dataset, or asks about evaluating this task. Reports AUC.

- Skill: `qhjqhj00/deepgrav-gw-detection-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/deepgrav-gw-detection-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/deepgrav-gw-detection-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/deepgrav-gw-detection-eval

---


# deepgrav-gw-detection-eval

> DeepGrav: Anomalous Gravitational-Wave Detection Through Deep Latent Features — Jianqi Yan et al. (2025) (arXiv:2503.03799, 2025)

## What this evaluates

Binary classification capability to distinguish background noise from gravitational-wave signals (specifically BBH and SGLF classes) in time-series data. It probes the model's ability to generalize to unseen gravitational wave anomalies using deep latent features.

## Datasets

- **HDR A3D3 gravitational-wave dataset** — total ?; splits: train (-1), val (-1), test (-1); repo https://github.com/yan123yan/HDR-anomaly-challenge-submission

## Metrics

- `AUC` **(primary)** — range: [0, 1]
  - Area under the Receiver Operating Characteristic curve, measuring the trade-off between true positive rate and false positive rate across all classification thresholds.
- `accuracy` — range: [0, 1]
  - Fraction of correctly classified instances out of the total number of instances.

## Input / output format

**Input**: Raw time-series gravitational-wave signal data (background or signal class).

**Output**: Binary classification label (0 for background, 1 for signal) or predicted probability for the positive class.

## Scoring recipe

```python
def compute_auc(y_true, y_pred_proba):
    fpr, tpr, _ = roc_curve(y_true, y_pred_proba)
    return auc(fpr, tpr)

def compute_accuracy(y_true, y_pred_labels):
    return np.mean(y_true == y_pred_labels)
```

## Common pitfalls

- BBH and SGLF classes are merged into a single positive class; evaluating them separately would misrepresent the reported AUC/accuracy.
- Data augmentation (signal averaging) is applied only to the training set; applying it to validation/test sets would invalidate the reported performance metrics.
- The dataset splits are fixed at 70/10/20; ensure the test set is strictly held out and not used during augmentation or tuning.

## Evidence (verbatim from paper)

> The dataset is divided into three subsets: 70% for training, 10% for validation, and 20% for testing. ... All our models and experimental settings are available on GitHub. ... assessing metrics on the validation set, including the Receiver Operating Characteristic (ROC) curves and AUC.

## Citation

```bibtex
@misc{yan2025deepgrav,
  title={DeepGrav: Anomalous Gravitational-Wave Detection Through Deep Latent Features},
  author={Jianqi Yan et al. (2025)},
  year={2025},
  note={arXiv:2503.03799}
}
```

- arXiv: 2503.03799