arXiv subjects

Dan Miao

Publications and source records attributed to Dan Miao.

3 recordsLinked to original sources

arXiv2026

MIAO-ALMA: Shocks and Protostellar Outflows in 70 $\mu$m-dark clumps with $L/M$ $<$ 1 $L_{\odot}$/$M_{\odot}$

To investigate the initial conditions of high-mass star-forming regions, we use SiO (2-1) emission to trace early shock-related kinematics toward sixteen 70 $\mu$m-dark and massive clumps with luminosity-to-mass ratios ($L/M$) $< 1\,L_{\odot}/M_{\odot}$, as part of the Multiwavelength Line-Imaging Survey of the 70 $\mu$m-dark and bright clouds (MIAO) project. Using ALMA observations at a spatial resolution of $\sim$0.06 pc and a velocity resolution of 0.21 km s$^{-1}$, we identify a total of thirty-seven outflows with a variety of morphologies. Outflow parameters were derived by integrating the HCO$^+$ (1-0) line wings, excluding the quiescent dense core component traced by H$^{13}$CO$^+$ (1-0). We find that outflow masses and velocities show moderate positive correlations with the masses of their driving cores. Owing to the high sensitivity of our observations, which yield longer projected outflow lengths compared to previous studies, the derived outflow dynamical ages span $\sim10^{3}$-$10^{5}$ yr. We detect six narrow-linewidth (0.6-1.4 km s$^{-1}$) and three broad ($>$ 2 km s$^{-1}$) SiO (2-1) features not associated with outflows driven by clearly identified protostars. Lacking coincident 3 mm dust continuum cores, their origins may be young outflows from undetected low-mass protostars, dissipating shocks, cloud-cloud collisions, or projection effects when the outflows lie close to the plane of the sky. The detection of these shocks and outflows in such extremely young environments demonstrates that protostellar activity has already begun.

astro-ph.GA↗

arXiv2026

Super-Jeans Fragmentation and Supply-Limited Accretion: Environment-Dependent Co-Evolution of Low- and High-Mass Cores

Protostellar core formation and growth in high-mass star-forming regions remain key to understanding massive star birth. We analyze the masses of 839 cores (resolved at scales of a few thousand au) from the ASHES project targeting 39 massive infrared dark cloud clumps. The masses of the three most massive cores scale linearly with the total core mass. They maintain a constant mass fraction of ~25%, 16%, and 10% along the mass growth sequence. These fractions reveal that the progenitor seeds destined to become high-mass cores establish their mass dominance very early. Additionally, the Gini coefficient (a statistical measure of inequality) of the core mass distributions increases along the mass growth sequence, confirming that the relative population of low-mass cores builds up toward later stages. This points to an environment-dependent fragmentation picture: central prestellar seeds rapidly evolve into high-mass cores via transport-driven super-Jeans fragmentation under rapid, non-stationary mass accumulation in high-density, turbulent hubs, subsequently maintain their dominance through supply-limited synchronized growth (at R<1 pc, n_H2>10^5 cm^-3), while the formation of the surrounding low-mass cores is relatively delayed due to their lower gas densities and the lack of non-stationary inflow acceleration effect, resulting in their continuous emergence through Jeans-like fragmentation in lower-density envelopes. This picture is consistent with a gravity-driven scenario where the local free-fall time is the controlling factor. Our analysis suggests that non-stationary density-regulated fragmentation and supply-limited accretion jointly drive the synchronized co-evolution of the core cluster, seamlessly linking small-scale core growth with large-scale reservoir regulation.

astro-ph.GA↗

arXiv2026

Demystifying image-recovery from radio interferometers: toward a multiscale predictive model

Radio interferometers suffer from the missing short-spacing problem, losing large-scale diffuse emission. This missing flux underestimates gas mass and biases key metrics like star formation efficiency. Quantifying this scale-dependent loss currently relies on computationally intensive mock observations, lacking an analytical image-domain framework. We introduce the Constrained Diffusion Decomposition (CDD) method to decompose an input image ($I_{\mathrm{in}}$) into $n$ continuous scale-space components, denoted as $I_l = \mathrm{CDD}_l(I_{\mathrm{in}})$ for $l \in [1, n]$, and apply it to simulated Atacama Large Millimeter/submillimeter Array (ALMA) observations of the Perseus molecular cloud across multiple array configurations. We find that the interferometric spatial filtering response can be mathematically decoupled: the scale-dependent flux recovery fraction follows a one-dimensional error function (\texttt{erf}), defined as $R(l) = \frac{B}{2} \left[ 1 - \mathrm{erf}\left( \frac{l - c_{\mathrm{recover}}}{w} \right) \right]$, where compact structures are effectively recovered, while extended emission decays monotonically as scales approach the maximum recoverable scale. The proposed CDD--\texttt{erf} framework predicts the spatially filtered interferometric image $I_{\mathrm{pred}}$ directly in the image domain, bypassing visibility simulations, mapping the true sky brightness distribution via the equation $I_{\mathrm{pred}} = \sum_{l=1}^{n} [ \mathrm{CDD}_l(I_{\mathrm{in}}) \times R(l)]$. This provides a quantitative bridge between model and interferometric observations.

astro-ph.IM↗