arXiv subjects

Konstantinos Antoniadis

Publications and source records attributed to Konstantinos Antoniadis.

5 recordsLinked to original sources

arXiv2026

The Stellar Winds Atlas I: Current uncertainties in mass-loss rates

Stellar winds are a major source of uncertainty in understanding the life and deaths of massive stars. Across studies in the field, prescriptions for stellar winds differ substantially in both their physical assumptions and implementation, making them a dominant contributor to model-to-model variation. In this work, we present a systematic analysis of the physical assumptions underlying commonly adopted wind prescriptions for optically thin and optically thick winds of hot stars, as well as the winds of cool supergiants. Our analysis reveals substantial discrepancies across all regimes: predicted mass-loss rates for optically thin winds differ by more than an order of magnitude, while rates for cool supergiants vary by several orders of magnitude, with even wider uncertainties arising in extrapolation regimes beyond the Humphreys-Davidson limit. These disparities introduce significant ambiguity into the predicted formation of Wolf-Rayet (WR) stars, a problem further compounded by the inconsistent application of transition criteria. A central issue is the "cool Wolf-Rayet problem", a temperature regime where the classical electron-scattering Eddington factor ($Γ_{\rm e}$) loses physical consistency. Because this factor is widely used to determine WR mass-loss rates, its failure forces models to rely on uncertain extrapolations and ad-hoc corrections. We conclude that the dominant stellar wind uncertainties arise from a mismatch between the physical assumptions in stellar wind models and the structure of the stars to which they are applied. Our framework clarifies the origins of current theoretical discrepancies and identifies the key physical bottlenecks that must be addressed to improve mass-loss modeling for massive stars.

astro-ph.SR↗

arXiv2026

The Stellar Winds Atlas II: Black Hole Formation at Solar Metallicity

Stellar winds are a primary source of uncertainty in predicting the masses of black holes (BHs) from massive stars. At solar metallicity, theoretical models lead to widely divergent results due to differing wind prescriptions. A key obstacle remains the lack of systematic investigations across a common parameter space. To address this, we construct a ``Wind Atlas'' using detailed 1D MESA stellar evolution models and population synthesis techniques to estimate the Galactic population of solar metallicity BH progenitors. We systematically investigate 14 distinct wind models, ranging from the most traditional and widespread prescriptions to the most recent. By evaluating stellar evolution across this extensive grid, we show that the final BH mass is dictated by a fundamental bifurcation: whether a star collapses as a cool supergiant or is first stripped of its envelope to become a Wolf-Rayet (WR) star. If a star enters the WR stage, its strong thick winds dominate, making the final mass sensitive to the WR wind prescription while largely erasing the memory of its prior mass-loss history. Conversely, stars that face core collapse as supergiants form significantly more massive BHs, producing a mass peak around an initial mass of 40 $M_\odot$. Rather than simply reproducing these divergent outcomes, our comprehensive evaluation demonstrates that this bifurcation is universally controlled by the highly uncertain mass loss during the cool supergiant phase. This framework strongly constrains the problem of BH mass prediction by identifying two key bottlenecks for future studies: envelope stripping efficiency and WR mass-loss rates. Our atlas provides a clear baseline for interpreting current theoretical discrepancies and testing wind models against observational constraints, such as the Galactic WR/OB population ratio.

