ترغب بنشر مسار تعليمي؟ اضغط هنا

Starspot mapping with adaptive parallel tempering I: Implementation of computational code

149   0   0.0 ( 0 )
 نشر من قبل Kai Ikuta
 تاريخ النشر 2020
  مجال البحث فيزياء
والبحث باللغة English




اسأل ChatGPT حول البحث

Starspots are thought to be regions of locally strong magnetic fields, similar to sunspots, and they can generate photometric brightness modulations. To deduce stellar and spot properties, such as spot emergence and decay rates, we implement computational code for starspot modeling. It is implemented with an adaptive parallel tempering algorithm and an importance sampling algorithm for parameter estimation and model selection in the Bayesian framework. For evaluating the performance of the code, we apply it to synthetic light curves produced with 3 spots. The light curves are specified in the spot parameters, such as the radii, intensities, latitudes, longitudes, and emergence/decay durations. The spots are circular with specified radii and intensities relative to the photosphere, and the stellar differential rotation coefficient is also included in the light curves. As a result, stellar and spot parameters are uniquely deduced. The number of spots is correctly determined: the 3-spot model is preferable because the model evidence is much greater than that of 2-spot models by orders of magnitude and more than that of 4-spot model by a more modest factor, whereas the light curves are produced to have 2 or 1 local minimum during one equatorial rotation period by adjusting the values of longitude. The spot emergence and decay rates can be estimated with error less than an order of magnitude, considering the difference of the number of spots.



