A catalog to unite them all: REGALADE, a revised galaxy compilation for the advanced detector era
Bibliographic record
Abstract
Many applications in transient science, gravitational wave follow-up, and galaxy population studies require all-sky galaxy catalogs with reliable distances, extents, and stellar masses. However, existing catalogs often lack completeness beyond $\sim 100$ Mpc, suffer from stellar contamination, or do not provide homogeneous stellar mass estimates and size information. Our goal is to build a high-purity, high-completeness, all-sky galaxy catalog out to 2,000 Mpc, specifically designed to support time-domain and multi-messenger astrophysics. We combined major galaxy catalogs and deep imaging surveys -- including the Legacy Surveys, Pan-STARRS, DELVE, and SDSS -- and added spectroscopic, photometric, and redshift-independent distances. We cleaned the sample using the Gaia catalog to remove stars and visually inspected all ambiguous cases below 100 Mpc through a classification platform that gathered 27,000 expert votes. Stellar masses were estimated using optical and mid-infrared profile-fit photometry, and we improved the accuracy of photometric distances by combining multiple independent estimates. The resulting catalog, REGALADE, includes nearly 80 million galaxies with distances under 2,000 Mpc. It provides stellar masses for 88% of the sample and ellipse fits for 80%. REGALADE is more than 90% complete for galaxies contributing 50% of the total $r$-band luminosity out to 360 Mpc. In science tests, it recovers 60% more known supernova hosts, doubles the number of low-luminosity transient hosts, and identifies more reliable hosts for ultraluminous and hyper-luminous X-ray sources. REGALADE is one of the most complete and reliable all-sky galaxy catalog to date for the nearby Universe, built for real-world applications in transient and multi-messenger astrophysics. The full dataset, visual classifications, and code will be released to support broad community use.
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 | 0.002 | 0.005 |
| 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 | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.053 |
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".