121 citations · 501 across the 18 of their papers we have counts for
20 papers
Filament Formation via Collision-induced Magnetic Reconnection -- Kinematic Features
Shuo Kong, Griselda Arroyo-Chávez, Volker Ossenkopf-Okada +4
The Collision-induced Magnetic Reconnection (CMR) mechanism has recently been proposed as a filament formation model that emphasizes the active role of magnetic fields. To enable b…
Assessing Zeeman Measurements of Magnetic Fields in Synthetic HI Observations
Duo Xu, Peter Martin, Stella Offner +3
Zeeman observations provide the only direct probe of line-of-sight (LOS) magnetic fields in the interstellar medium. To evaluate their accuracy and limitations, we generate synthet…
Predicting the Radiation Field of Molecular Clouds using Denoising Diffusion Probabilistic Models
Duo Xu, Stella Offner, Robert Gutermuth +3
Accurately quantifying the impact of radiation feedback in star formation is challenging. To address this complex problem, we employ deep learning techniques, denoising diffusion p…
CMR exploration II -- filament identification with machine learning
Duo Xu, Shuo Kong, Avichal Kaul +2
We adopt magnetohydrodynamics (MHD) simulations that model the formation of filamentary molecular clouds via the collision-induced magnetic reconnection (CMR) mechanism under varyi…
Denoising Diffusion Probabilistic Models to Predict the Density of Molecular Clouds
Duo Xu, Jonathan C. Tan, Chia-Jung Hsu +1
We introduce the state-of-the-art deep learning Denoising Diffusion Probabilistic Model (DDPM) as a method to infer the volume or number density of giant molecular clouds (GMCs) fr…
CMR exploration I -- filament structure with synthetic observations
Shuo Kong, Volker Ossenkopf-Okada, Héctor G. Arce +2
In this paper, we carry out a pilot parameter exploration for the collision-induced magnetic reconnection (CMR) mechanism that forms filamentary molecular clouds. Following Kong et…
Application of Convolutional Neural Networks to Predict Magnetic Fields Directions in Turbulent Clouds
Duo Xu, Chi-Yan Law, Jonathan C. Tan
We adopt the deep learning method CASI-3D (Convolutional Approach to Structure Identification-3D) to infer the orientation of magnetic fields in sub-/trans- Alfvenic turbulent clou…
A Census of Outflow to Magnetic Field Orientations in Nearby Molecular Clouds
Duo Xu, Stella S. R. Offner, Robert Gutermuth +1
We define a sample of 200 protostellar outflows showing blue and redshifted CO emission in the nearby molecular clouds Ophiuchus, Taurus, Perseus and Orion to investigate the corre…
A Census of Protostellar Outflows in Nearby Molecular Clouds
Duo Xu, Stella Offner, Robert Gutermuth +2
We adopt the deep learning method CASI-3D (Convolutional Approach to Structure Identification-3D) to systemically identify protostellar outflows in 12CO and 13CO observations of th…
Application of Convolutional Neural Networks to Identify Protostellar Outflows in CO Emission
Duo Xu, Stella S. R. Offner, Robert Gutermuth +1
We adopt the deep learning method CASI-3D (Convolutional Approach to Structure Identification-3D) to identify protostellar outflows in molecular line spectra. We conduct magneto-hy…
Application of Convolutional Neural Networks to Identify Stellar Feedback Bubbles in CO Emission
Duo Xu, Stella S. R. Offner, Robert Gutermuth +1
We adopt the deep learning method CASI (Convolutional Approach to Shell Identification) and extend it to 3D (CASI-3D) to identify signatures of stellar feedback in molecular line s…
Numerical Simulation and Completeness Survey of Bubbles in the Taurus and Perseus Molecular Clouds
Mengting Liu, Di Li, Marko Krco +3
Previous studies have analyzed the energy injection into the interstellar matter due to molecular bubbles. They found that the total kinetic energies of bubbles are comparable to,…
CASI: A Convolutional Neural Network Approach for Shell Identification
Colin M. Van Oort, Duo Xu, Stella S. R. Offner +1
We utilize techniques from deep learning to identify signatures of stellar feedback in simulated molecular clouds. Specifically, we implement a deep neural network with an architec…
The Milky Way Project Second Data Release: Bubbles and Bow Shocks
Tharindu Jayasinghe, Don Dixon, Matthew S. Povich +9
Citizen science has helped astronomers comb through large data sets to identify patterns and objects that are not easily found through automated processes. The Milky Way Project (M…
Where is OH and Does It Trace the Dark Molecular Gas (DMG)?
Di Li, Ningyu Tang, Hiep Nguyen +15
Hydroxyl (OH) is expected to be abundant in diffuse interstellar molecular gas as it forms along with under similar conditions and within a similar extinction range. We have…
CH as a Molecular Gas Tracer and C-Shock Tracer Across a Molecular Cloud Boundary in Taurus
Duo Xu, Di Li
We present new observations of all three ground-state transitions of the methylidyne (CH) radical and all four ground-state transitions of the hydroxyl (OH) radical toward a sharp…
Evolution of OH and CO-dark Molecular Gas Fraction Across a Molecular Cloud Boundary In Taurus
Duo Xu, Di Li, Nannan Yue +1
We present observations of 12CO J=1-0, 13CO J=1-0, HI, and all four ground-state transitions of the hydroxyl (OH) radical toward a sharp boundary region of the Taurus molecular clo…
Outflows and Bubbles in Taurus: Star-formation Feedback Sufficient to Maintain Turbulence
Huixian Li, Di Li, Lei Qian +6
We have identified outflows and bubbles in the Taurus molecular cloud based on the deg Five College Radio Astronomy Observatory CO(1-0) and CO(1-0) map…
Quantifying Dark Gas
Di Li, Duo Xu, Carl Heiles +2
A growing body of evidence has been supporting the existence of so-called "dark molecular gas" (DMG), which is invisible in the most common tracer of molecular gas, i.e., CO rotati…
Can Gamma-Ray Bursts Be Used to Measure Cosmology? A Further Analysis
D. Xu, Z. G. Dai, E. W. Liang
Three different methods of measuring cosmology with gamma-ray bursts (GRBs) have been proposed since a relation between the -ray energy of a GRB jet and the peak energy $…