---
license: apache-2.0
pretty_name: Astro Sky Image VQA
language:
  - en
task_categories:
  - visual-question-answering
  - image-to-text
tags:
  - astronomy
  - scientific-figures
  - chart-understanding
  - synthetic
  - matplotlib
  - document-understanding
size_categories:
  - 1K<n<10K
viewer: false
---

# Astro Sky Image VQA

A visual-question-answering benchmark of **synthetic astronomical figures** with
exact, machine-generated ground truth.

Alongside the figures, the repo ships the **raw responses of three
vision-language models** on a 150-figure subset, run twice: once on the clean
figures and once on the same figures artificially aged to look like a scanned
page from a mid-century journal.


---

## At a glance

| | |
|---|---|
| Figures | **2,001** (1 panel each) |
| Categories | 667 `contour` / 667 `sky-gmm` / 667 `sky-real` — exactly balanced |
| Questions | **47,357** (21 per contour figure, 25 per sky figure) |
| Images | 2,001 JPEG, RGB, ~2000–2700 px wide |
| Model responses | 3 models x 150 figures x 2 conditions = **21,300** answered questions |
| Ground truth | Generator-exact, not human-annotated |
| Download size | ~8 GB of data |
| License | Apache 2.0 (see [Provenance](#provenance-and-attribution) for the sky cutouts) |

Each figure's metadata includes precise bounding boxes and location of all data points:

<table style="width: 75%; border-collapse: collapse; border: none;">
  <tr style="border: none;">
    <td style="width: 50%; padding: 5px; border: none;">
      <img src="https://huggingface.co/datasets/ReadingTimeMachine/astro_sky_image_vqa/resolve/main/example_data/imgs/Picture_200601.jpeg" alt="First Image Description" style="width: 100%; height: auto;">
    </td>
    <td style="width: 50%; padding: 5px; border: none;">
      <img src="https://huggingface.co/datasets/ReadingTimeMachine/astro_sky_image_vqa/resolve/main/example_data/diags/Picture_200601.jpeg" alt="Second Image Description" style="width: 100%; height: auto;">
    </td>
  </tr>
</table>

For the 150 figures sent to LMMs, there are aged versions as well:

<table style="width: 75%; border-collapse: collapse; border: none;">
  <tr style="border: none;">
    <td style="width: 50%; padding: 5px; border: none;">
      <img src="https://huggingface.co/datasets/ReadingTimeMachine/astro_sky_image_vqa/resolve/main/VQA_full/imgs/vqa_000004.jpeg" alt="First Image Description" style="width: 100%; height: auto;">
    </td>
    <td style="width: 50%; padding: 5px; border: none;">
      <img src="https://huggingface.co/datasets/ReadingTimeMachine/astro_sky_image_vqa/resolve/main/LMM_outputs_n150_archive_light/aged_imgs/vqa_000004.jpeg" alt="Second Image Description" style="width: 100%; height: auto;">
    </td>
  </tr>
</table>

---

## Repository layout

```
VQA_full/
  imgs/       vqa_NNNNNN.jpeg        2001 rendered figures
  qa_jsons/   vqa_NNNNNN_qa.json     2001 records: figure params + bounding
                                     boxes + underlying data + the questions

LMM_outputs_n150/                    RUN 1 -- the clean figures
  chatgpt_api/  vqa_NNNNNN_qa.pickle    150 per model: every question, with
  claude_haiku/                         the raw response the model gave
  gemini/

LMM_outputs_n150_archive_light/      RUN 2 -- the SAME 150 figures, aged
  aged_imgs/    vqa_NNNNNN.jpeg         the degraded images actually sent
  archive_manifest.json                 which effects fired, per figure
  chatgpt_api/  vqa_NNNNNN_qa.pickle    responses, as above
                vqa_NNNNNN_qa_archive.json   that figure's aging recipe
  claude_haiku/
  gemini/

example_data/                        3 illustrative figures shown twice: as
  imgs/      Picture_N00089.jpeg     rendered, and with every bounding box
  diags/     Picture_N00089.jpeg     drawn on in red, so the annotations can
                                     be checked by eye. Generator naming, NOT
                                     part of the 2001 indexed figures.
```

`example_data/diags/` is the quickest way to see what is annotated: each red
box in those images is one entry in the corresponding `plot0` record — the plot
area, every tick label, the axis labels, the colorbar, its ticks and its label.
The three examples are illustrative only; they predate the `vqa_NNNNNN`
numbering and are not part of the 2,001.

