Search arXiv⌕ Search

arXiv · 2609.31362

The Molecular Circumstellar Environments of Red Supergiants in Galactic Open Clusters

Abstract

We aim to expand the sample of RSGs with direct measurements of circumstellar gas through millimeter observations of two of the largest coeval RSG populations in the Galaxy: the open clusters RSGC1 and RSGC2. We present an interferometric molecular line study of 28 RSGs in RSGC1 and RSGC2 with ALMA. The primary targeted emission is CO $J=2-1$ and continuum, with additional thermal and maser SiO lines observed for RSGC2 targets. We measure terminal expansion velocities, systemic velocities, emission-region sizes, and provide updated dust models and luminosities using the new long-wavelength constraints. Finally, we perform 1D radiative transfer modeling of CO rotational-line emission to estimate mass-loss rates. We detect $^{12}$CO toward six RSGs in RSGC2 and five in RSGC1. Except for DFK 52, the spatial distribution of all $^{12}$CO emission is relatively compact or unresolved. The obtained mass-loss rates range from $\log(\dot{M}/M_\odot\,\mathrm{yr}^{-1})=-5.5$ to $-3$, but are highly uncertain due to a lack of stringent constraints on the envelope sizes and temperatures. The lower-limit $\dot{M}$-values we obtain are high compared to standard empirical prescriptions and favor dust models assuming a radiatively driven wind. DFK 49 is a clear outlier: its revised luminosity is the lowest in both clusters, yet it harbors a strong wind with $\dot{M}>10^{-5}\,M_\odot\,\mathrm{yr}^{-1}$. The CO emission regions are much smaller than expected from nearby RSGs and theoretical models, and suggest a unique phase structure for the CSM around RSGs in cluster environments. This work demonstrates the mutual utility of molecular line and dust observations for constraining RSG winds, while highlighting the need for future spatially resolved, multi-tracer observations of RSGC targets to establish the physical structure of their circumstellar environments and improve mass-loss determinations.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mark A. Siebert, Elvire De Beck, Guillermo Quintana-Lacaci, Theo Khouri, Maryam Saberi, Matthias Maercker, Wouter H. T. Vlemmings. 2026-09-25. The Molecular Circumstellar Environments of Red Supergiants in Galactic Open Clusters. https://arxiv.org/abs/2609.31362

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Connecting Dynamo Theory with DNS Data: A Computational Analysis of $α$ and $β$ effects

We investigate the influence of current helicity on the turbulent magnetic diffusivity $β$ using three complementary derivations of the $α$ and $β$ coefficients, based on the large-scale magnetic field $\overline{\mathbf{B}}$, the turbulent velocity $\mathbf{u}$, and the turbulent magnetic field $\mathbf{b}$. Applying these coefficients to raw DNS data, we reconstruct $\overline{\mathbf{B}}$ and compare the results with the original simulations. In the kinematic regime all models agree well with the DNS data. In the nonlinear regime, however, $β_{\mathrm{vv-vw}}$ alone produces unbounded growth of $\overline{\mathbf{B}}$. Including the contribution from turbulent magnetic fields ($β_{\mathrm{bb+jb}}$) suppresses this unphysical growth and restores agreement with the DNS results. We find that kinetic helicity drives $β$ more negative, while current helicity shifts it back toward zero. Weighted combinations of the coefficients further show that the $β$ effect dominates the evolution of $\overline{\mathbf{B}}$ throughout, whereas the $α$ effect becomes important mainly for sustaining the field in the nonlinear regime. The corresponding IDL analysis scripts are provided to facilitate practical implementation of the theoretical models.

astro-ph.SR↗

Magnetic field diagnostics of a solar active region filament

We performed spectropolarimetric observations of an active region filament in He I 10830 angstrom and Si I 10827 angstrom lines to investigate its magnetic field structure. We carried out full-Stokes inversions with the HAZEL code, which takes into account the Zeeman and Hanle effects. As a result, we yielded a mean field strength of 101 $\pm$ 33 G and a horizontal field nearly parallel to the filament axis, such that the distinction between the two classical normal- and reverse-polarity models becomes physically insignificant. In addition, we found Zeeman-like signatures in the linear polarization, characterized by double-peaked symmetric profiles, in some pixels of our observations. Since these profiles could not be well reproduced by modeling that included both the Zeeman and Hanle effects, we performed inversions assuming only the Zeeman effect. The inversion yielded a strong magnetic field of approximately 500 G. However, simultaneous observations of Si I 10827 angstrom indicate a photospheric magnetic field weaker than 100 G. Therefore, the scenario proposed by Diaz Baso et al. (2016), in which the strong field inferred from He I 10830 angstrom originates from contamination by the underlying photosphere, does not apply to our filament. The Zeeman-like profiles are preferentially found in optically thick regions ($τ$ ~ 1.4-2.5), where the simplifying assumptions adopted in HAZEL are expected to become less reliable. Our results suggest that these profiles reveal limitations of the current inversion framework in optically thick regions and motivate future radiative-transfer modeling incorporating self-consistent radiation fields, differential illumination of the multiplet components, and possibly partial frequency redistribution.

astro-ph.SR↗

HXI-DLA2: A Physics-Constrained Deep Learning Algorithm for the ASO-S Hard X-ray Imager

Solar flare hard X-ray imaging is a key diagnostic of flare energy release and electron acceleration. The Hard X-ray Imager (HXI) aboard ASO-S compresses the two-dimensional source distribution into counts measured by 91 sub-collimators, making image reconstruction an inherently underdetermined inverse problem. Conventional algorithms such as CLEAN rely on point-source priors and manual tuning, whereas recent deep-learning methods offer no guarantee that their reconstructions obey the instrument's modulation-sampling forward equation. In this work we show that the counts decompose into two nearly decoupled quantities---the counts average energy, which tracks the total source flux, and the normalized counts distribution, which encodes the source spatial structure---and we exploit this property to construct a physics-constrained network, the Hard X-ray Imager Deep Learning Algorithm 2 (HXI-DLA2). Non-negativity and exact counts-average-energy closure are enforced at the network output, while a distribution-consistency loss aligns the re-projected counts with the measurement, so that the reconstruction satisfies the forward equation by construction. Tests on simulated Gaussian sources, observed soft X-ray morphologies, and a real HXI flare event show two main improvements over existing methods: the limiting resolvable dynamic range of double sources is pushed well beyond that of conventional imaging algorithms and our previous method; and complex morphologies on which prior reconstructions degrade, such as ring-like and diffuse structures, are reliably reconstructed, with the real-event result consistent with contemporaneous SDO/AIA imaging. Embedding the instrumental forward equation as a hard constraint while learning source priors from data offers a general inversion framework for modulation imaging.

astro-ph.SR↗