Observations of the HK Tau binary with JWST reveal gas-phase molecular lines in the low-inclination primary and ice absorption features in the edge-on secondary, enabled by their differing inclinations.
A systematic search for late-stage infall of material onto Class II disks
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 4representative citing papers
X-Shooter survey of 127 Upper Scorpius disks finds no correlation between accretion rate and disk dust mass or gas radius, with increased dispersion versus younger regions suggesting inner-outer disk decoupling.
Halos in Elias 2-24, IM Lup, and DM Tau hold 20-30% of total dust mass with cm-sized grains, helping resolve the disk mass-budget problem even though drift and growth timescales are shorter than disk ages.
The paper proposes the iSEEDs project to integrate machine learning with astrochemistry for extracting physical conditions and molecular abundances from protostellar disk datasets.
citing papers explorer
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MINDS: Complementary inclinations in the binary system HK Tau reveal gas- and ice-phase chemistry
Observations of the HK Tau binary with JWST reveal gas-phase molecular lines in the low-inclination primary and ice absorption features in the edge-on secondary, enabled by their differing inclinations.
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X-Shooter survey of disk accretion in Upper Scorpius II. A lack of correlation between accretion rates and disk properties
X-Shooter survey of 127 Upper Scorpius disks finds no correlation between accretion rate and disk dust mass or gas radius, with increased dispersion versus younger regions suggesting inner-outer disk decoupling.
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Dust characterization of halos: The extended emission in protoplanetary disks
Halos in Elias 2-24, IM Lup, and DM Tau hold 20-30% of total dust mass with cm-sized grains, helping resolve the disk mass-budget problem even though drift and growth timescales are shorter than disk ages.
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Astrochemical Study of Early Embedded Disks
The paper proposes the iSEEDs project to integrate machine learning with astrochemistry for extracting physical conditions and molecular abundances from protostellar disk datasets.