arXiv subjects

Yikai Liu

Publications and source records attributed to Yikai Liu.

10 recordsLinked to original sources

arXiv2026

The VariableTNG project: mass-dependent regulation of galaxy morphology by baryonic feedback

Galaxy morphology is shaped by both assembly history and baryonic processes, but their relative roles remain uncertain. We use the VariableTNG (VTNG) simulation suite to investigate how variations in baryonic feedback regulate galaxy morphology while keeping the initial conditions fixed. VTNG consists of cosmological magnetohydrodynamic simulations performed with the moving-mesh code {\sc AREPO}, varying eight parameters governing stellar and AGN feedback. At $z=1$, we characterize morphology using $\kappa_{\rm co}$, $v/\sigma$, and the axis ratio $c/a$. We find substantial morphological diversity across feedback models, with the dominant mechanism strongly dependent on stellar mass. For $M_\ast \lesssim 10^{11}\,{\rm M_\odot}$, morphology is primarily controlled by the supernova temperature $T_{\rm SN}$: larger $T_{\rm SN}$ delays early star formation, promotes a denser and more rotationally supported gas reservoir, and favours subsequent disc growth through in-situ star formation. At higher masses, morphology becomes increasingly sensitive to AGN feedback, particularly the quasar-mode coupling efficiency $\epsilon_{\rm f,high}$. Higher $\epsilon_{\rm f,high}$ suppresses early black hole growth, thereby weakening subsequent radio-mode feedback associated with gas depletion and loss of rotational support. The resulting morphology--mass relation is non-monotonic, with maximum rotational support at intermediate stellar masses. Our results demonstrate that baryonic feedback regulates galaxy morphology through distinct mass-dependent pathways, with stellar feedback dominating at lower masses and AGN self-regulation becoming increasingly important at the massive end.

astro-ph.GA↗

arXiv2026

The VariableTNG project: how baryonic mechanisms shape galaxy properties

We use 50 Sobol-sampled VariableTNG simulations, varying eight subgrid parameters at fixed cosmology and initial conditions, to determine which baryonic processes regulate galaxies and black holes at $z=6$ and $z=8$. We compare the simulations with recent high-redshift measurements of the stellar mass function, star-forming main sequence, stellar and gas-phase mass-metallicity relations, stellar mass-size relation, and black-hole host and accretion properties. The simulations reproduce several broad trends in these observables, although differences remain in gas-phase metallicity, galaxy size, and the most extreme black-hole populations. Given the substantial uncertainties in both the physical modelling and the observational inference of high-redshift galaxy properties, we regard these differences as diagnostic tensions rather than definitive model failures. Random-forest analyses reveal a clear hierarchy in parameter sensitivity. The abundance of low-mass galaxies is regulated primarily by stellar feedback, particularly the supernova temperature $T_{\mathrm{SN}}$ and thermal wind fraction $\tau_{\mathrm{w}}$, whereas the sensitivity of the scatter in the star-forming main sequence is weaker and redshift dependent. The high-accretion tail of black-hole growth depends on black-hole seeding and feedback parameters, but also on $T_{\mathrm{SN}}$, suggesting that stellar feedback indirectly regulates rapid black-hole growth through its impact on the available gas supply. Although cosmic variance can obscure these intrinsic responses in independent small volumes, our controlled experiment identifies stellar feedback as a common physical link between early low-mass galaxy formation and rapid black-hole growth.