astro-ph.SR↗

arXiv2026

A homogeneous view of asymptotic giant branch carbon stars as seen by Gaia

Carbon stars on the asymptotic giant branch are major contributors to galactic dust enrichment, with gas mass-loss rates up to 1e-4 Msun/yr. We present a homogeneous spectral energy distribution analysis of the Gaia DR3 Golden Sample of carbon stars in the Milky Way and Magellanic Clouds. Our dataset includes 14,747 sources with multi-band photometry from Gaia, 2MASS, and WISE, combined with recent distance and extinction estimates. For a subsample of 2,494 Mira variables, we model multi-band light curves to derive accurate mean magnitudes. Stellar and circumstellar parameters are obtained by fitting observations with a large grid of synthetic spectra computed with the DUSTY radiative transfer code using COMARCS atmospheres. We derive effective temperature, optical depth, and gas mass-loss rate for each source. The distributions peak around Teff = 3150 K, with mass-loss rates spanning 1e-11 to 1e-4 Msun/yr and inner dust temperatures near 1000 K. We find a correlation between variability amplitude and mass-loss rate. This framework provides a statistically robust view of carbon stars across environments with different metallicities. Apparent environmental dependencies are influenced by luminosity distributions and selection effects rather than purely intrinsic metallicity differences. The combined Gaia and WISE selection limits the detection of both highly obscured and faint Magellanic Cloud sources, but the observed trends remain significant within the sampled populations.

astro-ph.SR↗

arXiv2023

Evolved Massive Stars at Low-metallicity V. Mass-Loss Rate of Red Supergiant Stars in the Small Magellanic Cloud

We assemble the most complete and clean red supergiant (RSG) sample (2,121 targets) so far in the Small Magellanic Cloud (SMC) with 53 different bands of data to study the MLR of RSGs. In order to match the observed spectral energy distributions (SEDs), a theoretical grid of 17,820 Oxygen-rich models (``normal'' and ``dusty'' grids are half-and-half) is created by the radiatively-driven wind model of the DUSTY code, covering a wide range of dust parameters. We select the best model for each target by calculating the minimal modified chi-square and visual inspection. The resulting MLRs from DUSTY are converted to real MLRs based on the scaling relation, for which a total MLR of $6.16\times10^{-3}$ $M_\odot$ yr$^{-1}$ is measured (corresponding to a dust-production rate of $\sim6\times10^{-6}$ $M_\odot$ yr$^{-1}$), with a typical MLR of $\sim10^{-6}$ $M_\odot$ yr$^{-1}$ for the general population of the RSGs. The complexity of mass-loss estimation based on the SED is fully discussed for the first time, indicating large uncertainties based on the photometric data (potentially up to one order of magnitude or more). The Hertzsprung-Russell and luminosity versus median absolute deviation diagrams of the sample indicate the positive relation between luminosity and MLR. Meanwhile, the luminosity versus MLR diagrams show a ``knee-like'' shape with enhanced mass-loss occurring above $\log_{10}(L/L_\odot)\approx4.6$, which may be due to the degeneracy of luminosity, pulsation, low surface gravity, convection, and other factors. We derive our MLR relation by using a third-order polynomial to fit the sample and compare our result with previous empirical MLR prescriptions. Given that our MLR prescription is based on a much larger sample than previous determinations, it provides a more accurate relation at the cool and luminous region of the H-R diagram at low-metallicity compared to previous studies.

astro-ph.SR↗

arXiv2022

Using machine learning to investigate the populations of dusty evolved stars in various metallicities

Mass loss is a key property to understand stellar evolution and in particular for low-metallicity environments. Our knowledge has improved dramatically over the last decades both for single and binary evolutionary models. However, episodic mass loss although definitely present observationally, is not included in the models, while its role is currently undetermined. A major hindrance is the lack of large enough samples of classified stars. We attempted to address this by applying an ensemble machine-learning approach using color indices (from IR/Spitzer and optical/Pan-STARRS photometry) as features and combining the probabilities from three different algorithms. We trained on M31 and M33 sources with known spectral classification, which we grouped into Blue/Yellow/Red/B[e] Supergiants, Luminous Blue Variables, classical Wolf-Rayet and background galaxies/AGNs. We then applied the classifier to about one million Spitzer point sources from 25 nearby galaxies, spanning a range of metallicites ($1/15$ to $\sim3~Z_{\odot}$). Equipped with spectral classifications we investigated the occurrence of these populations with metallicity.

astro-ph.GA↗