🔭 astrophysics

A catalog to unite them all: REGALADE, a revised galaxy compilation for the advanced detector era

The paper introduces REGALADE, a high-purity, all-sky galaxy catalog containing nearly 80 million galaxies out to 2,000 Mpc with reliable distances, stellar masses, and size estimates, specifically designed to enhance transient science and multi-messenger astrophysics by significantly improving completeness and host identification compared to existing datasets.

Original authors: Hugo Tranin, Nadejda Blagorodnova, Marco A. Gómez-Muñoz, Maxime Wavasseur, Paul J. Groot, Lloyd Landsberg, Fiorenzo Stoppa, Steven Bloemen, Paul M. Vreeswijk, Daniëlle L. A. Pieterse, Jan van Roestel

Published 2026-02-18

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Hugo Tranin, Nadejda Blagorodnova, Marco A. Gómez-Muñoz, Maxime Wavasseur, Paul J. Groot, Lloyd Landsberg, Fiorenzo Stoppa, Steven Bloemen, Paul M. Vreeswijk, Daniëlle L. A. Pieterse, Jan van Roestel, Simone Scaringi, Sara Faris

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine the universe as a massive, bustling city. For decades, astronomers have been trying to build a complete phone book for this city—a catalog of every "neighborhood" (galaxy) within a certain distance, including their addresses, sizes, and how many people live there (stellar mass).

The problem? The old phone books were messy. Some were missing entire neighborhoods, others listed streetlights as houses, and many didn't have accurate addresses. This made it hard for scientists to find the "hosts" of cosmic events, like supernovae (exploding stars) or gravitational waves (ripples in space-time), because they didn't know exactly where to look.

Enter REGALADE. Think of this new catalog as the ultimate, super-accurate, all-sky "Master Phone Book" for the nearby universe, designed specifically for the era of advanced telescopes and detectors.

Here is the story of how they built it, explained simply:

1. The Great Collection (The Ingredients)

The team didn't just write a new list from scratch. Instead, they acted like master chefs gathering ingredients from the world's best restaurants. They took data from massive existing surveys (like SDSS, Pan-STARRS, and the Legacy Surveys) and combined them.

The Analogy: Imagine trying to find a specific house in a city. You might have a map from 1990, a GPS from 2010, and a street view from 2024. REGALADE merges all these maps into one perfect, up-to-date guide.

2. The Great Cleanup (Removing the Noise)

When you mix so many different lists together, you get a lot of garbage. The old lists were full of "stars" (which are just our neighbors in the Milky Way, not distant galaxies) and "ghosts" (image glitches or artifacts).

The Analogy: It's like cleaning a room where someone threw in a pile of laundry, a box of toys, and a few actual furniture pieces. The team used a "metal detector" (the Gaia satellite data) to find and remove the stars. They also used a "crowd-sourced inspection" where 21 human experts looked at 27,000 blurry images to decide: "Is this a real galaxy, or just a smudge?"
The Result: They started with a messy pile of 88 million entries and cleaned it down to a pristine list of nearly 80 million galaxies.

3. Filling in the Blanks (The Missing Data)

Old catalogs often had galaxies with missing information. Some had no size, some had no distance, and most had no idea how much "stuff" (mass) they contained.

The Analogy: Imagine a phone book where some entries just say "House," but don't say how big it is or how many people live there. REGALADE calculated the size (drawing an oval around each galaxy) and the mass (estimating the weight of the stars inside) for 88% of the entries.
The Distance Trick: To get the most accurate address, they didn't just rely on one method. If a galaxy had three different distance estimates, they averaged them out, throwing away the weird outliers. This made the "addresses" much more reliable.

4. Why Do We Need This? (The Real-World Use)

Why spend so much effort making a better phone book? Because astronomers are now "listening" to the universe in new ways, and they need to know exactly where to look.

Gravitational Waves (The Earthquake): When two black holes crash, they send out ripples. Detectors like LIGO can hear the crash, but they can't see where it happened. They get a "search area" the size of a large country. REGALADE tells them, "Hey, look at these 1,300 galaxies in that area, not just the 700 the old list had." This doubles their chances of finding the light from the crash.
Supernovae (The Fireworks): Every year, thousands of stars explode. To understand them, we need to know how bright they really are, which requires knowing their distance. REGALADE helps scientists instantly match an explosion to its home galaxy, even if that galaxy is faint and far away.
X-Ray Sources (The Hidden Monsters): Some galaxies hide super-bright X-ray sources. The old lists often confused these with nearby stars. REGALADE filters out the confusion, revealing new "monsters" hiding in the dark.

5. The Bottom Line

Before REGALADE, astronomers were trying to navigate the universe with a map that had missing streets and fake buildings.

REGALADE is the first all-sky map that goes deep (2,000 million light-years away), is incredibly clean (no fake stars), and is packed with details (size and mass).
It is 90% complete for the bright, massive galaxies up to 360 million light-years away.
It is designed to be the "gold standard" for the next generation of time-domain astronomy, helping scientists catch cosmic events faster and understand the universe better.

In short: REGALADE is the ultimate directory for the cosmic neighborhood, ensuring that when the universe makes a noise, we know exactly which house to knock on.

1. Problem Statement

Transient science (supernovae, kilonovae) and multi-messenger astronomy (gravitational wave follow-ups) require comprehensive, all-sky galaxy catalogs with reliable distances, physical extents, and stellar masses. Existing catalogs suffer from several critical limitations:

Incompleteness: Many catalogs lack coverage beyond $\sim100$ Mpc or miss low-surface-brightness and dwarf galaxies.
Contamination: High rates of stellar contamination and spurious artifacts, particularly near the Galactic plane.
Data Homogeneity: Lack of consistent stellar mass estimates, galaxy size (extent) data, and unified distance measurements across the sky.
Coverage Gaps: Major surveys like SDSS and DES cover only partial sky regions, leaving the Southern Hemisphere and low Galactic latitudes underrepresented.

2. Methodology

The authors constructed REGALADE (Revised Galaxy List for the Advanced Detector Era) by integrating, cleaning, and homogenizing data from 16 major input catalogs and deep imaging surveys.

A. Data Integration

The compilation combines curated catalogs (HECATE, GLADE/GLADE+, NED-LVS, CosmicFlows, SGA) with deep imaging surveys (Legacy Surveys DR9/DR10, Pan-STARRS, DESI DR1/PV, SDSS, DELVE, GSC).

Distance Limits: The catalog targets galaxies out to 2,000 Mpc ( $z \lesssim 0.37$ ), covering the sensitivity horizons of current and next-generation gravitational wave detectors (LIGO/Virgo/KAGRA) and transient surveys (LSST).
Cosmology: Distances assume a flat $\Lambda$ CDM cosmology ( $H_0 = 71$ km/s/Mpc, $\Omega_m = 0.27$ , $\Omega_\Lambda = 0.73$ ).

B. Cleaning and Contaminant Removal

A rigorous multi-step filtering process was applied to remove stars, artifacts, and spurious sources:

Orphan Removal: Sources appearing in only one catalog were flagged, especially if overlapping large galaxies or located in crowded Galactic fields ( $|b| < 15^\circ$ ).
Stellar Contamination: Utilized Gaia DR3 data (proper motion and $G-BP$ color) to separate stars from galaxies. Compact sources were removed unless they appeared in $\geq 4$ input catalogs or matched a known galaxy classification.
Visual Inspection: For the critical nearby volume ( $D < 100$ Mpc), a dedicated platform gathered 27,000 expert votes from 21 astronomers to classify 9,505 ambiguous candidates. This process refined constraints for removing distant outliers, stars, and artifacts.
Duplicate Handling: A variable-radius elliptical matching algorithm (accounting for galaxy size and position angle) was used to merge entries, prioritizing sources with spectroscopic redshifts and larger physical sizes.

C. Parameter Homogenization

Extents: Galaxy sizes (semimajor axis $R_1$ , semiminor axis $R_2$ , position angle $PA$) were recalibrated to a common system based on the SGA definition (isophotal radius at $\mu = 26$ mag arcsec $^{-2}$ ).
Distances: A priority ranking was established for distance sources (spectroscopic > redshift-independent > photometric). A trimmed mean of multiple independent distance estimates was calculated to improve accuracy.
Stellar Masses: Derived using a unified mass–light–color relation calibrated against the WH24 reference. Optical and mid-infrared (WISE W1) profile-fit photometry were used to estimate masses for 88% of the sample.

3. Key Contributions

Scale and Scope: A unified all-sky catalog of ~80 million galaxies ( $D < 2,000$ Mpc).
First of its Kind: The first all-sky catalog at this depth to systematically include galaxy extents (ellipse fits for 80% of the sample) and stellar masses (88% of the sample).
High Purity: Significant reduction in stellar contamination through Gaia cross-matching and visual vetting, particularly in the local universe ( $D < 100$ Mpc).
Improved Distance Accuracy: Demonstrated that combining multiple independent photometric redshifts significantly reduces scatter and outlier fractions compared to single-source estimates.
Open Science: The full dataset, visual classification labels, and analysis code are released via Zenodo, CDS, and GitHub.

4. Results

Completeness: REGALADE is >90% complete for galaxies contributing 50% of the total $r$ $r$ -band luminosity out to 360 Mpc.
- It shows a 55% increase in galaxy counts within 100 Mpc compared to HECATE and a 30% increase over GLADE1.
- Completeness drops near the Galactic plane ( $|b| < 20^\circ$ ) due to extinction but reaches ~100% at higher latitudes.
Transient Host Recovery:
- Supernovae: Recovers 60% more known supernova hosts than GLADE1 (15,101 vs. 9,469).
- Low-Luminosity Transients: Doubles the number of identified hosts for faint transients (e.g., BlackGEM discoveries), recovering 1,139 hosts vs. 507 in GLADE1.
Gravitational Wave (GW) Follow-up:
- For the event S250119cv, REGALADE identifies 1,385 host candidates within the 90% confidence region, nearly double the 759 found in GLADE+. These additional hosts are predominantly low-mass dwarf galaxies ( $M_* \sim 2 \times 10^9 M_\odot$ ).
X-ray Sources: Identifies 2.8 times more Hyper-Luminous X-ray (HLX) hosts than GLADE1 after filtering out false positives (Galactic objects and background quasars).

5. Significance

REGALADE addresses a critical bottleneck in modern astrophysics by providing a high-purity, volume-limited, and homogeneous galaxy reference. Its significance lies in:

Enabling Multi-Messenger Astronomy: It provides the necessary volume-limited samples to prioritize host candidates for gravitational wave events, improving the efficiency of electromagnetic follow-up.
Transient Classification: By providing reliable distances for faint and distant galaxies, it allows for real-time classification of transients (e.g., distinguishing Type Ia supernovae from core-collapse events) and accurate rate calculations.
Population Studies: The inclusion of galaxy sizes and stellar masses allows for unbiased studies of transient host environments, including the outskirts of galaxies where many transients occur.
Foundation for Future Surveys: The catalog serves as a baseline for upcoming deep surveys (LSST, Euclid) and acts as a training set for machine-learning algorithms aimed at automated galaxy classification and artifact removal.

In conclusion, REGALADE represents a major step forward in creating a "master catalog" for the nearby universe, specifically tailored to support the data-intensive demands of the advanced detector era.