Xu, D., Zhu, Y., 2024, "Surveying Image Segmentation Approaches in Astronomy", Astronomy and Computing, 100838 [*Invited review]
Xu, D., Tan, J., Staff, J., Ramsey, J., Zhang, Y., Tanaka, K., 2024, “Disk Wind Feedback from High-mass Protostars. III. Synthetic CO Line Emission”, ApJ, 966, 117
Xu, D., Offner, S., Gutermuth, R., Grudi, M., Guszejnov, D., Hopkins, P., 2023, “Predicting the Radiation Field of Molecular Clouds using Denoising Diffusion Probabilistic Models”, ApJ in press
Xu, D., Kong, S., Kaul, A., Arce, H., Ossenkopf-Okada, V., 2023, “CMR exploration II -- filament identification with machine learning”, ApJ, 955, 113
Xu, D., Tan, J., Hsu, C., Zhu, Y., 2023, “Denoising Diffusion Probabilistic Models to Predict the Density of Molecular Clouds", ApJ, 950, 146
Xu, D., Law, C., Tan, J., 2023, “Application of Convolutional Neural Networks to Predict Magnetic Fields Directions in Turbulent Clouds”, ApJ, 942, 95
Xu, D., Offner, S., Gtermuth, R., Tan, J., 2022,“A Census of Outflow to Magnetic Field Orientations in Nearby Molecular Clouds”, ApJ, 941, 81
Xu, D., Offner, S., Gutermuth, R., Kong, S., Arce, H., 2022,“A Census of Protostellar Outflows in Nearby Molecular Clouds”, ApJ, 926, 19
Xu, D., Offner, S., Gutermuth, R., Van Oort, C., 2020, “Application of Convolutional Neural Networks to Identify Protostellar Outflows in CO Emission”, ApJ, 905, 172
Xu, D., Offner, S., Gutermuth, R., Van Oort, C., 2020, “Application of Convolutional Neural Networks to Identify Stellar Feedback Bubbles in CO Emission”, ApJ, 890, 64
Santos, J., Xu, D., Jo, H., Landry, C., Prodanović, M., Pyrcz, M., 2020, “PoreFlow-Net: a 3D convolutional neural network to predict fluid flow through porous media”, Advances in Water Resources, p.103539
Van Oort C., Xu, D., Offner, S., Gutermuth R., 2019, “CASI: A Convolutional Neural Network Approach for Shell Identification”, ApJ, 880, 83
Liu, M., Li, D., Krčo, M., Ho, L., Xu, D., Li, H., 2019, “Numerical Simulation and Completeness Survey of Bubbles in the Taurus and Perseus Molecular Clouds”, ApJ, 885, 124
Jayasinghe, T., Dixon, D., Povich, M., Binder B., Velasco, J., Lepore, D., Xu, D., Offner, S., Kobulnicky, H., Anderson, L., Kendrew, S., Simpson, R., 2019, “The Milky Way Project second data release: bubbles and bow shocks”, MNRAS, 488, 1141
Li, D., Tang, N., Nguyen, H., Dawson, J., Heiles, C., Xu, D., Pan, Z., Goldsmith, P., Gibson, S., Murray, C., Robishaw, T., McClure-Griffiths, N., Dickey, J., Pineda, J., Stanimirovic, S., Bronfman, L., Troland, T., the PRIMO collaboration, 2018, Where is OH and Does It Trace the Dark Molecular Gas (DMG)?, ApJS, 235, 1
Xu, D. & Offner, S, 2017, Assessing the Performance of a Machine Learning Algorithm in Identifying Bubbles in Dust Emission, ApJ, 851, 149
Xu, D. & Li, D., 2016, CH as a Molecular Gas Tracer and C-shock Tracer Across a Molecular Cloud Boundary in Taurus, ApJ, 833, 90
Xu, D., Li, D., Yue N., Goldsmith, P., 2016, Evolution of OH and CO-dark Molecular Gas Fraction Across a Molecular Cloud Boundary In Taurus, ApJ, 819, 22
Li, H., Li, D., Qian, L., Xu, D., Goldsmith, P., Noriega-Crespo, A., Wu, Y., Song, Y., Nan, R., 2015, Outflows and Bubbles in Taurus: Star-formation Feedback Sufficient to Maintain Turbulence, ApJS, 219, 20
Li, D., Xu, D., Heiles, C., Pan, Z., Tang,N. 2015, Quantifying Dark Gas, Publication of Korean Astronomical Society, 30, 75