The New Swift/UVOT+MaNGA (SwiM) Value-added Catalog
Bibliographic record
Abstract
We present the the new Swift/UVOT+MaNGA (SwiM) catalog (SwiM_v4.1). SwiM_v4.1 is designed to study star-formation and dust attenuation within nearby galaxies given the unique overlap of Swift/UVOT near-ultraviolet (NUV) imaging and MaNGA integral field optical spectroscopy. SwiM_v4.1 comprises 559 objects, ~4 times more than the original SwiM catalog (SwiM_v3.1), spans a redshift range z~0.0002-0.1482, and provides a more diverse and rich sample. Approximately 5% of the final MaNGA sample is included in SwiM_v4.1, and 42% of the SwiM_v4.1 galaxies are cross-listed with other well-known catalogs. We present the same data as SwiM_v3.1, including UVOT images, SDSS images and MaNGA emission-line and spectral index maps with the same pixel size and angular resolution for each galaxy, and a file containing galaxy and observational properties. We designed SwiM_v4.1 to be unbiased, which resulted in some objects having low signal-to-noise ratios in their MaNGA or Swift data. We addressed this by providing a new file containing the fraction of science-ready pixels in each MaNGA emission-line map, and the integrated flux and inverse variance for all three NUV filters. The uniform angular resolution and sampling in SwiM_v4.1 will help answer a number of scientific questions, including constraining quenching and attenuation in the local Universe and studying the effects of black hole feedback. The galaxy maps, catalog files, and their associated data models are publicly released on the SDSS website: https://www.sdss4.org/dr17/data_access/value-added-catalogs/?vac_id=swift-manga-value-added-catalog.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.049 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".