arXiv subjects

Haowen Guan

Publications and source records attributed to Haowen Guan.

3 recordsLinked to original sources

arXiv2026

Cooler Phases of the Circumgalactic Medium Are More Centrally Concentrated: Constraints from Multiphase Absorption Lines

We present a systematic study of the multiphase circumgalactic medium (CGM) around galaxies and quasars, traced by Ca II $\lambda\lambda3934,3969$, Mg II $\lambda\lambda2796,2803$, and C IV $\lambda\lambda1548,1550$, using the Year 1 dataset from the Dark Energy Spectroscopic Instrument. These three doublets trace CGM gas across a range of temperatures, from cold to warm phases, and we employ a stacking technique to measure the corresponding absorption signals using background sources. We show that CGM structure is strongly phase-dependent: ions tracing progressively cooler gas exhibit increasingly steep radial profiles in equivalent width ($W_i$). These trends are broadly consistent with predictions from cosmological simulations, supporting a phase-stratified CGM in which cooler gas is more centrally concentrated. Specifically, halos of emission-line galaxies exhibit a strong radial transition from cool to warm gas, whereas halos of quasars show a more uniform distribution, likely regulated by active galactic nuclei feedback; in contrast, the cold gas traced by Ca II in low-redshift galaxies is tightly confined to inner regions. We further demonstrate that the radial scaling $W_i \propto D^{\alpha}$ is primarily set by host stellar mass, particularly for the cool-phase medium, suggesting efficient heating processes in massive halos. By jointly leveraging multiple absorption tracers from observations and simulations, we map the CGM from cold to warm phases and place new constraints on the baryon cycle governing galaxy evolution.

astro-ph.GA↗

arXiv2023

Discovering Galaxy Features via Dataset Distillation

In many applications, Neural Nets (NNs) have classification performance on par or even exceeding human capacity. Moreover, it is likely that NNs leverage underlying features that might differ from those humans perceive to classify. Can we "reverse-engineer" pertinent features to enhance our scientific understanding? Here, we apply this idea to the notoriously difficult task of galaxy classification: NNs have reached high performance for this task, but what does a neural net (NN) "see" when it classifies galaxies? Are there morphological features that the human eye might overlook that could help with the task and provide new insights? Can we visualize tracers of early evolution, or additionally incorporated spectral data? We present a novel way to summarize and visualize galaxy morphology through the lens of neural networks, leveraging Dataset Distillation, a recent deep-learning methodology with the primary objective to distill knowledge from a large dataset and condense it into a compact synthetic dataset, such that a model trained on this synthetic dataset achieves performance comparable to a model trained on the full dataset. We curate a class-balanced, medium-size high-confidence version of the Galaxy Zoo 2 dataset, and proceed with dataset distillation from our accurate NN-classifier to create synthesized prototypical images of galaxy morphological features, demonstrating its effectiveness. Of independent interest, we introduce a self-adaptive version of the state-of-the-art Matching Trajectory algorithm to automate the distillation process, and show enhanced performance on computer vision benchmarks.

cs.CV↗

arXiv2021

Artificial Intelligence Advances for De Novo Molecular Structure Modeling in Cryo-EM

Cryo-electron microscopy (cryo-EM) has become a major experimental technique to determine the structures of large protein complexes and molecular assemblies, as evidenced by the 2017 Nobel Prize. Although cryo-EM has been drastically improved to generate high-resolution three-dimensional (3D) maps that contain detailed structural information about macromolecules, the computational methods for using the data to automatically build structure models are lagging far behind. The traditional cryo-EM model building approach is template-based homology modeling. Manual de novo modeling is very time-consuming when no template model is found in the database. In recent years, de novo cryo-EM modeling using machine learning (ML) and deep learning (DL) has ranked among the top-performing methods in macromolecular structure modeling. Deep-learning-based de novo cryo-EM modeling is an important application of artificial intelligence, with impressive results and great potential for the next generation of molecular biomedicine. Accordingly, we systematically review the representative ML/DL-based de novo cryo-EM modeling methods. And their significances are discussed from both practical and methodological viewpoints. We also briefly describe the background of cryo-EM data processing workflow. Overall, this review provides an introductory guide to modern research on artificial intelligence (AI) for de novo molecular structure modeling and future directions in this emerging field.

q-bio.BM↗