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Register a new userImprovements on analytic modelling of stellar spots
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In this work we present the solution of the stellar spot problem using the Kelvin-Stokes theorem. Our result is applicable for any given location and dimension of the spots on the stellar surface. We present explicitely the result up to the second degree in the limb darkening law. This technique can be used to calculate very efficiently mutual photometric effects produced by eclipsing bodies occulting stellar spots and to construct complex spot shapes.
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We assess a physically feasible explanation for the low number of discovered (near-)grazing planetary transits through all ground and space based transit surveys. We performed simulations to generate the synthetic distribution of detectable planets based on their impact parameter, and found that a larger number of (near-)grazing planets should have been detected than have been detected. Our explanation for the insufficient number of (near-)grazing planets is based on a simple assumption that a large number of (near-)grazing planets transit host stars which harbor dark giant polar spot, and thus the transit light-curve vanishes due to the occultation of grazing planet and the polar spot. We conclude by evaluating the properties required of polar spots in order to make disappear the grazing transit light-curve, and we conclude that their properties are compatible with the expected properties from observations.
The Doppler method of exoplanet detection has been extremely successful, but suffers from contaminating noise from stellar activity. In this work a model of a rotating star with a magnetic field based on the geometry of the K2 star Epsilon Eridani is presented and used to estimate its effect on simulated radial velocity measurements. A number of different distributions of unresolved magnetic spots were simulated on top of the observed large-scale magnetic maps obtained from eight years of spectropolarimetric observations. The radial velocity signals due to the magnetic spots have amplitudes of up to 10 m s$^{-1}$, high enough to prevent the detection of planets under 20 Earth masses in temperate zones of solar type stars. We show that the radial velocity depends heavily on spot distribution. Our results emphasize that understanding stellar magnetic activity and spot distribution is crucial for detection of Earth analogues.
Nearly 15 years of radial velocity (RV) monitoring and direct imaging enabled the detection of two giant planets orbiting the young, nearby star $beta$ Pictoris. The $delta$ Scuti pulsations of the star, overwhelming planetary signals, need to be carefully suppressed. In this work, we independently revisit the analysis of the RV data following a different approach than in the literature to model the activity of the star. We show that a Gaussian Process (GP) with a stochastically driven damped harmonic oscillator kernel can model the $delta$ Scuti pulsations. It provides similar results as parametric models but with a simpler framework, using only 3 hyperparameters. It also enables to model poorly sampled RV data, that were excluded from previous analysis, hence extending the RV baseline by nearly five years. Altogether, the orbit and the mass of both planets can be constrained from RV only, which was not possible with the parametric modelling. To characterize the system more accurately, we also perform a joint fit of all available relative astrometry and RV data. Our orbital solutions for $beta$ Pic b favour a low eccentricity of $0.029^{+0.061}_{-0.024}$ and a relatively short period of $21.1^{+2.0}_{-0.8}$ yr. The orbit of $beta$ Pic c is eccentric with $0.206^{+0.074}_{-0.063}$ with a period of $3.36pm0.03$ yr. We find model-independent masses of $11.7pm1.4$ and $8.5pm0.5$ M$_{Jup}$ for $beta$ Pic b and c, respectively, assuming coplanarity. The mass of $beta$ Pic b is consistent with the hottest start evolutionary models, at an age of $25pm3$ Myr. A direct direction of $beta$ Pic c would provide a second calibration measurement in a coeval system.
AU Mic is a young planetary system with a resolved debris disc showing signs of planet formation and two transiting warm Neptunes near mean-motion resonances. Here we analyse three transits of AU Mic b observed with the CHaracterising ExOPlanet Satellite (CHEOPS), supplemented with sector 1 and 27 Transiting Exoplanet Survey Satellite (TESS) photometry, and the All-Sky Automated Survey (ASAS) from the ground. The refined orbital period of AU Mic b is 8.462995 pm 0.000003 d, whereas the stellar rotational period is P_{rot}=4.8367 pm 0.0006 d. The two periods indicate a 7:4 spin--orbit commensurability at a precision of 0.1%. Therefore, all transits are observed in front of one of the four possible stellar central longitudes. This is strongly supported by the observation that the same complex star-spot pattern is seen in the second and third CHEOPS visits that were separated by four orbits (and seven stellar rotations). Using a bootstrap analysis we find that flares and star spots reduce the accuracy of transit parameters by up to 10% in the planet-to-star radius ratio and the accuracy on transit time by 3-4 minutes. Nevertheless, occulted stellar spot features independently confirm the presence of transit timing variations (TTVs) with an amplitude of at least 4 minutes. We find that the outer companion, AU Mic c may cause the observed TTVs.
The goal of AIMS (Asteroseismic Inference on a Massive Scale) is to estimate stellar parameters and credible intervals/error bars in a Bayesian manner from a set of asteroseismic frequency data and so-called classical constraints. To achieve reliable parameter estimates and computational efficiency, it searches through a grid of pre-computed models using an MCMC algorithm -- interpolation within the grid of models is performed by first tessellating the grid using a Delaunay triangulation and then doing a linear barycentric interpolation on matching simplexes. Inputs for the modelling consist of individual frequencies from peak-bagging, which can be complemented with classical spectroscopic constraints. AIMS is mostly written in Python with a modular structure to facilitate contributions from the community. Only a few computationally intensive parts have been rewritten in Fortran in order to speed up calculations.
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