Rotational evolution of young-to-old stars with data-driven three-dimensional wind models

Solar-type stars form with a wide range of rotation rates. A wide range persists until a stellar age of 0.6 Gyr, after which solar-type stars exhibit Skumanich spin-down. Rotational evolution models incorporating polytropic stellar winds struggle to simultaneously reproduce these two regimes, namely the initially wide range and the Skumanich spin-down without imposing an a-priori cap on the wind mass-loss rate. We show that a three-dimensional wind model driven by Alfv\'en waves and observational data yields wind torques that agree with the observed age distribution of rotation rates. In our models of the Sun and twenty-seven open cluster stars aged from 0.04 to 0.6 Gyr that have observationally derived surface magnetic maps and rotation rates, we find evidence of exponential spin-down in young stars that are rapid rotators and Skumanich spin-down for slow rotators. The two spin-down regimes emerge naturally from our data-driven models. Our modelling suggests that the observed age distribution of stellar rotation rates arises as a consequence of magnetic field strength saturation in rapid rotators.Comment: 10 pages, 4 figures; accepted for publication in MNRA

Multiple imputation of missing categorical data using latent class models:State of art

This paper provides an overview of recent proposals for using latent class models for the multiple imputation of missing categorical data in large-scale studies. While latent class (or finite mixture) modeling is mainly known as a clustering tool, it can also be used for density estimation, i.e., to get a good description of the lower- and higher-order associations among the variables in a dataset. For multiple imputation, the latter aspect is essential in order to be able to draw meaningful imputing values from the conditional distribution of the missing data given the observed data. We explain the general logic underlying the use of latent class analysis for multiple imputation. Moreover, we present several variants developed within either a frequentist or a Bayesian framework, each of which overcomes certain limitations of the standard implementation. The different approaches are illustrated and compared using a real-data psychological assessment application

A computational fluid dynamics approach to determine white matter permeability

Glioblastomas represent a challenging problem with an extremely poor survival rate. Since these tumour cells have a highly invasive character, an effective surgical resection as well as chemotherapy and radiotherapy is very difficult. Convection-enhanced delivery (CED), a technique that consists in the injection of a therapeutic agent directly into the parenchyma, has shown encouraging results. Its efficacy depends on the ability to predict, in the pre-operative phase, the distribution of the drug inside the tumour. This paper proposes a method to compute a fundamental parameter for CED modelling outcomes, the hydraulic permeability, in three brain structures. Therefore, a bidimensional brain-like structure was built out of the main geometrical features of the white matter: axon diameter distribution extrapolated from electron microscopy images, extracellular space (ECS) volume fraction and ECS width. The axons were randomly allocated inside a defined border, and the ECS volume fraction as well as the ECS width maintained in a physiological range. To achieve this result, an outward packing method coupled with a disc shrinking technique was implemented. The fluid flow through the axons was computed by solving Navier–Stokes equations within the computational fluid dynamics solver ANSYS. From the fluid and pressure fields, an homogenisation technique allowed establishing the optimal representative volume element (RVE) size. The hydraulic permeability computed on the RVE was found in good agreement with experimental data from the literature

The solar wind in time – II. 3D stellar wind structure and radio emission

In this work, we simulate the evolution of the solar wind along its main-sequence lifetime and compute its thermal radio emission. To study the evolution of the solar wind, we use a sample of solar mass stars at different ages. All these stars have observationally reconstructed magnetic maps, which are incorporated in our 3D magnetohydrodynamic simulations of their winds. We show that angular-momentum loss and mass-loss rates decrease steadily on evolutionary time-scales, although they can vary in a magnetic cycle time-scale. Stellar winds are known to emit radiation in the form of thermal bremsstrahlung in the radio spectrum. To calculate the expected radio fluxes from these winds, we solve the radiative transfer equation numerically from first principles. We compute continuum spectra across the frequency range 100 MHz to 100 GHz and find maximum radio flux densities ranging from 0.05 to 2.2 μJy. At a frequency of 1 GHz and a normalized distance of d = 10 pc, the radio flux density follows 0.24 (Ω/Ω☉)0.9 (d/[10pc])-2μJy, where Ω is the rotation rate. This means that the best candidates for stellar wind observations in the radio regime are faster rotators within distances of 10 pc, such as κ1 Ceti (0.73 μJy) and χ1 Ori (2.2 μJy). These flux predictions provide a guide to observing solar-type stars across the frequency range 0.1-100 GHz in the future using the next generation of radio telescopes, such as ngVLA and Square Kilometre Array

On the magnetic structure and wind parameter profiles of Alfven wave driven winds in late-type supergiant stars

Cool stars at giant and supergiant evolutionary phases present low velocity and high density winds, responsible for the observed high mass-loss rates. Although presenting high luminosities, radiation pressure on dust particles is not sufficient to explain the wind acceleration process. Among the possible solutions to this still unsolved problem, Alfven waves are, probably, the most interesting for their high efficiency in transfering energy and momentum to the wind. Typically, models of Alfven wave driven winds result in high velocity winds if they are not highly damped. In this work we determine self-consistently the magnetic field geometry and solve the momentum, energy and mass conservation equations, to demonstrate that even a low damped Alfven wave flux is able to reproduce the low velocity wind. We show that the magnetic fluxtubes expand with a super-radial factor S>30 near the stellar surface, larger than that used in previous semi-empirical models. The rapid expansion results in a strong spatial dilution of the wave flux. We obtained the wind parameter profiles for a typical supergiant star of 16 M_sun. The wind is accelerated in a narrow region, coincident with the region of high divergence of the magnetic field lines, up to 100 km/s. For the temperature, we obtained a slight decrease near the surface for low damped waves, because the wave heating mechanism is less effective than the radiative losses. The peak temperature occurs at 1.5 r_0 reaching 6000 K. Propagating outwards, the wind cools down mainly due to adiabatic expansion.Comment: to appear in the MNRA

Stellar Coronal and Wind Models: Impact on Exoplanets

Surface magnetism is believed to be the main driver of coronal heating and stellar wind acceleration. Coronae are believed to be formed by plasma confined in closed magnetic coronal loops of the stars, with winds mainly originating in open magnetic field line regions. In this Chapter, we review some basic properties of stellar coronae and winds and present some existing models. In the last part of this Chapter, we discuss the effects of coronal winds on exoplanets.Comment: Chapter published in the "Handbook of Exoplanets", Editors in Chief: Juan Antonio Belmonte and Hans Deeg, Section Editor: Nuccio Lanza. Springer Reference Work

Erratum: The solar wind in time II: 3D stellar wind structure and radio emission

This is an erratum to the paper ‘The solar wind in time - II: 3D stellar wind structure and radio emission’, which was published in MNRAS, 483(1), 873, 2019 (Ó Fionnagáin et al. 2019)