| # Dataset Card for FAMA Downstream Evaluation Datasets | |
| This repository contains three benchmark datasets used in the evaluation of the **FAMA (Foundational Astronomical Masked Autoencoder)** model across heterogeneous astronomical tasks. These include two galaxy morphology classification datasets and one photometric redshift regression dataset. | |
| ## 1. `galaxy-desi`: Galaxy Morphology Classification (In-Distribution) | |
| ### Description | |
| The `galaxy-desi` dataset is a curated collection of galaxy images extracted from the **DESI Legacy Imaging Surveys Data Release 9 (DR9)**. It serves as the primary in-distribution benchmark for fine-grained galaxy morphology classification. | |
| Each image is a 3-band (g, r, z) cutout of size 256×256 pixels with a pixel scale of 0.262 arcseconds per pixel, centered on the galaxy’s celestial coordinates. Labels are derived from **Galaxy Zoo 2** crowdsourced classifications, filtered using confidence thresholds and quality protocols. | |
| ### Task Type | |
| Image classification into **8 morphological classes**. | |
| ### Total Samples | |
| 70,132 labeled galaxy images. | |
| ### Class Distribution | |
| - Round elliptical: 12,321 samples | |
| - In-between elliptical: 12,193 samples | |
| - Cigar-shaped elliptical: 12,130 samples | |
| - Edge-on: 6,282 samples | |
| - Barred spirals: 4,090 samples | |
| - Unbarred spirals: 12,060 samples | |
| - Irregular (without merger): 6,738 samples | |
| - Merger: 4,318 samples | |
| The dataset is slightly imbalanced, with elliptical and spiral types being more prevalent than mergers or barred spirals. | |
| --- | |
| ## 2. `galaxy-sdss`: Galaxy Morphology Classification (Out-of-Distribution) | |
| ### Description | |
| The `galaxy-sdss` dataset comprises galaxy images from the **Sloan Digital Sky Survey (SDSS)** and is used as an **out-of-distribution (OOD)** testbed to evaluate model generalization across different survey instruments and data distributions. | |
| Images are labeled into five simplified morphological categories based on Galaxy Zoo–derived thresholds. The dataset follows the protocol of [Cheng et al., 2020] and uses the same train/test split as the original study. | |
| ### Task Type | |
| Image classification into **5 coarse morphological classes**. | |
| ### Total Samples | |
| 28,793 images (23,037 training + 5,754 test). | |
| ### Class Distribution (Training Set) | |
| - Completely round smooth: 6,749 samples | |
| - In-between smooth: 6,456 samples | |
| - Cigar-shaped smooth: 464 samples | |
| - Spiral: 6,245 samples | |
| - Edge-on: 3,123 samples | |
| ### Class Distribution (Test Set) | |
| - Completely round smooth: 1,687 samples | |
| - In-between smooth: 1,612 samples | |
| - Cigar-shaped smooth: 115 samples | |
| - Spiral: 1,560 samples | |
| - Edge-on: 780 samples | |
| Note the extreme rarity of cigar-shaped galaxies in this dataset, especially in the test set. | |
| --- | |
| ## 3. `photo-z-sdss`: Photometric Redshift Estimation | |
| ### Description | |
| This dataset is constructed from the **SDSS Data Release 12 Main Galaxy Sample** via the CasJobs interface. It is designed for **photometric redshift regression**, a critical task in cosmology. | |
| For each galaxy, multi-band (u, g, r, i, z) cutouts of size 300×300 pixels (0.396 arcsec/pixel) are retrieved from the SDSS Science Archive Server using `astroquery`. Only galaxies with reliable spectroscopic redshifts (`ZWARNING = 0`), r-band dereddened Petrosian magnitude < 17.77, and redshift in the range 0.01 < z < 0.3 are included. | |
| ### Task Type | |
| Regression (predicting continuous redshift value from multi-band images). | |
| ### Total Samples | |
| 50,896 galaxies. | |
| ### Split | |
| - Training set: 10,100 samples | |
| - Test set: 40,796 samples | |
| The split is performed using **redshift-stratified sampling** to ensure consistent redshift distributions between training and test sets. | |
| ## 4. `lens-detection`: Gravitational Lens Detection (Object Detection) | |
| ### Description | |
| This dataset is designed for **strong gravitational lens detection** in wide-field survey images. It likely consists of image cutouts from surveys such as DESI, LSST, or the Kilo-Degree Survey (KiDS), annotated with bounding boxes around candidate lens systems (e.g., Einstein rings, arcs). | |
| The dataset supports the **object detection** downstream task evaluated in the FAMA paper, where the model demonstrated significant gains over supervised baselines. | |
| ### Task Type | |
| Object detection (localization + binary classification: lens vs. non-lens). | |