---

## Citation

<!-- TODO: replace with the WASP 2026 paper reference once it is public. -->

```bibtex
tbd
```

**If you publish work using the `sky-real` figures, you inherit the
acknowledgement obligations of the underlying surveys**, which are not waived
by this repo's Apache 2.0 license. In particular SkyView asks to be cited, and
the Digitized Sky Surveys — the largest single source here — carry their own
acknowledgement requirements from STScI and the originating plate collections.
Check the terms for the specific surveys in your subset; the metadata above
tells you which those are.

The Apache 2.0 license applies to the dataset as assembled: the synthetic
figures, the generated questions and ground truth, and the model responses.

---

## Generating code

The figures and questions are produced by the
[`SkyImagesWASP2026`](https://anonymous.4open.science/r/SkyImagesWASP2026-027E) repository.

---

# Extended Information

---

## The figures

Three categories, **667 of each**:

| Category | `plot0.type` | `plot0.distribution` | What it is |
|---|---|---|---|
| `contour` | `contour` | `random` / `linear` / `gmm` | A contour plot of synthetic data |
| `sky-gmm` | `image of the sky` | `gmm` | A **synthetic** sky image: a Gaussian mixture, deposited to sky coordinates |
| `sky-real` | `image of the sky` | `sky` | A **real** survey cutout, plotted the same way |

The last two are rendered identically and are
distinguishable only by the structure of the data itself. A model that answers
the provenance question correctly has to tell a real astronomical field from a
plausible-looking fake.

Everything else about each figure is randomised independently — plot style,
colormap, dpi, aspect ratio, font sizes, colorbar placement, tick formats,
label text, coordinate epoch. 


---

## The questions

Questions are organized by the conceptual level model developed by [Lundgard et. al 2022](https://arxiv.org/abs/2110.04406):

| Level | What it asks for | Examples |
|---|---|---|
| **Level 1** | Things *displayed on the figure* — readable directly off the page | plot style, colormap, axis limits, tick values, titles, coordinate epoch |
| **Level 2** | Things requiring *estimation from the rendered data* | min/max/median/mean of the colour axis, angular field width and height |
| **Level 3** | Things requiring *inference about how the data was made* | which distribution generated it; real sky vs. Gaussian mixture; pixel scale |

Level 4 requires domain-specific expertise and is not covered by this dataset.

### Full question inventory

**Asked of every figure (10 figure-level, Level 1):**

| Name | Question |
|---|---|
| `plot style` | What is the plot style used in this figure? |
| `colormap` | What is the colormap that was used in this figure? |
| `aspect ratio` | What is the aspect ratio of this figure? |
| `titles` | What is the title of the plot in this figure? |
| `xlabels` / `ylabels` | What is the x/y-axis title of the plot in this figure? |
| `xtick values` / `ytick values` | What are the values for each of the tick marks on the x/y-axis? |
| `plot types` | What is the plot type of the plot in this figure? *(open-ended)* |
| `plot types (list)` | Same question, but offering `[contour, image of the sky]` |

`plot types` is asked **twice on purpose** — once open-ended and once as a
multiple choice. Only the list variant measures recognition; the open-ended one
scores near zero for every model because they answer things like "astronomical
survey image", which never string-matches the ground truth. Score the list
variant unless you specifically want to measure phrasing.

**Contour figures only (11 plot-level):**

| Level | Name | Question |
|---|---|---|
| 1 | `image or lines (list)` | Is the contour plot drawn as an image, lines, or both? |
| 1 | `minimum/maximum x axis limit` | Lower/upper limit of the x **axis** (not of the data) |
| 1 | `minimum/maximum y axis limit` | Lower/upper limit of the y axis |
| 2 | `minimum/maximum/median/mean color` | Statistics of the colour-axis data |
| 3 | `distribution-color + list)` | Which distribution generated the colour data? `[random, linear, gaussian mixture model]` |
| 3 | `distribution-x/y + list)` | Same, for the x/y-plane |

**Sky figures only (15 plot-level):**

| Level | Name | Question |
|---|---|---|
| 1 | `epoch` | Which coordinate epoch is on the axis labels? (`"none"` if absent) |
| 1 | `finest unit right ascension` | Smallest unit on the RA ticks: `[hours, minutes, seconds]` |
| 1 | `finest unit declination` | Smallest unit on the Dec ticks: `[degrees, arcminutes, arcseconds]` |
| 1 | `minimum/maximum right ascension axis limit` | RA axis limits |
| 1 | `minimum/maximum declination axis limit` | Dec axis limits |
| 2 | `field width` / `field height` | Angular size of the sky region, along RA / Dec |
| 2 | `minimum/maximum/median/mean color` | Statistics of the colour-axis data |
| 3 | `distribution-image + list)` | `[gaussian mixture model, real image of the sky]` — **the provenance question** |
| 3 | `pixel scale` | Angular size one pixel spans, in arcseconds |

Totals: **21** questions per contour figure, **25** per sky figure —
`667 x 21 + 1334 x 25 = 47,357`.

