arXiv subjects

Qingshan Wang

Publications and source records attributed to Qingshan Wang.

4 recordsLinked to original sources

arXiv2026

Black Hole-Galaxy Correlations in Cluster Zoomed-in Simulations: GIZMO-SIMBA and TNG-Cluster

We investigate the co-evolution of supermassive black holes (SMBHs) and central galaxies in massive clusters using the GIZMO-SIMBA and TNG-Cluster zoom-in simulations at $z=0-5$. We find that the distinct subgrid physics of these two models suggest fundamentally different evolutionary pathways. On the one hand, GIZMO-SIMBA, employs torque-limited accretion and predicts a supply-driven scenario where the SMBHs rapidly assemble synchronized with dark matter halo ($M_{200c}$) growth (i.e. the halo mass-BH mass relation is set by $z=3.0$ and similar to the present-day relationship). On the other hand, TNG-Cluster, exhibits a feedback-regulated growth phase delayed by an early thermal suppression. While both models successfully reproduce some local black hole-galaxy scaling relations, they imply significantly different evolution for these relations. Analysis of the BH mass-gas mass ratio relations suggests that TNG-Cluster's isotropic kinetic winds efficiently deplete cold gas, resulting in a "hard quench" of star formation. In the black hole accretion rate (BHAR)-star formation rate (SFR) relation we find that both simulations successfully reproduce the decoupling of BHAR and star formation observed in recent massive cluster ellipticals. The divergent evolutionary trends emphasize the importance of the multiphase intracluster medium; while these subgrid models do not have the necessary resolution and employ distinct formalisms, the sustained BHAR in quenched systems resemble outcomes broadly consistent with modern multiphase feeding paradigms such as chaotic cold accretion in turbulent cluster cores. Furthermore, we demonstrate that for both models, black hole mass is a primary regulator of atomic and molecular gas depletion in galaxy clusters.

astro-ph.GA↗

arXiv2026

Dependence of Equilibrium Propagation Training Success on Network Architecture

The rapid rise of artificial intelligence has led to an unsustainable growth in energy consumption. This has motivated progress in neuromorphic computing and physics-based training of learning machines as alternatives to digital neural networks. Many theoretical studies focus on simple architectures like all-to-all or densely connected layered networks. However, these may be challenging to realize experimentally, e.g. due to connectivity constraints. In this work, we investigate the performance of the widespread physics-based training method of equilibrium propagation for more realistic architectural choices, specifically, locally connected lattices. We train an XY model and explore the influence of architecture on various benchmark tasks, tracking the evolution of spatially distributed responses and couplings during training. Our results show that sparse networks with only local connections can achieve performance comparable to dense networks. Our findings provide guidelines for further scaling up architectures based on equilibrium propagation in realistic settings.

cs.LG↗

arXiv2025

Gamma Analytical Modeling Evolution (GAME) I: The physical implications of deriving the stellar mass functions from z=0 to z=8

The $Γ$ growth model is an effective parameterization employed across various scientific disciplines and scales to depict growth. It has been demonstrated that the cosmic star formation rate density (CSFRD) can also be described broadly by this pattern, i.e. $\frac{dM(T)}{dT} = M_{z,0}\, \times \frac{β^α}{Γ(α)} \, T^{α-1} e^{-β\, T }$ M$_{\odot}$ Gyr$^{-1}$, where $M_{z,0}$ is the stellar mass at $z$ = 0, $α= 3.0$, $β= 0.5 $ Gyr$^{-1}$ and $T$ describes time. We use the identical $Γ$ growth pattern given by the CSFRD to extend the present day (z = 0) stellar mass bins $M_{\ast}(T)$ of the Galaxy Stellar Mass Function (GSMF) and investigate if we are able to reproduce observations for the high redshift GSMFs. Surprisingly, our scheme describes successfully the evolution of the GSMF over 13.5 Gyrs, especially for objects with intermediate and low masses. We observe some deviations that manifest {\it solely} at very high redshifts ($z > 1.5$, i.e. more than 9.5 Gyr ago) and {\it specifically} for very small and exceedingly massive objects. We discuss the possible solutions (e.g. impacts of mergers) for these offsets. Our formalism suggests that the evolution of the GSMF is set by simple (few parameters) and physically motivated arguments. The parameters $β$ and $α$ are theoretically consistent within a multi-scale context and are determined from the dynamical time scale ($β$) and the radial distribution of the accreting matter ($α$). We demonstrate that both our formalism and state-of-the-art simulations are consistent with recent GSMFs derived from JWST data at high redshifts.

astro-ph.GA↗

arXiv2024

Training Coupled Phase Oscillators as a Neuromorphic Platform using Equilibrium Propagation

Given the rapidly growing scale and resource requirements of machine learning applications, the idea of building more efficient learning machines much closer to the laws of physics is an attractive proposition. One central question for identifying promising candidates for such neuromorphic platforms is whether not only inference but also training can exploit the physical dynamics. In this work, we show that it is possible to successfully train a system of coupled phase oscillators - one of the most widely investigated nonlinear dynamical systems with a multitude of physical implementations, comprising laser arrays, coupled mechanical limit cycles, superfluids, and exciton-polaritons. To this end, we apply the approach of equilibrium propagation, which permits to extract training gradients via a physical realization of backpropagation, based only on local interactions. The complex energy landscape of the XY/ Kuramoto model leads to multistability, and we show how to address this challenge. Our study identifies coupled phase oscillators as a new general-purpose neuromorphic platform and opens the door towards future experimental implementations.

cs.ET↗