Spatial and Kinematic Clustering of Stars in the Galactic Disk
Abstract
The Galactic disk is expected to be spatially and kinematically clustered on many scales due to both star formation and the Galactic potential. In this work we calculate the spatial and kinematic two-point correlation functions (TPCF) using a sample of 1.7 × 10⁶ stars with radial velocities from Gaia DR2. Clustering is detected on spatial scales of 1–300 pc and a velocity scale of 15 km s⁻¹. After removing bound structures, the data have a power-law index of γ ≈ −1 for 1 pc r γ ≲ −1.5 for Δr > 100 pc. We interpret these results with the aid of a star-by-star simulation of the Galaxy, in which stars are born in clusters orbiting in a realistic potential that includes spiral arms, a bar, and giant molecular clouds. We find that the simulation largely agrees with the observations at most spatial and kinematic scales. In detail, the TPCF in the simulation is shallower than the data at ≲20 pc scales, and steeper than the data at ≳30 pc. We also find a persistent clustering signal in the kinematic TPCF for the data at large Δv (>5 km s⁻¹) that is not present in the simulations. We speculate that this mismatch between observations and simulations may be due to two processes: hierarchical star formation and transient spiral arms. We also predict that the addition of ages and metallicities measured with a precision of 50% and 0.05 dex, respectively, will enhance the clustering signal beyond current measurements.
Additional Information
© 2021. The American Astronomical Society. We thank Angus Beane, Anthony Brown, Lehman Garrison, Yan-Fei Jiang, Diederik Kruijssen, Hans-Walter Rix, and members of the Conroy group at Harvard for useful discussions and helpful comments. H.M.K. acknowledges support from the DOE CSGF under grant number DE-FG02-97ER25308. C.C. acknowledges support from the Packard Foundation. Y.S.T. is supported by the NASA Hubble Fellowship grant HST-HF2-51425.001 awarded by the Space Telescope Science Institute. The computations in this paper were run on the Odyssey cluster supported by the FAS Division of Science, Research Computing Group at Harvard University. This work has made use of data from the European Space Agency mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. The Sloan Digital Sky Survey IV is funded by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions and acknowledges support and resources from the Center for High-Performance Computing at the University of Utah. Software: CorrFunc (Sinha & Garrison 2018, 2020), IPython (Pérez & Granger 2007), Cython (Behnel et al. 2011), Astropy (Astropy Collaboration et al. 2013, 2018), NumPy (Van Der Walt et al. 2011), SciPy (Jones et al. 2001), scikit-Learn (Pedregosa et al. 2011), Matplotlib (Hunter 2007).Additional details
- Eprint ID
- 118713
- Resolver ID
- CaltechAUTHORS:20230105-895092000.36
- Department of Energy (DOE)
- DE-FG02-97ER25308
- David and Lucile Packard Foundation
- NASA Hubble Fellowship
- HST-HF2-51425.001
- Gaia Multilateral Agreement
- Alfred P. Sloan Foundation
- Created
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2023-01-09Created from EPrint's datestamp field
- Updated
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2023-01-09Created from EPrint's last_modified field