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The Hubble Missing Globular Cluster Survey. III. Astro-photometric catalogs, artificial-star tests, and improved absolute proper motions

This paper presents the official astro-photometric catalogs and data reduction techniques for the Hubble Missing Globular Cluster Survey, which secured high-resolution HST data for 34 previously unobserved Galactic globular clusters, enabling improved absolute proper motions and updated associations with their galaxy progenitors through synergy with Gaia data.

Original authors: M. Libralato, A. Bellini, D. Massari, M. Bellazzini, F. Aguado-Agelet, S. Cassisi, E. Ceccarelli, E. Dalessandro, E. Dodd, F. R. Ferraro, C. Gallart, B. Lanzoni, M. Monelli, A. Mucciarelli, E. Pancino

Published 2026-03-30

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CC0 1.0

Original authors: M. Libralato, A. Bellini, D. Massari, M. Bellazzini, F. Aguado-Agelet, S. Cassisi, E. Ceccarelli, E. Dalessandro, E. Dodd, F. R. Ferraro, C. Gallart, B. Lanzoni, M. Monelli, A. Mucciarelli, E. Pancino, R. Pascale, L. Rosignoli, M. Salaris, S. Saracino, C. Zerbinati

Original paper dedicated to the public domain under CC0 1.0 (http://creativecommons.org/publicdomain/zero/1.0/). ✨ 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 Milky Way galaxy as a massive, ancient city. Scattered throughout this city are thousands of "neighborhoods" called Globular Clusters. These are tight-knit groups of hundreds of thousands of stars that formed together billions of years ago. For decades, astronomers have been trying to map every single one of these neighborhoods to understand how the city was built.

However, there was a "missing list." While the Hubble Space Telescope (HST) had photographed most of these neighborhoods, about 34 of them had never been seen in high definition by HST. They were the "ghosts" of the galaxy.

This paper is the final report from a special mission called the Hubble Missing Globular Cluster Survey (MGCS). The team successfully took high-resolution photos of these 34 missing neighborhoods and is now handing over the complete, polished maps to the rest of the scientific community.

Here is a breakdown of what they did, using simple analogies:

1. The Great Photo Shoot (Data Reduction)

Think of Hubble as a super-powered camera. The team took pictures of these 34 clusters using two different lenses: a wide-angle lens for the outer edges and a zoom lens for the crowded centers.

The Challenge: Sometimes the camera glitches, or the telescope has to twist in a way that makes the "wide" and "zoom" shots not line up perfectly.
The Fix: The team acted like expert photo editors. They took all the raw images, corrected for the camera's quirks (like lens distortion), and stitched them together into one perfect, seamless map for each cluster. They even created a "bright star list" to mask out the super-bright stars that would otherwise blind the camera's sensors, ensuring the fainter stars weren't lost in the glare.

2. The "Synthetic" Test (Artificial-Star Tests)

How do you know your photo isn't missing any stars? You can't just look at the picture; you have to test the camera's limits.

The Analogy: Imagine you are trying to count grains of sand on a beach, but some are buried deep in the wet sand. To test your counting ability, you secretly sprinkle a known number of fake grains of sand (artificial stars) onto the beach.
The Result: The team ran their software over the images to see if it could "find" these fake stars. By seeing how many fake stars were missed or miscounted, they created a "completeness map." This tells other scientists exactly how deep into the crowd of stars they can trust their data. It's like a warranty card for the data, saying, "We are 100% sure about these stars, but be careful with these faint ones."

3. The "Double-Check" with Gaia (Photometric Calibration)

Hubble is amazing, but it's not perfect. Sometimes, the colors in its photos might be slightly off compared to reality.

The Analogy: Imagine Hubble is a master painter, but the lighting in the studio makes the reds look a bit orange. To fix this, the team brought in a second, independent expert: the Gaia satellite (a European space mission that maps the whole sky).
The Synergy: They compared Hubble's colors with Gaia's "synthetic" colors (colors calculated from Gaia's low-resolution data). Where Hubble was slightly off, they used Gaia as a ruler to straighten the measurements. This ensured that the colors of stars in Cluster A match the colors of stars in Cluster B perfectly, allowing for fair comparisons across the whole galaxy.

