Using machine-learning classifications to constrain hot-subdwarf formation channels

Determine whether classification outputs from machine-learning pipelines can indirectly constrain the dominant formation channel of an individual hot subdwarf, including common-envelope ejection, stable Roche-lobe overflow, or the merger of two helium white dwarfs, using population-level correlations such as inferred binary status, orbital period, and companion type.

Background

Hot-subdwarf formation is thought to proceed primarily through binary evolutionary channels, including common-envelope ejection, stable Roche-lobe overflow, and helium-white-dwarf mergers. The paper notes that current machine-learning applications mainly identify candidates and classify variability, rather than connecting inferred observational properties to evolutionary origin.

The unresolved problem is whether machine-learning classifications, particularly when combined with inferred binarity, orbital periods, and companion types across large populations, can provide indirect empirical constraints on the dominant formation pathway of a given hot subdwarf. The paper characterizes this as largely unexplored.

References

Second, an open question is whether classification outputs from ML pipelines can be used to indirectly constrain the dominant formation channel of a given hot subdwarf, such as common-envelope ejection, stable Roche-lobe overflow, or the merger of two helium white dwarfs.

— Exploring Late Stellar Evolution in the Era of Large Surveys: Machine Learning Prospects for Hot Subdwarfs and White Dwarfs  (2608.25957 - Ranaivomanana et al., 26 Aug 2026) in Section 3.4, “Outstanding Challenges Specific to Hot Subdwarf Classification”