arXiv subjects

Ryusei R. Kano

Publications and source records attributed to Ryusei R. Kano.

4 recordsLinked to original sources

arXiv2026

Topological Signatures of AGN Feedback in the Simba Simulations

Active galactic nuclei (AGN) feedback can alter the abundance and spatial distribution of galaxies within large-scale structure. We apply topological data analysis (TDA) to Simba hydrodynamical simulations with varying AGN feedback at $z=0$ to determine which galaxy populations, environments, and spatial scales are most affected. The galaxy two-point correlation function is most sensitive to feedback at $r<1 h^{-1} Mpc$. We then use Betti curves, persistence diagrams, and the 2-Wasserstein distance to quantify feedback-dependent changes in topology. Persistence diagrams show clearer differences than Betti curves, particularly for satellite galaxies. For satellites, loop-like features show significant differences at $α=0.1$--$0.4 h^{-1} Mpc$ for all feedback channels, together with a weaker cumulative difference at $α=1$--$3 h^{-1} Mpc$. The differences are strongest for satellites in quenched halos with $M_h\gtrsim10^{12} M_\odot$. Optimal matching shows that they are driven mainly by shifts in the birth and death scales of loop-like features rather than by their appearance or disappearance. These results indicate that persistent homology can provide complementary constraints on AGN feedback and environmental quenching in galaxy surveys.

astro-ph.GA↗

arXiv2026

Joint State-and-Dynamics Inference for Galaxy Population Evolution on an Effective Manifold

Galaxy surveys provide noisy, incomplete, selection-affected population snapshots rather than complete evolutionary histories. We formulate galaxy evolution as joint inference of effective physical states, their population intensity, and cross-epoch dynamics. An effective state is a coarse-grained description defined by predictive adequacy and distinguished from measured variables and learned representations. The intrinsic population is a finite non-negative measure evolving through one-galaxy transport, source and removal terms, and nonlocal two-to-one merger jumps. A sub-probability observational kernel maps this population to survey catalogs and supplies the likelihood for state inference. Projected luminosity and stellar-mass functions generally do not obey closed dynamics and cannot identify the underlying channels alone. In a restricted IllustrisTNG proof of concept, a finite-time non-merger kernel and reduced merger operator are calibrated on training root merger trees and evaluated on held-out lineages. Dynamical propagation improves the normalized later mass--sSFR distribution relative to a static baseline, while the merger channel captures number loss from disappearing secondary progenitors. Under noisy and censored mock observations, the propagated prior improves posterior-stacked reconstruction of the latent population relative to a frozen-static prior. The framework provides an observation-directed, simulation-assisted route to galaxy state-and-dynamics inference.

astro-ph.GA↗

arXiv2026

cloelib: A Flexible Python Library for Computing Cosmological Observables in the Euclid Era

cloelib is a Python library developed to compute cosmological observables within the Cosmology Likelihood for Observables in Euclid (CLOE) ecosystem (cloe-org). As cosmology enters a precision era driven by galaxy survey missions such as Euclid, there is a growing need for flexible, efficient, and differentiable software capable of supporting next-generation inference pipelines. cloelib addresses these demands through a modular architecture that interfaces seamlessly with established Boltzmann solvers whilst incorporating JAX-based automatic differentiation to enable gradient-based methods. The library defines consistent protocols for background evolution, perturbations, and non-linear structure formation, and supports a wide range of observables, including photometric and spectroscopic large-scale structure probes, as well as cross-correlations with the Cosmic Microwave Background and galaxy clusters. In its finalised form, cloelib is intended to serve as the reference theory computation infrastructure for Euclid's first cosmological release, bridging traditional numerical cosmology with modern optimisation techniques and emerging machine learning approaches to inference.

astro-ph.CO↗

arXiv2026

Resolving the Dust Budget Crisis at $z \sim 8$ with Optically Thick, High-Density Molecular Clumps: MACS0416_Y1

Dust plays a crucial role in galaxy evolution by shaping the spectral energy distribution (SED) and star formation history. However, standard models often underestimate the infrared luminosity of high-redshift galaxies ($z \sim 8$), leading to the so-called dust budget crisis. In this work, we modify the theoretical framework by focusing on compact star-forming clumps in the interstellar medium. Motivated by the observed compactness of high-z galaxies, we treat the cold neutral medium density as a free parameter. Our analysis reveals that the ISM must reach extreme densities ($n_{\text{H,CNM}} \sim 7.5 \times 10^3 \, \mathrm{cm}^{-3}$). This enhances UV photon trapping, accelerates dust processing in dense gas, and reduces dust destruction by supernova shocks. Our model successfully reproduces the observed UV-to-FIR SED of MACS0416_Y1 ($z = 8.312$). A grain-size-resolved treatment further shows that the warm IR emission is dominated by intermediate-size grains ($a = 0.01$ - $0.1\,μ$m), which contribute about 89% of the luminosity near the SED peak and in the ALMA Band~9 continuum. These grains are nearly in thermal equilibrium at characteristic temperatures of $\sim 70$ K, while the largest grains remain cooler and the smallest grains exhibit a high-temperature tail with low probability. We conclude that extreme ISM densities can alleviate the dust budget crisis by promoting efficient UV photon trapping and rapid dust evolution, thereby increasing dust mass and producing a multi-temperature grain population.

astro-ph.GA↗