4. The "Speed Trap" (Proper Motions)

Stars aren't just sitting still; they are zooming through space. To understand where a star came from, you need to know its speed and direction (its "proper motion").

The Problem: Gaia is great at measuring speed, but it gets fuzzy when stars are too crowded or too faint. HST is great at seeing crowded areas, but it only watched for a short time, making speed calculations tricky.
The Solution: The team combined the two. They used Gaia's long-term speed data as a backbone and HST's sharp, short-term snapshots as the muscle.
The Result: They created a "speed trap" that is 3.7 times more precise than Gaia alone. It's like upgrading from a blurry security camera to a high-speed, 4K slow-motion camera.

5. The Family Reunion (Tracing Origins)

Why does all this matter? Because these star clusters are like time capsules. If you know exactly where a cluster is and how fast it's moving, you can trace its path backward in time to see where it was born.

The Discovery: Using their new, super-precise speed data, the team updated the "family tree" of the Milky Way.
- Some clusters were born right here in our galaxy's "Bulge" (the city center).
- Some were born in the "Disk" (the suburbs).
- Others were "adopted"—stolen from other smaller galaxies that crashed into the Milky Way long ago (like the Gaia-Enceladus or Sagittarius galaxies).
The Update: For six of these clusters, this is the first time we know their true origin story. It's like finally finding the birth certificates for six long-lost relatives.

The Takeaway

This paper isn't just a list of numbers; it's a gift to the scientific community. The authors have released:

The Maps: The official catalogs of positions and brightness for all 34 clusters.
The Test Results: The "fake star" data so others know the limits of the maps.
The Speed Data: The most accurate movement data ever recorded for these specific clusters.

By sharing this, they are ensuring that astronomers for the next decade can study the history of our galaxy without having to redo the hard work of taking the pictures. They have cleared the "missing list," leaving the Hubble Space Telescope's census of our galactic neighborhood complete.

1. Problem Statement

The Galactic globular cluster (GC) census has historically been incomplete regarding Hubble Space Telescope (HST) coverage. While previous Treasury programs (e.g., ACS Survey, UV Legacy Survey) provided homogeneous data for many clusters, approximately 34 kinematically confirmed GCs lacked suitable high-resolution HST imaging. These "missing" clusters often reside in extreme parameter spaces (mass, density, environment) or regions with high extinction, making them difficult to study with ground-based telescopes. The lack of homogeneous photometric and astrometric data for these systems hindered a comprehensive understanding of the Milky Way's formation history, the origins of specific GC populations, and the structural/dynamical properties of the Galaxy.

2. Methodology

The authors present the data reduction, calibration, and analysis pipeline for the Hubble Missing Globular Cluster Survey (MGCS), which observed 34 previously unobserved GCs.