astro-ph.GA↗

arXiv2026

The VariableTNG project: Unveiling the physical drivers of galaxy quenching

Understanding the physical processes that regulate galaxy quenching is a key challenge in galaxy formation and evolution. The VariableTNG (VTNG) project provides a laboratory to investigate these processes, as it systematically varies eight parameters of galaxy formation while keeping the initial conditions fixed, allowing the effects of individual feedback prescriptions to be isolated. We use interpretable machine-learning techniques as a tool to identify the parameters that most strongly regulate the quenched galaxy fraction. Our goal is to quantify the relative importance of the galaxy formation and feedback parameters that regulate the quenched galaxy fraction at z=0, determine how their influence changes across stellar mass and environment. We compute the quenched galaxy fraction for 26 VTNG boxes as a function of stellar mass, black hole mass, and gas mass, considering both the total galaxy population and separate samples of central and satellite galaxies. We train Random Forest regressors to predict the variation of the quenched fraction relative to the TNG100-1 model and use SHAP values to quantify both the magnitude and direction of the influence of each parameter. Our analysis reveals that only a small subset of the VTNG parameters dominates the variance of the quenched fraction. The stellar feedback wind parameter is the primary driver at low stellar masses, while its importance gradually shifts toward AGN-related parameters at higher masses. The supernova temperature also plays an important role at both extremes of the stellar-mass range. This transition persists when the galaxy population is divided into central and satellite systems. Comparisons with observational measurements further suggest that variations in these feedback parameters may contribute to the discrepancies between the fiducial TNG100-1 model and the observed passive galaxy population.

astro-ph.GA↗

arXiv2026

SE(3)-MeanFlow: Few-Step Protein Backbone Generation on Lie Groups

Generative modeling of protein backbones promises the de novo design of proteins with prescribed structural and functional properties. Existing diffusion and flow-matching models produce high-quality backbones on SE(3)^N, but inference requires numerically integrating an ODE over hundreds of network evaluations, each involving a Lie group exponential map - a bottleneck for high-throughput design campaigns. We introduce SE(3)-MeanFlow, a few-step generative framework that extends MeanFlow from Euclidean space to the Lie group geometry of protein frames. Working natively in the Lie algebra so(3) and in R^3, we derive closed-form average-velocity identities for rotations and translations, giving simulation-free training targets. We further introduce an SE(3) alpha-Flow objective that removes the Jacobian-vector product from the rotation branch and serves as a warm-up stage, after which training switches to a small-t stabilized MeanFlow loss that is used for the remainder of pretraining and for rectification-based post-training. In protein backbone generation, SE(3)-MeanFlow matches or exceeds flow-matching baselines that use several times more sampling steps, and its advantage widens in the few-step regime, where rectification lets it lead at every matched budget - at a modest cost in diversity.

cs.LG↗

arXiv2025

ProTDyn: a foundation Protein language model for Thermodynamics and Dynamics generation

Molecular dynamics (MD) simulation has long been the principal computational tool for exploring protein conformational landscapes and dynamics, but its application is limited by high computational cost. We present ProTDyn, a foundation protein language model that unifies conformational ensemble generation and multi-timescale dynamics modeling within a single framework. Unlike prior approaches that treat these tasks separately, ProTDyn allows flexible independent and identically distributed (i.i.d.) ensemble sampling and dynamic trajectory simulation. Across diverse protein systems, ProTDyn yields thermodynamically consistent ensembles, faithfully reproduces dynamical properties over multiple timescales, and generalizes to proteins beyond its training data. It offers a scalable and efficient alternative to conventional MD simulations.

physics.bio-ph↗

arXiv2024

TOP-Nav: Legged Navigation Integrating Terrain, Obstacle and Proprioception Estimation

Legged navigation is typically examined within open-world, off-road, and challenging environments. In these scenarios, estimating external disturbances requires a complex synthesis of multi-modal information. This underlines a major limitation in existing works that primarily focus on avoiding obstacles. In this work, we propose TOP-Nav, a novel legged navigation framework that integrates a comprehensive path planner with Terrain awareness, Obstacle avoidance and close-loop Proprioception. TOP-Nav underscores the synergies between vision and proprioception in both path and motion planning. Within the path planner, we present and integrate a terrain estimator that enables the robot to select waypoints on terrains with higher traversability while effectively avoiding obstacles. In the motion planning level, we not only implement a locomotion controller to track the navigation commands, but also construct a proprioception advisor to provide motion evaluations for the path planner. Based on the close-loop motion feedback, we make online corrections for the vision-based terrain and obstacle estimations. Consequently, TOP-Nav achieves open-world navigation that the robot can handle terrains or disturbances beyond the distribution of prior knowledge and overcomes constraints imposed by visual conditions. Building upon extensive experiments conducted in both simulation and real-world environments, TOP-Nav demonstrates superior performance in open-world navigation compared to existing methods.

cs.RO↗

arXiv2024

The origin of lopsided satellite galaxy distribution around isolated systems in MillenniumTNG

