Searching for Stellar Siblings: Testing Chemodynamical Tagging of Open Clusters in the Milky Way

Astronomers are on a quest to identify long-lost “siblings” of stars — those that were born together in open clusters but have since drifted apart across the Milky Way. In their recent paper, Barth et al. investigate whether it’s possible to reunite these stellar families using a technique called chemodynamical tagging. This method uses both the chemical fingerprints and the orbital motions of stars to try and group them back with their original clusters. The study makes use of data from two major surveys, Gaia and GALAH, and puts current tagging methods to the test.

Why Tag Stars at All?

The Milky Way is a giant cosmic puzzle made up of stars that were born in different environments. Over time, the stars that formed together often drift apart, spreading across the galaxy. Chemodynamical tagging aims to trace stars back to their birth environments using their chemical compositions (what elements they contain) and their dynamics (how they move in the galaxy). While this has been used successfully to identify big structures like the galaxy’s disk or halo, doing it for individual star clusters — known as strong chemical tagging — is much harder.

What Data Did They Use?

To test the method, the authors used two large datasets. Gaia DR3 provides information on the positions and motions of stars, while GALAH DR4 provides detailed chemical information. By combining these, the authors created a sample of nearly 1 million stars, including a subset of 29 well-studied open clusters (OCs) with known members. These clusters were chosen because they have enough members and good measurements for things like metallicity (how rich they are in elements heavier than hydrogen and helium), age, and where in the galaxy they were likely born.

How Did They Try to Find the Clusters?

Barth and collaborators used a clustering algorithm called HDBSCAN, which groups stars based on similarities in their properties. They tried different combinations of parameters — such as orbital energy and chemical abundances — to see which ones best grouped the OC stars together. They measured success using a score called the V-measure, which reflects how complete and accurate the groupings are. A high score means the algorithm successfully pulled out the real clusters from the sea of stars.

What Worked Best?

The team found that orbital dynamics — like the energy and angular momentum of a star’s orbit — were more effective at recovering clusters than chemical information. The best-performing combination included four orbital parameters (E, JR, Jϕ, JZ) and achieved a modest V-measure score of 0.5 in an ideal test case with only cluster stars and mock field stars. However, when applied to the full dataset that includes hundreds of thousands of field stars, the recovery scores dropped drastically, often near zero.

Can We Improve the Recovery?

Next, the authors tried to improve the results by narrowing down the dataset. They selected only stars that had similar metallicity, age, or birth radius to each cluster. While this trimming slightly improved some recovery scores, it wasn’t enough to make a big difference. In general, even after these cuts, open clusters remained hard to find. Adding certain chemical elements — particularly neutron-capture elements like barium and yttrium — helped in specific cases, but did not dramatically change the overall success.

What Does This Mean for the Field?

The discussion section emphasizes the current limitations of chemodynamical tagging. The main challenge is that many clusters have very similar chemical compositions, making it hard to distinguish them based on chemistry alone. Also, stars in the galactic disk — where most OCs live — tend to have overlapping orbits. This makes it hard to separate them using dynamics either. The authors suggest that better models of the Milky Way’s gravitational potential and more precise abundance measurements could help in the future.

Conclusion: A Work in Progress

While chemodynamical tagging shows promise, this study highlights how hard it is to recover star clusters using current data and methods. The best hope may be to combine multiple types of information — chemistry, dynamics, and possibly even machine learning — in smarter ways. For now, reuniting stars with their long-lost siblings remains a challenging task, but one that astronomers are determined to solve.

Source: Barth