قيم البحث

اقرأ أيضاً

General relativistic force-free electrodynamics is one possible plasma-limit employed to analyze energetic outflows in which strong magnetic fields are dominant over all inertial phenomena. The amazing images of black hole shadows from the galactic c enter and the M87 galaxy provide a first direct glimpse into the physics of accretion flows in the most extreme environments of the universe. The efficient extraction of energy in the form of collimated outflows or jets from a rotating BH is directly linked to the topology of the surrounding magnetic field. We aim at providing a tool to numerically model the dynamics of such fields in magnetospheres around compact objects, such as black holes and neutron stars. By this, we probe their role in the formation of high energy phenomena such as magnetar flares and the highly variable teraelectronvolt emission of some active galactic nuclei. In this work, we present numerical strategies capable of modeling fully dynamical force-free magnetospheres of compact astrophysical objects. We provide implementation details and extensive testing of our implementation of general relativistic force-free electrodynamics in Cartesian and spherical coordinates using the infrastructure of the Einstein Toolkit. The employed hyperbolic/parabolic cleaning of numerical errors with full general relativistic compatibility allows for fast advection of numerical errors in dynamical spacetimes. Such fast advection of divergence errors significantly improves the stability of the general relativistic force-free electrodynamics modeling of black hole magnetospheres.
The study of the stability of massive gaseous disks around a star in a non-isolated context is not a trivial issue and becomes a more complicated task for disks hosted by binary systems. The role of self-gravity is thought to be significant, whenever the ratio of the disk to the star mass is non-negligible. To tackle these issues we implemented, tested and applied our own Smoothed Particle Hydrodynamics (SPH) algorithm. The code (named GaSPH) passed various quality tests and shows good performances, so to be reliably applied to the study of disks around stars accounting for self-gravity. This work aims to introduce and describe the algorithm, making some performance and stability tests. It constitutes the first part of a series of studies in which self-gravitating disks in binary systems will be let evolve in larger environments such as Open Clusters.
The use of Gaussian processes (GPs) as models for astronomical time series datasets has recently become almost ubiquitous, given their ease of use and flexibility. GPs excel in particular at marginalization over the stellar signal in cases where the variability due to starspots rotating in and out of view is treated as a nuisance, such as in exoplanet transit modeling. However, these effective models are less useful in cases where the starspot signal is of primary interest since it is not obvious how the parameters of the GP model are related to the physical properties of interest, such as the size, contrast, and latitudinal distribution of the spots. Instead, it is common practice to explicitly model the effect of individual starspots on the light curve and attempt to infer their properties via optimization or posterior inference. Unfortunately, this process is degenerate, ill-posed, and often computationally intractable when applied to stars with more than a few spots and/or to ensembles of many light curves. In this paper, we derive a closed-form expression for the mean and covariance of a Gaussian process model that describes the light curve of a rotating, evolving stellar surface conditioned on a given distribution of starspot sizes, contrasts, and latitudes. We demonstrate that this model is correctly calibrated, allowing one to robustly infer physical parameters of interest from one or more stellar light curves, including the typical radii and the mean and variance of the latitude distribution of starspots. Our GP has far-ranging implications for understanding the variability and magnetic activity of stars from both light curves and radial velocity (RV) measurements, as well as for robustly modeling correlated noise in both transiting and RV exoplanet searches. Our implementation is efficient, user-friendly, and open source, available as the Python package starry-process.
We present a forward-modeling framework using the Bayesian inference tool Starfish and cloudless Sonora-Bobcat model atmospheres to analyze low-resolution ($Rapprox80-250$) near-infrared ($1.0-2.5$ $mu$m) spectra of T dwarfs. Our approach infers effe ctive temperatures, surface gravities, metallicities, radii, and masses, and by accounting for uncertainties from model interpolation and correlated residuals due to instrumental effects and modeling systematics, produces more realistic parameter posteriors than traditional ($chi^2$-based) spectral-fitting analyses. We validate our framework by fitting the model atmospheres themselves and finding negligible offsets between derived and input parameters. We apply our methodology to three well-known benchmark late-T dwarfs, HD 3651B, GJ 570D, and Ross 458C, using both solar and non-solar metallicity atmospheric models. We also derive these benchmarks physical properties using their bolometric luminosities, their primary stars ages and metallicities, and Sonora-Bobcat evolutionary models. Assuming the evolutionary-based parameters are more robust, we find our atmospheric-based, forward-modeling analysis produces two outcomes. For HD 3615B and GJ 570D, spectral fits provide accurate $T_{rm eff}$ and $R$ but underestimated $log{g}$ (by $approx1.2$ dex) and $Z$ (by $approx0.35$ dex), likely due to the systematics from modeling the potassium line profiles. For Ross 458C, spectral fits provide accurate $log{g}$ and $Z$ but overestimated $T_{rm eff}$ (by $approx120$ K) and underestimated $R$ (by $approx1.6times$), likely because our model atmospheres lack clouds, reduced vertical temperature gradients, or disequilibrium processes. Finally, the spectroscopically inferred masses of these benchmarks are all considerably underestimated.
We describe the CRASH (Center for Radiative Shock Hydrodynamics) code, a block adaptive mesh code for multi-material radiation hydrodynamics. The implementation solves the radiation diffusion model with the gray or multigroup method and uses a flux l imited diffusion approximation to recover the free-streaming limit. The electrons and ions are allowed to have different temperatures and we include a flux limited electron heat conduction. The radiation hydrodynamic equations are solved in the Eulerian frame by means of a conservative finite volume discretization in either one, two, or three-dimensional slab geometry or in two-dimensional cylindrical symmetry. An operator split method is used to solve these equations in three substeps: (1) solve the hydrodynamic equations with shock-capturing schemes, (2) a linear advection of the radiation in frequency-logarithm space, and (3) an implicit solve of the stiff radiation diffusion, heat conduction, and energy exchange. We present a suite of verification test problems to demonstrate the accuracy and performance of the algorithms. The CRASH code is an extension of the Block-Adaptive Tree Solarwind Roe Upwind Scheme (BATS-R-US) code with this new radiation transfer and heat conduction library and equation-of-state and multigroup opacity solvers. Both CRASH and BATS-R-US are part of the publicly available Space Weather Modeling Framework (SWMF).
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا