Galaxy formation and evolution

Abstract:

ABSTRACT We investigate the evolution of black holes on orbits with small inclinations ($i < 2^\circ$) to the gaseous discs of active galactic nuclei (AGNs). We perform 3D adiabatic hydrodynamic simulations within a shearing frame, studying the damping of inclination by black hole-gas gravitation. We find that for objects with $i< 3H_0R_0^{-1}$, where $H_0R_0^{-1}$ is the disc aspect ratio, the inclination lost per mid-plane crossing is proportional to the inclination preceding the crossing, resulting in a net exponential decay in inclination. For objects with $i>3H_0R_0^{-1}$, damping efficiency decreases for higher inclinations. We consider a variety of different AGN environments, finding that damping is stronger for systems with a higher ambient Hill mass: the initial gas mass within the BH sphere of influence. We provide a fitting formula for the inclination changes as a function of Hill mass. We find reasonable agreement between the damping driven by gas gravity in the simulations and the damping driven by accretion under a Hill-limited Bondi–Hoyle–Lyttleton prescription. We find that gas dynamical friction consistently overestimates the strength of damping, especially for lower inclination systems, by at least an order of magnitude. For regions in the AGN disc where coplanar binary black hole formation by gas dissipation is efficient, we find that the simulated damping time-scales are especially short with $\tau _d < 10P_\mathrm{SMBH}$. We conclude that as the time-scales for inclination damping are shorter than the expected interaction time between isolated black holes, the vast majority of binaries formed from gas capture should form from components with negligible inclination to the AGN disc.

Abstract:

ABSTRACT We investigate the unusual emission-line luminosity ratios observed in the JWST Deep Extragalactic Survey (JADES) NIRSpec spectroscopy of GN-z11, which reveal exceptionally strong emission lines and a significant detection of the rarely observed N iii] $\lambda 1748-1753$, multiplet. These features suggest an elevated N/O abundance, challenging existing models of stellar populations and nebular emission. To assess whether Wolf–Rayet (WR) stars can account for the observed line ratios, we construct a suite of stellar and nebular models incorporating high-resolution stellar spectral libraries, enabling a more accurate treatment of WR evolution and its influence on the ionizing radiation field. We find that the inclusion of WR stars is essential for reproducing the observed position of GN-z11 in the C iii]/He ii versus C iii]/C iv diagnostic plane, resolving discrepancies from previous studies. The model-derived metallicity (0.07 $\lesssim$ Z/Z$_{\odot }\lesssim$ 0.15), ionization parameter ($\log \, U$$\approx$−2), and stellar ages are consistent with the literature estimates. However, our models underpredict the N iii/O iii] ratio, suggesting that WR stars alone cannot fully explain the nitrogen enrichment. This suggests that additional mechanisms, such as rapid chemical enrichment in a young, metal-poor environment, may be necessary to explain the nitrogen excess. While our models successfully reproduce most observed line ratios, further refinements to the models are needed to fully characterize the stellar populations and the enrichment processes of high-redshift galaxies like GN-z11.

Abstract:

Abstract Adaptive binning is a crucial step in the analysis of large astronomical datasets, such as those from integral-field spectroscopy, to ensure a sufficient signal-to-noise ratio ($\mathcal {S/N}$) for reliable model fitting. However, the widely-used Voronoi-binning method and its variants suffer from two key limitations: they scale poorly with data size, often as $\mathcal {O}(N^2)$, creating a computational bottleneck for modern surveys, and they can produce undesirable non-convex or disconnected bins. I introduce PowerBin, a new algorithm that overcomes these issues. I frame the binning problem within the theory of optimal transport, for which the solution is a Centroidal Power Diagram (CPD), guaranteeing convex bins. Instead of formal CPD solvers, which are unstable with real data, I develop a fast and robust heuristic based on a physical analogy of packed soap bubbles. This method reliably enforces capacity constraints even for non-additive measures like $\mathcal {S/N}$ with correlated noise. I also present a new bin-accretion algorithm with $\mathcal {O}(N\log N)$ complexity, removing the previous bottleneck. The combined PowerBin algorithm scales as $\mathcal {O}(N\log N)$, making it about two orders of magnitude faster than previous methods on million-pixel datasets. I demonstrate its performance on a range of simulated and real data, showing it produces high-quality, convex tessellations with excellent $\mathcal {S/N}$ uniformity. The public Python implementation provides a fast, robust, and scalable tool for the analysis of modern astronomical data.