SearcharxivSearch

arXiv subjects

Xiaoxuan Chen

Publications and source records attributed to Xiaoxuan Chen.

2 recordsLinked to original sources

Fountain pattern of baryon cycle revealed in galaxy ecosystems

Baryons in galaxy ecosystems are believed to undergo continuous cycles of inflow and outflow, forming fountain-like patterns that encode key information about how galaxies acquire matter from their environments and respond through feedback. The presence of such baryon cycles has been inferred from pieces of observational evidence, but a concrete understanding remains elusive because individual galaxy ecosystems are diverse and dynamic. Here we introduce a stacking method that combines baryonic fields across ensembles of individual galaxy ecosystems to suppress irregularities and reveal the underlying pattern. Applied to a cosmological hydrodynamic simulation, this approach unveils strikingly regular patterns in gas properties across the full spatial extent of galaxy ecosystems, in close agreement with those inferred from observations. This method is straightforward to implement, allowing the processes shaping the gas-cycling pattern to be fully understood within the structure-formation paradigm, and a solid framework to be constructed for linking simulated galaxy ecosystems with observations.

astro-ph.GA

Multimodal Trajectory Prediction for Autonomous Driving on Unstructured Roads using Deep Convolutional Network

Recently, the application of autonomous driving in open-pit mining has garnered increasing attention for achieving safe and efficient mineral transportation. Compared to urban structured roads, unstructured roads in mining sites have uneven boundaries and lack clearly defined lane markings. This leads to a lack of sufficient constraint information for predicting the trajectories of other human-driven vehicles, resulting in higher uncertainty in trajectory prediction problems. A method is proposed to predict multiple possible trajectories and their probabilities of the target vehicle. The surrounding environment and historical trajectories of the target vehicle are encoded as a rasterized image, which is used as input to our deep convolutional network to predict the target vehicle's multiple possible trajectories. The method underwent offline testing on a dataset specifically designed for autonomous driving scenarios in open-pit mining and was compared and evaluated against physics-based method. The open-source code and data are available at https://github.com/LLsxyc/mine_motion_prediction.git

cs.AI