---

## What a `qa_json` contains

Each file in `VQA_full/qa_jsons/` is a **JSON-encoded string**, so it takes two
decodes:

```python
import json
with open('VQA_full/qa_jsons/vqa_000001_qa.json') as f:
    record = json.loads(json.load(f))   # note: twice
record.keys()   # ['figure', 'plot0', 'VQA', 'vqa_id']
```

| Key | Contents |
|---|---|
| `figure` | Figure-wide render parameters: `dpi`, `figsize`, `aspect ratio`, `plot style`, `color map`, font sizes, `facecolor`, pixel dimensions |
| `plot0` | The single panel — its type, distribution, the underlying data, and **pixel bounding boxes** for the plot area, every tick label, axis labels, title, colorbar and colorbar ticks |
| `VQA` | The questions, as `VQA[level][kind][name]` |
| `vqa_id` | e.g. `"vqa_000001"` — matches the filename and the image |

Because `plot0` carries bounding boxes for every element, this doubles as
labelled training data for **figure-element detection**, not just VQA.

### Reading the questions — mind the nesting

The two question kinds nest differently, which is the single most common thing
to get wrong:

```python
VQA['Level 1']['Figure-level questions']['plot style']          # -> the entry
VQA['Level 2']['Plot-level questions']['mean color']['plot0']   # -> the entry
                                                     ^^^^^^^^
```

Plot-level questions are keyed **by panel** underneath the question name. Every
figure in this release has exactly one panel (`plot0`), so it is easy to miss —
and code that skips the panel level will silently return a dict instead of a
question. A safe accessor:

```python
def question_entries(node):
    """Yield (panel, entry) whether or not the question is per-panel."""
    if 'Q' in node:                 # figure-level: the entry itself
        yield None, node
    else:                           # plot-level: {panel: entry}
        for panel, entry in node.items():
            yield panel, entry
```

Each entry holds the prompt in assembled and component form:

| Field | Meaning |
|---|---|
| `Q` | The full assembled prompt: persona + context + question + format |
| `A` | **Ground truth**, from the generator. Always a `dict` here, e.g. `{"plot style": "seaborn-v0_8-muted"}` |
| `persona` | e.g. `"You are a helpful assistant that can analyze images."` |
| `context` | Framing, and the multiple-choice list when one is offered |
| `question` | The question alone |
| `format` | The requested JSON answer shape, e.g. `{"plot style":""}` |

The `format` field is worth using rather than hard-coding key names: it states
exactly which JSON key the model was asked to fill, so parsing can be driven
from the prompt instead of drifting from it.

---

## The model runs

Three low-tier vision models, the same 150 figures (50 per category,
`vqa_000001`–`vqa_000157`), asked all 25/21 of each figure's questions:

| Directory | Model id as run |
|---|---|
| `chatgpt_api/` | `gpt-5.4-nano-2026-03-17` |
| `claude_haiku/` | `claude-haiku-4-5` |
| `gemini/` | `gemini-3.5-flash-lite` |

Both runs use **identical figures and identical questions**; they differ only
in whether the image was aged before being sent. Any gap between them is an
effect of the degradation, not of the sample.

### Reading a response pickle

```python
import pickle
with open('LMM_outputs_n150/gemini/vqa_000001_qa.pickle', 'rb') as f:
    qa_list, model_id = pickle.load(f)

qa_list[0]['question']     # what was asked
qa_list[0]['A']            # ground truth
qa_list[0]['raw answer']   # what the model said, verbatim
```

Fields present for **every** model: `Q`, `A`, `Level`, `type`, `persona`,
`context`, `question`, `format`, `reasoning`, `prompt`, `Response`,
`Response String`, `raw answer`.

Provider-specific extras: `fac` (GPT); `usage` and `system prompt` (Claude,
Gemini); `Response (raw)` (Gemini).

