arXiv subjects

Donglin Wu

Publications and source records attributed to Donglin Wu.

4 recordsLinked to original sources

arXiv2026

Asymmetry in the protostellar system HOPS 198: Evidence for the evolution of outflow opening angle driven by density of the surrounding core

Protostellar outflows are thought to be responsible for the low star formation efficiency of protostellar cores. However, whether outflows can disperse a significant fraction of the gas in the core depends on the outflow opening angle. It is established that the outflow opening angle increases during the early stages of the protostellar evolution, but the underlying mechanism is poorly understood. Observations of HOPS 198, a Class 0 protostar in the Orion A molecular cloud, provide insights into this question. HOPS 198 exhibits a strong east-west asymmetry in its outflow and its core. The opening angle of the eastern lobe ($\sim80^{\circ}$) is more than twice wider than that of the western lobe ($\sim30^{\circ}$), while the surface density of the west side of the core is $1.5-2.8$ times higher than the east side. Using an analytical model in which the molecular outflow morphology is shaped by interactions between the wide-angle protostellar wind ($\gtrsim 80^{\circ}$) and surrounding material in the core, we find that the difference in opening angle for the two lobes can be explained by the difference in core density on the two sides. This result supports the hypothesis that the evolution of the outflow opening angle is driven by the evolution in the density of the protostellar core.

astro-ph.SR↗

arXiv2025

Constraining properties of dust formed in Wolf-Rayet binary WR 112 using mid-infrared and millimeter observations

Binaries that host a carbon-rich Wolf-Rayet (WC) star and an OB-type companion can be copious dust producers. Yet the properties of dust, particularly the grain size distribution, in these systems remain uncertain. We present Band 6 observations of WR 112 by the Atacama Large Millimeter/submillimeter Array telescope (ALMA), which are the first millimeter observations of a WC binary system capable of resolving its dust emission. By combining ALMA observations with James Webb Space Telescope (JWST) images, we were able to analyze the spatially resolved spectral energy distribution (SED) of WR 112. We found that the SEDs are consistent with emissions from hydrogen-poor amorphous carbon grains. Notably, our results also suggest that the majority of grains in the system have radii below one micrometer, and the extended dust structures are dominated by nanometer-sized grains. Among four parameterizations of the grain radius distribution that we tested, a bimodal distribution, with abundant nanometer-sized grains and a secondary population of 0.1-micron grains, best reproduces the observed SED. This bimodal distribution helps to reconcile the previously conflicting grain size estimates reported for WR 112 and for other WC systems. We hypothesize that dust destruction mechanisms such as radiative torque disruption and radiative-driven sublimation are responsible for driving the system to the bimodal grain size distribution.

astro-ph.SR↗

arXiv2024

Deep learning for cosmological parameter inference from a dark matter halo density field

We propose a lightweight deep convolutional neural network (lCNN) to estimate cosmological parameters from simulated three-dimensional dark matter (DM) halo distributions and associated statistics. The training dataset comprises 2000 realizations of a cubic box with a side length of 1000 $h^{-1}{\rm Mpc}$, and interpolated over a cubic grid of $300^3$ voxels, with each simulation produced using $512^3$ DM particles and $512^3$ neutrinos. Under the flat $Λ$CDM model, simulations vary standard six cosmological parameters including $Ω_m$, $Ω_b$, $h$, $n_s$, $σ_8$, $w$, along with the neutrino mass sum, $M_ν$. We find that: 1) within the framework of lCNN, extracting large-scale structure information is more efficient from the halo density field compared to relying on the statistical quantities including the power spectrum, the two-point correlation function, and the coefficients from wavelet scattering transform; 2) combining the halo density field with its Fourier transformed counterpart enhances predictions, while augmenting the training dataset with measured statistics further improves performance; 3) achieving high accuracy in inferring $Ω_m$, $h$, and $σ_8$ by the neural network model, while being inefficient in predicting $Ω_b$, { $n_s$}, $M_ν$ and $w$; 4) { compared to the simple fully connected network trained with three statistical quantities, our CNN yields statistically reduced errors, showing improvements of approximately 23\% for $Ω_m$, 11\% for $h$, 8\% for $n_s$, and 21\% for $σ_8$. Additionally, in comparison with the likelihood-based analysis on $P(k)$ data, our CNN provides much tighter constraints on parameters, especially on $Ω_m$ and $σ_8$.} Our study emphasizes this lCNN-based novel approach in extracting large-scale structure information and estimating cosmological parameters.

astro-ph.CO↗

arXiv2022

New estimate for the contribution of the Geminga pulsar to the positron excess

The origin of the positron excess is one of the most intriguing mysteries in astroparticle physics. The recent discovery of extended $γ$-ray halos around the pulsars Geminga, Monogem and PSR J0621+3755 have brought indirect evidence that pulsar wind nebulae accelerate $e^{\pm}$ up to very-high-energy. While the precision of previous data does not permit precise evaluation of the parameters for the pulsars, we are able to find the more precise shape of the injection spectrum using new data released by HAWC and LHAASO in 2020 and 2021. We find that this is well fitten by a power-law with an exponential cutoff. The spectral index is quite hard with values around 1 while the cutoff energy is roughly 100 TeV. We also derive the strength of the diffusion coefficient around the pulsars finding that it is two orders of magnitude lower than the average of the Galaxy. Finally, we use the above mentioned results to estimate the contribution of Geminga to the positron excess. This source alone can contribute to the entire positron excess at around 1 TeV.

astro-ph.HE↗