Impact of Gaia–Sausage–Enceladus Debris on Milky Way Escape-velocity Estimates in TNG50
Abstract The escape velocity is a key probe of the Milky Way (MW) potential, but standard estimates can be biased by unrelaxed substructures. Using MW analogs from the TNG50 cosmological simulation, we investigate the impact of the dominant, highly radial Gaia–Sausage–Enceladus (GSE) debris on escape-velocity measurements. We introduce an adaptive selection strategy that balances statistical reliability with sample size to identify the power-law decline regime of the local halo velocity distribution. Applying this strategy to 16 MW analogs with prominent GSE-like structures and continuous high-velocity tails, we find that retaining GSE-like stars yields a systematic underestimate, with a recovery fraction of 97 . 5 % − 2.9 % + 2.1 % , while removing GSE-like stars alone increases the recovery fraction to 99 . 4 % − 2.5 % + 2.1 % . The ∼2% underestimation of the escape velocity corresponds to an ∼6% underestimation of the total mass. This improvement persists in LAMOST-like magnitude and footprint selection. GSE-like debris is therefore a key contributor to the systematic underestimation of escape-velocity measurements, with direct implications for the inferred MW mass.
Authors
- Shi Shao (ORCID: https://orcid.org/0000-0001-8382-6323)
- Xiang-Xiang Xue (ORCID: https://orcid.org/0000-0002-0642-5689)
- Yin Wu (ORCID: https://orcid.org/0009-0003-4116-6824)
- Gang Zhao (ORCID: https://orcid.org/0000-0002-8980-945X)
- Haining Li (ORCID: https://orcid.org/0000-0002-0389-9264)
- Lan Zhang (ORCID: https://orcid.org/0000-0001-7080-0618)
Institutions
- Chinese Academy of Sciences (CN)
- Beijing Normal University (CN)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Journal
- The Astrophysical Journal
- Published
- 2026-11-02
- DOI
- https://doi.org/10.3847/1538-4357/aeb00d
- Primary Topic
- Stellar, planetary, and galactic studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Chinese Academy of Sciences
- Gauss Centre for Supercomputing