> **Unpickling needs the vendor SDKs.** Each pickle stores the provider's own
> response object next to the text, so a plain `pickle.load` raises
> `ModuleNotFoundError` without `anthropic` / `google-genai` installed. If you
> only want the text — and the fields above are all plain strings — substitute
> a stub for unknown classes:
>
> ```python
> class _Stub:
>     def __init__(self, *a, **k): pass
>     def __setstate__(self, s): self.__dict__.update(s if isinstance(s, dict) else {})
>
> class TolerantUnpickler(pickle.Unpickler):
>     def find_class(self, module, name):
>         try:
>             return super().find_class(module, name)
>         except (ImportError, AttributeError):
>             return type(name, (_Stub,), {})
> ```

### The archive-light condition

`LMM_outputs_n150_archive_light/` asks the same questions of the same figures
after degrading each one to resemble a scanned page from an old journal —
ink bleed and mottling, paper colouring and texture, stains, folding, subtle
noise, JPEG artefacts, and a 2-in-3 chance of being converted to grayscale.

The aging is **reproducible and shared across models**: each figure's random
draw is seeded from its own id, so GPT, Claude and Gemini all saw the byte-identical
aged image. Comparing the three models would otherwise be confounded by each
having been shown a differently-damaged page.

`archive_manifest.json` records the draw for all 150 figures; the same record
is repeated per figure as `<id>_qa_archive.json`:

| Field | Meaning |
|---|---|
| `preset` | `"archive-light"` |
| `base_seed` / `seed` | Run-wide seed, and this figure's derived seed |
| `effects` | Which effects fired, in order |
| `effects_by_phase` | The same, split into `ink` / `paper` / `post` |
| `grayscale`, `gray_prob` | Whether this figure went gray, and with what probability |
| `page background` | RGB of the simulated paper |

The aged images themselves are in `aged_imgs/` — you do not need to re-run the
aging to reproduce the experiment.

---

## Known issues and caveats

Read these before reporting numbers.

- **Score the list variant of `plot types`.** The open-ended variant scores
  near zero for all models for phrasing reasons, not vision reasons. See above.
- **Two different "distribution" questions exist.** For contour figures,
  `distribution-color` and `distribution-x/y` over `[random, linear, gaussian
  mixture model]`. For sky figures, `distribution-image` over `[gaussian
  mixture model, real image of the sky]`. They are not comparable and should
  not be pooled — match on the full question text, not on the substring
  "underlying distribution".
- **Ground truth is shaped differently in the two file types.** In the released
  qa jsons `A` is *always* a `dict` (all 47,357 of them). In the run pickles it
  is a `dict` for figure-level questions but **unwrapped to a bare `str` or
  `float`** for plot-level ones. Each question is consistent in its own type,
  but code that reads both file types must handle both shapes.
- **Models rename the answer key.** Claude commonly answers `plot_types` when
  asked for `plot types`. Match keys up to spacing, underscores and case, or
  you will score a correct answer as a parse failure.
- **A small number of responses are unparsable** — about 0.14% (30 of 21,300),
  split between prose-instead-of-JSON and valid JSON containing an explicit
  `null` refusal. Decide deliberately whether those count as wrong or as
  excluded; the two failure modes arguably differ.
- **Level 2 answers are often wrong by orders of magnitude.** Bounded metrics
  (e.g. sSMAPE) saturate here. `log10(predicted/true)` is the more informative
  summary for the angular-size and colour-statistic questions.
- **The dataset viewer is disabled** (`viewer: false`). The JSON records are
  deeply nested rather than tabular, so the viewer cannot render them
  meaningfully.

---

## Provenance and attribution

The **`sky-real`** figures are plotted from real survey cutouts retrieved via
[NASA SkyView](https://skyview.gsfc.nasa.gov/). Across the 667 real-sky
figures: 659 distinct astronomical objects, drawn from 14 SkyView survey
categories — predominantly optical (`OtherOptical`, `Optical:DSS`), with
infrared (`IRAS`, `WISE`, `2MASS`, `AKARI`, `Planck`, `WMAP&COBE`, `UKIDSS`),
radio (`Radio:MHz`, `Radio:GHz`, `GLEAM`) and X-ray (`ROSATDiffuse`)
representative among them.

Each figure's `plot0.data['data params']['sky image params']` keeps the full
provenance: source `filename`, the original FITS `header`, `survey`,
`telescope`, `instrument`, `object`, `bunit`, and the ADS-style bibcode of the
paper the field was taken from (411 distinct source papers).