Observations:
- Sample: 34 GCs.
- Instruments: 27 clusters observed with ACS/WFC and WFC3/UVIS (optical); 7 clusters observed with WFC3/IR.
- Strategy: Designed for back-to-back visits to cover both cluster cores and outskirts, though some visits failed due to guide-star issues, leading to partial overlaps in parallel fields.
Data Reduction & Photometry:
- Pipeline: Utilized state-of-the-art HST reduction techniques (similar to Bellini et al. and Libralato et al.).
- Steps:
  1. First-pass: Fitting effective-PSF (ePSF) models to calibrated, unresampled images to identify bright sources.
  2. Astrometry: Anchored to the Gaia Early Data Release 3 (EDR3) frame, correcting for geometric distortion and propagating positions to the HST observation epoch.
  3. Bright-star masking: Created a list of saturated/bright sources to mask during second-pass photometry.
  4. Second-pass Photometry: Used the KS2 code to perform neighbor-subtracted photometry in crowded fields, detecting faint sources by utilizing all exposures simultaneously.
- Calibration: Calibrated to the Vega-magnitude system. Crucially, the authors performed an external cross-check against Gaia DR3 synthetic photometry (via GaiaXPy) to identify and correct systematic offsets (up to 0.1 mag) between ACS and WFC3 filters, ensuring homogeneity across the dataset.
Artificial-Star Tests (AST):
- Generated input catalogs based on Hess diagrams of observed Color-Magnitude Diagrams (CMDs) to mimic the real stellar populations.
- Injected $\sim$ 93,000 to $\sim$ 393,000 artificial stars per cluster (depending on field size and depth) into the images.
- Ran KS2 on these modified images to measure recovery rates, determining completeness levels and spatial systematic errors (e.g., chip gaps, dithering effects).
Proper Motion (PM) Analysis:
- Combined HST astrometry with Gaia DR3 data using the GaiaHub tool.
- Applied local corrections to remove position-dependent systematics (residuals from PSF modeling or geometric distortion).
- Calculated absolute PMs with significantly improved precision compared to Gaia alone.
Orbital Dynamics:
- Combined new PMs with literature distances and line-of-sight velocities.
- Used the AGAMA code with the McMillan (2017) gravitational potential to compute orbital parameters (Energy $E$ , Angular Momenta $L_z, L_\perp$ ).
- Assigned clusters to progenitor groups (e.g., Gaia-Enceladus, Sagittarius, In situ) based on integral-of-motion space.

3. Key Contributions

Public Data Release: The first release of official, homogeneous astro-photometric catalogs for all 34 MGCS targets, including positions, magnitudes, quality flags, and artificial-star test catalogs.
Photometric Homogeneity: A novel correction method using Gaia synthetic photometry to align HST optical filters (ACS and WFC3) across different clusters, resolving discrepancies that could not be explained by standard calibration alone.
Comprehensive AST: The provision of detailed artificial-star test catalogs for every target, allowing the community to assess completeness and systematic errors specific to each cluster's environment.
Improved Astrometry: A new set of absolute proper motions for the MGCS sample, achieving a precision roughly 3.7 times better than Gaia DR3 alone for most clusters.

4. Key Results

Catalog Quality: The final catalogs include positions and magnitudes for millions of sources. The 50% completeness level is reached at $m_{F606W} \approx 27.5$ (approx. 7 magnitudes below the main-sequence turn-off) in the optical fields.
Photometric Calibration: The Gaia-based correction successfully reduced zero-point differences between HST and Gaia synthetic photometry to near zero (median $\Delta m \approx 0.000$ ), though a small residual offset ( $\sim -0.02$ mag) remained in WFC3/UVIS F814W.
Proper Motions: The new PMs allowed for the identification of cluster members in crowded fields where Gaia alone failed.
Progenitor Associations:
- Updated the dynamical associations for the 34 clusters.
- New Associations: Identified Laevens 3 and Kim 3 as members of the Elqui stream; linked VVV-CL002 to Gaia-Enceladus (previously thought to be Bulge); and associated VVV-CL001 with the In situ Bulge.
- First-Time Determinations: Provided progenitor associations for five systems (Garro 1, Gran 2, Gran 3, Gran 4, Gran 5) for the first time based on this homogeneous framework.
- Discrepancies: Noted differences with previous studies (e.g., Callingham et al. 2022) for about one-third of the systems, attributed to the higher precision of the new PMs resolving overlaps in integral-of-motion space.

5. Significance

This paper represents a critical step in completing the HST census of Galactic globular clusters. By providing a homogeneous dataset for the "missing" clusters, it enables:

Galactic Archaeology: More robust reconstruction of the Milky Way's assembly history by accurately linking GCs to their accreted progenitors (e.g., Gaia-Enceladus, Sagittarius).
Standardization: Establishes a benchmark for future large-scale surveys (JWST, Nancy Grace Roman Space Telescope) by demonstrating how to combine HST and Gaia data for high-precision astrometry and photometry.
Scientific Utility: The release of AST catalogs and stacked images allows researchers to study stellar populations, binary fractions, and dynamical evolution in extreme environments previously inaccessible to high-resolution space-based surveys.

The work continues the legacy of HST Treasury programs, ensuring that the community has a complete, precise, and accurate atlas of the Milky Way's globular cluster system.