46 citations · 70 across the 3 of their papers we have counts for
7 papers
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…
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…