arXiv subjects

Renyu Luo

Publications and source records attributed to Renyu Luo.

4 recordsLinked to original sources

arXiv2026

Effect of quark-hadron phase transitions on the neutron star mass distribution

The neutron star (NS) mass distribution serves as a crucial probe of the dense matter equation of state (EOS) and core-collapse supernova explosion mechanisms. Although observations reveal a bimodal NS mass distribution, existing theoretical simulations often struggle to naturally reproduce the high-mass peak, and the impact of the quark-hadron phase transition (PT) in high-density nuclear matter remains largely unexplored. Employing the 1D general-relativistic hydrodynamics code GR1D with hybrid EOSs incorporating a first-order PT (RDF1.1 and RDF1.2), we systematically simulate core-collapse supernovae for progenitors with zero-age main-sequence masses ranging from 13 to 120 $M_\odot$. Our results reveal a threefold impact of the PT onset density on the final supernova fate: in low-compactness progenitors, an early PT truncates accretion and produces less massive NSs; in the critical-compactness regime, it facilitates successful explosions; and in high-compactness progenitors, it accelerates black hole formation. Compared with the $\sim 1.4\,M_\odot$ peak typical of the conventional core-collapse scenario, the inclusion of the PT produces NSs with masses centered around $1.70\,M_\odot$ for RDF1.1 and within $1.55$--$1.70\,M_\odot$ for RDF1.2. Furthermore, we identify a novel formation channel whereby low-compactness progenitors with failed initial shocks can form more massive NSs via a PT-induced second shock. Due to the fixed nature of the PT boundary, the final NS masses exhibit a pronounced clustering effect. This study elucidates the critical role of the quark-hadron PT in shaping the NS mass distribution, providing important astrophysical constraints on the properties of high-density nuclear matter and the formation mechanism of massive NSs.

astro-ph.HE↗

arXiv2026

LISA Double White Dwarfs in Triple Systems

Double white dwarfs (DWDs) in the Laser Interferometer Space Antenna (LISA) band are major Galactic gravitational-wave sources, but many tight DWD progenitors may reside in hierarchical triples. Triple evolution can alter both the production rate and parameter distributions of LISA DWDs. Using the Multiple Star Evolution (MSE) code, we study how the adiabatic mass-loss model (Ge model) and the SCATTER formalism for triple common-envelope (TCE) evolution affect triple-channel LISA DWD formation. Our main model evolves 10^5 triples with both ingredients. Three control models, each with 10^4 triples, vary the mass-transfer stability criterion to the original MSE polytropic model, remove SCATTER, or adopt low metallicity (Z=0.001). We also evolve 10^6 isolated binaries with the same framework as a binary-channel reference. Systems that are dynamically unstable under the polytropic model can instead undergo stable Roche-lobe overflow under the Ge model. This sustained mass transfer modifies the donor envelope, reduces its binding energy, and helps the subsequent common-envelope phase form a close DWD rather than a merger. The Ge model increases the triple-channel LISA DWD yield by about 60% relative to the polytropic model, while SCATTER changes the integrated yield by only about 3%. After residence-time weighting, triples contribute about 1.3e7 LISA-band DWDs, including 1.4e4 individually resolvable systems. In the combined triple-plus-binary normalization, the binary channel contributes 3.1e6 LISA-band DWDs and 4.7e3 resolvable systems; these are the binary share of the combined population, not the binary-only Galactic estimate. The main triple model has a flatter, higher-q mass-ratio distribution than the polytropic-model triples, is dominated by He-CO and He-He binaries, and produces rare eccentric systems only when a tertiary star remains bound.

astro-ph.SR↗

arXiv2025

The influence of rotation and metallicity on the explodability of massive stars

During the late stages of massive stellar evolution, failed supernovae (FSN) may form through core-collapse processes. The traditional evaluation criterion $ξ_{2.5}$ $=$ 0.45, primarily established using non-rotating progenitor models, suffers from significant inaccuracies when applied to rotating pre-supernova systems. The effects of metallicity and rotation on the explodability landscapes of massive stars lack robust quantification. We aim to investigate how rotation and metallicity influence the explodability of massive stars. We investigate how rotation and metallicity affect stellar explodability using MESA simulations with initial rotational velocities of $0$, $300$, and $600~\mathrm{km,s^{-1}}$ at three metallicities ($Z_{\odot}$, $1/10,Z_{\odot}$, $1/50,Z_{\odot}$). Core-collapse phases are simulated with GR1D to determine critical heating efficiencies. Our results yield revised $ξ_{2.5}$ criteria: 0.45 for non-rotating models; 0.48 for $300~\mathrm{km,s^{-1}}$; 0.47 for $600~\mathrm{km,s^{-1}}$ at solar metallicity; and 0.59 for low-metallicity models. Chemically homogeneous evolution in rapidly rotating low-metallicity stars significantly raises the compactness limit for successful explosions and narrows the zero-age main sequence mass range for failed supernovae. Rotation substantially affects the explodability of low-metallicity massive stars, underscoring the importance of incorporating rotational effects in models of core-collapse supernova progenitors.

astro-ph.SR↗

arXiv2025

Multiscale super-resolution reconstruction of fluid flows with deep neural networks

We present a novel multiscale super-resolution framework (SRLBM) that applies deep learning directly to the mesoscopic density distribution functions of the lattice Boltzmann method for high-fidelity flow reconstruction. Two neural network architectures, a standard convolutional neural network (CNN) and a deeper residual dense network (RDN), are trained to upscale distribution functions from coarse grids by factors of 2, 4 and 8, and then recover velocity, pressure, and vorticity from a single model. For flow past a single cylinder at $\mathrm{Re}=100$, RDN reduces the mean relative error in distribution functions by an order of magnitude compared to CNN and avoids spurious pressure oscillations and vorticity smoothing that affect interpolation and simpler networks. To examine the generalization ability, both models are trained using data from the flow past two cylinders of diameter $d$ at a spanwise distance between the centers of $1.5d$ and a Reynolds number of 200. They are then applied without retraining to wake configurations with distances ranging from $2.0d$ to $3.0d$. In these tests, the mean errors remain essentially unchanged across all distances. However, RDN consistently produces sharper shear-layer roll-ups and secondary eddies. These results demonstrate that super-resolving mesoscopic distribution functions yields richer and more transferable features than operating on macroscopic fields alone. By integrating kinetic theory with deep learning, SRLBM offers a compelling alternative for fluid flow reconstruction, enabling a single model to simultaneously recover multiple high-fidelity flow fields while substantially reducing computational cost.

physics.flu-dyn↗