Sersic Fit Mock Eval

---
name: sersic-fit-mock-eval
description: Evaluates the accuracy and robustness of Sérsic profile fitting algorithms (GIM2D and GALFIT) on simulated HST/ACS galaxy images. It probes how well these codes recover true structural parameters under varying signal-to-noise ratios and surface brightness levels. Use when the user wants to benchmark on GEMS Bulge0001, GEMS Disk0001, or asks about evaluating this task. Reports magnitude_residual.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 1010.2352
  bibtex_key: hoyos2010hstacs
  confidence: high
---

# sersic-fit-mock-eval

> The HST/ACS Coma Cluster Survey III. Structural Parameters of Galaxies using single-S\'ersic Fits — Hoyos et al. (2010) (arXiv:1010.2352, 2010)

## What this evaluates

Evaluates the accuracy and robustness of Sérsic profile fitting algorithms (GIM2D and GALFIT) on simulated HST/ACS galaxy images. It probes how well these codes recover true structural parameters under varying signal-to-noise ratios and surface brightness levels.

## Datasets

- **GEMS Bulge0001** — total 558; splits: test (558)
- **GEMS Disk0001** — total 470; splits: test (470)

## Metrics

- `magnitude_residual` **(primary)** — range: mag
  - Fitted total magnitude minus the true simulated magnitude.
- `effective_radius_ratio` — range: ratio
  - Fitted effective radius divided by the true effective radius.
- `sersic_index_error` — range: index
  - Fitted Sérsic index minus the true Sérsic index.

## Input / output format

**Input**: Simulated HST/ACS images containing artificial galaxies with known pure Sérsic profiles (n=1 or n=4), embedded in Poisson noise and real sky background patches.

**Output**: Fitted structural parameters: total magnitude, effective radius, Sérsic index, and sky background level.

## Scoring recipe

```python
# For each simulated galaxy:
true_mag, true_re, true_n = get_true_parameters(simulation)
fit_mag, fit_re, fit_n = get_fitted_parameters(model)
magnitude_residual = fit_mag - true_mag
re_ratio = fit_re / true_re
n_error = fit_n - true_n
# Aggregate across dataset:
scatter_mag = std(magnitude_residual)
scatter_re = std(re_ratio)
# Analyze trends vs true surface brightness
```

## Common pitfalls

- Inaccurate sky determination is the dominant source of parameter recovery errors.
- Default cutout sizes cause systematic offsets in magnitude and effective radius at faint surface brightness levels.
- Failing to mask overlapping sources increases scatter in fitted parameters.

## Evidence (verbatim from paper)

> The upper panel presents the magnitude residual (GIM2D minus Model), the middle panel presents the ratio in effective radii, and the lower panel presents the output Sérsic index as a function of the average surface brightness within a effective radius of the simulation.

## Citation

```bibtex
@misc{hoyos2010hstacs,
  title={The HST/ACS Coma Cluster Survey III. Structural Parameters of Galaxies using single-S\'ersic Fits},
  author={Hoyos et al. (2010)},
  year={2010},
  note={arXiv:1010.2352}
}
```

- arXiv: 1010.2352