Dwarf satellites in galaxy groups are distributed in an anisotropic and asymmetric manner, which is called the ``lopsided satellite distribution''. This lopsided signal has been observed not only in galaxy pairs but also in isolated systems. However, the physical origin of the lopsided signal in isolated systems is still unknown. In this work, we investigate this in the state-of-the-art hydrodynamical simulation of the MillenniumTNG Project by tracing each system back to high redshift. We find that the lopsided signal is dominated by satellites located in the outer regions of the halo and is also dominated by recently accreted satellites. The lopsided signal originates from the anisotropic accretion of galaxies from the surrounding large-scale structure and that, after accretion, the nonlinear evolution of satellites inside the dark-matter halo weakens the lopsidedness. The signal decreases as cosmic time passes because of a competition between anisotropic accretion and internal evolution within dark matter halos. Our findings provide a useful perspective for the study of galaxy evolution, especially for the origin of the spatial satellite galaxy distributions.

astro-ph.CO↗

arXiv2023

Unbiasing Enhanced Sampling on a High-dimensional Free Energy Surface with Deep Generative Model

Biased enhanced sampling methods utilizing collective variables (CVs) are powerful tools for sampling conformational ensembles. Due to high intrinsic dimensions, efficiently generating conformational ensembles for complex systems requires enhanced sampling on high-dimensional free energy surfaces. While methods like temperature-accelerated molecular dynamics (TAMD) can adopt many CVs in a simulation, unbiasing the simulation requires accurate modeling of a high-dimensional CV probability distribution, which is challenging for traditional density estimation techniques. Here we propose an unbiasing method based on the score-based diffusion model, a deep generative learning method that excels in density estimation across complex data landscapes. We test the score-based diffusion unbiasing method on TAMD simulations. The results demonstrate that this unbiasing approach significantly outperforms traditional unbiasing methods, and can generate accurate unbiased conformational ensembles for simulations with a number of CVs higher than usual ranges.

cs.LG↗

arXiv2023

Backdiff: a diffusion model for generalized transferable protein backmapping

Coarse-grained (CG) models play a crucial role in the study of protein structures, protein thermodynamic properties, and protein conformation dynamics. Due to the information loss in the coarse-graining process, backmapping from CG to all-atom configurations is essential in many protein design and drug discovery applications when detailed atomic representations are needed for in-depth studies. Despite recent progress in data-driven backmapping approaches, devising a backmapping method that can be universally applied across various CG models and proteins remains unresolved. In this work, we propose BackDiff, a new generative model designed to achieve generalization and reliability in the protein backmapping problem. BackDiff leverages the conditional score-based diffusion model with geometric representations. Since different CG models can contain different coarse-grained sites which include selected atoms (CG atoms) and simple CG auxiliary functions of atomistic coordinates (CG auxiliary variables), we design a self-supervised training framework to adapt to different CG atoms, and constrain the diffusion sampling paths with arbitrary CG auxiliary variables as conditions. Our method facilitates end-to-end training and allows efficient sampling across different proteins and diverse CG models without the need for retraining. Comprehensive experiments over multiple popular CG models demonstrate BackDiff's superior performance to existing state-of-the-art approaches, and generalization and flexibility that these approaches cannot achieve. A pretrained BackDiff model can offer a convenient yet reliable plug-and-play solution for protein researchers, enabling them to investigate further from their own CG models.

q-bio.QM↗

arXiv2023

EASE: An Easily-Customized Annotation System Powered by Efficiency Enhancement Mechanisms

The performance of current supervised AI systems is tightly connected to the availability of annotated datasets. Annotations are usually collected through annotation tools, which are often designed for specific tasks and are difficult to customize. Moreover, existing annotation tools with an active learning mechanism often only support limited use cases. To address these limitations, we present EASE, an Easily-Customized Annotation System Powered by Efficiency Enhancement Mechanisms. \sysname provides modular annotation units for building customized annotation interfaces and also provides multiple back-end options that suggest annotations using (1) multi-task active learning; (2) demographic feature based active learning; (3) a prompt system that can query the API of large language models. We conduct multiple experiments and user studies to evaluate our system's flexibility and effectiveness. Our results show that our system can meet the diverse needs of NLP researchers and significantly accelerate the annotation process.

cs.HC↗