exoALMA. VI. Rotating under Pressure: Rotation curves, azimuthal velocity substructures, and pressure variations

Abstract

The bulk motion of the gas in protoplanetary disks around newborn stars is nearly Keplerian. By leveraging the high angular and spectral resolution of ALMA, we can detect small-scale velocity perturbations in molecular line observations caused by local gas pressure variations in the disk, possibly induced by embedded protoplanets. This paper presents the azimuthally averaged rotational velocity and its deviations from Keplerian rotation ($\delta {\upsilon }_{\varphi }$) for the exoALMA sample, as measured in the ${}^{12}$CO and ${}^{13}$CO emission lines. The rotation signatures show evidence for vertically stratified disks, in which ${}^{13}$CO rotates faster than ${}^{12}$CO due to a distinct thermal gas pressure gradient at their emitting heights. We find $\delta {\upsilon }_{\varphi }$-substructures in the sample on both small ($\sim$10 au) and large ($\sim$100 au) radial scales, reaching deviations up to 15% from background Keplerian velocity in the most extreme cases. More than 75% of the rings and 80% of the gaps in the dust continuum emission resolved in $\delta {\upsilon }_{\varphi }$ are co-located with gas pressure maxima and minima, respectively. Additionally, gas pressure substructures are observed far beyond the dust continuum emission. For the first time, we determined the gas pressure derivative at the midplane from observations and found it to align well with the dust substructures within the given uncertainties. Based on our findings, we conclude that gas pressure variations are likely the dominant mechanism for ring and gap formation in the dust continuum.

This paper is part of the exoALMA Focus Issue of The Astrophysical Journal Letters. 37 pages, 19 figures

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• exoALMA IV: Substructures, Asymmetries, and the Faint Outer Disk in Continuum Emission
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• exoALMA. XVII. Characterizing the Gas Dynamics around Dust Asymmetries
• exoALMA. VIII. Probabilistic Moment Maps and Data Products using Non-parametric Linear Models
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