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Grain growth in the envelopes and disks of Class I protostars

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arxiv 1405.0821 v1 pith:7NOJMKE3 submitted 2014-05-05 astro-ph.SR

classification astro-ph.SR
keywords classdataenvelopesalreadyanalysisbaselinesdisk-likedust
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abstract

We present new 3 mm ATCA data of two Class I Young Stellar Objects in the Ophiucus star forming region: Elias29 and WL12. For our analysis we compare them with archival 1.1 mm SMA data. In the (u,v) plane the two sources present a similar behavior: a nearly constant non-zero emission at long baselines, which suggests the presence of an unresolved component and an increase of the fluxes at short baselines, related to the presence of an extended envelope. Our data analysis leads to unusually low values of the spectral index $\alpha_{\rm 1.1-3mm}$, which may indicate that mm-sized dust grains have already formed both in the envelopes and in the disk-like structures at such early stages. To explore the possible scenarios for the interpretation of the sources we perform a radiative transfer modeling using a Monte Carlo code, in order to take into account possible deviations from the Rayleigh-Jeans and optically thin regimes. Comparison between the model outputs and the observations indicates that dust grains may form aggregates up to millimeter size already in the inner regions of the envelopes of Class I YSOs. Moreover, we conclude that the embedded disk-like structures in our two Class Is are probably very compact, in particular in the case of WL12, with outer radii down to tens of AU.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Chemical Inventory of the Disk around the Class 0 Protostar L1527 IRS with ALMA

    astro-ph.GA 2026-08 accept novelty 6.0 of 10

    A comprehensive ALMA-based chemical inventory of the Class 0 protostar L1527 IRS finds 39 molecular species and a carbon-rich to oxygen-rich transition from envelope to disk.

  2. Astrochemical Study of Early Embedded Disks

    astro-ph.SR 2026-06 unverdicted novelty 3.0 of 10

    The paper proposes the iSEEDs project to integrate machine learning with astrochemistry for extracting physical conditions and molecular abundances from protostellar disk datasets.

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