- ID:
- ivo://CDS.VizieR/J/ApJ/742/L19
- Title:
- Physics of Kepler hot rocky planetary candidates
- Short Name:
- J/ApJ/742/L19
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- This paper outlines a simple approach to evaluate the atmospheric composition of hot rocky planets by assuming different types of planetary composition and using corresponding model calculations. To explore hot atmospheres above 1000K, we model the vaporization of silicate magma and estimate the range of atmospheric compositions according to the planet's radius and semi-major axis for the Kepler 2011 February data release. Our results show five atmospheric types for hot, rocky super-Earth atmospheres, strongly dependent on the initial composition and the planet's distance to the star. We provide a simple set of parameters that can be used to evaluate atmospheric compositions for current and future candidates provided by the Kepler mission and other searches.
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Search Results
- ID:
- ivo://CDS.VizieR/J/ApJS/171/146
- Title:
- Population synthesis in the blue. IV
- Short Name:
- J/ApJS/171/146
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- We present a new set of model predictions for 16 Lick absorption line indices from H{delta} through Fe5335 and UBV colors for single stellar populations with ages ranging between 1 and 15Gyr, [Fe/H] ranging from -1.3 to +0.3, and variable abundance ratios. The models are based on accurate stellar parameters for the Jones library stars and a new set of fitting functions describing the behavior of line indices as a function of effective temperature, surface gravity, and iron abundance. The abundances of several key elements in the library stars have been obtained from the literature in order to characterize the abundance pattern of the stellar library, thus allowing us to produce model predictions for any set of abundance ratios desired. We develop a method to estimate mean ages and abundances of iron, carbon, nitrogen, magnesium, and calcium that explores the sensitivity of the various indices modeled to those parameters. The models are compared to high-S/N data for Galactic clusters spanning the range of ages, metallicities, and abundance patterns of interest. Essentially all line indices are matched when the known cluster parameters are adopted as input. Comparing the models to high-quality data for galaxies in the nearby universe, we reproduce previous results regarding the enhancement of light elements and the spread in the mean luminosity-weighted ages of early-type galaxies.
- ID:
- ivo://CDS.VizieR/J/ApJ/715/1050
- Title:
- Predicted abundances for extrasolar planets. I.
- Short Name:
- J/ApJ/715/1050
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- Extrasolar planet host stars have been found to be enriched in key planet-building elements. These enrichments have the potential to drastically alter the composition of material available for terrestrial planet formation. Here, we report on the combination of dynamical models of late-stage terrestrial planet formation within known extrasolar planetary systems with chemical equilibrium models of the composition of solid material within the disk. This allows us to determine the bulk elemental composition of simulated extrasolar terrestrial planets. A wide variety of resulting planetary compositions are found, ranging from those that are essentially "Earth like", containing metallic Fe and Mg silicates, to those that are dominated by graphite and SiC. This shows that a diverse range of terrestrial planets may exist within extrasolar planetary systems.
- ID:
- ivo://CDS.VizieR/J/ApJ/765/9
- Title:
- Predicted CO and [CII] fluxes of HUDF galaxies
- Short Name:
- J/ApJ/765/9
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- Modern (sub-)millimeter/radio interferometers such as ALMA, JVLA, and the PdBI successor NOEMA will enable us to measure the dust and molecular gas emission from galaxies that have luminosities lower than the Milky Way, out to high redshifts and with unprecedented spatial resolution and sensitivity. This will provide new constraints on the star formation properties and gas reservoir in galaxies throughout cosmic times through dedicated deep field campaigns targeting the CO/[C II] lines and dust continuum emission in the (sub-)millimeter regime. In this paper, we present empirical predictions for such line and continuum deep fields. We base these predictions on the deepest available optical/near-infrared Advanced Camera for Surveys and NICMOS data on the Hubble Ultra Deep Field (over an area of about 12arcmin^2^). Using a physically motivated spectral energy distribution model, we fit the observed optical/near-infrared emission of 13099 galaxies with redshifts up to z=5, and obtain median-likelihood estimates of their stellar mass, star formation rate, dust attenuation, and dust luminosity. We combine the attenuated stellar spectra with a library of infrared emission models spanning a wide range of dust temperatures to derive statistical constraints on the dust emission in the infrared and (sub-)millimeter which are consistent with the observed optical/near-infrared emission in terms of energy balance. This allows us to estimate, for each galaxy, the (sub-)millimeter continuum flux densities in several ALMA, PdBI/NOEMA, and JVLA bands. As a consistency check, we verify that the 850{mu}m number counts and extragalactic background light derived using our predictions are consistent with previous observations. Using empirical relations between the observed CO/[C II] line luminosities and the infrared luminosity of star-forming galaxies, we infer the luminosity of the CO(1-0) and [C II] lines from the estimated infrared luminosity of each galaxy in our sample.
- ID:
- ivo://CDS.VizieR/J/A+A/651/A55
- Title:
- Predicted redshifts of galaxies with NetZ
- Short Name:
- J/A+A/651/A55
- Date:
- 22 Feb 2022
- Publisher:
- CDS
- Description:
- The redshifts of galaxies are a key attribute that is needed for nearly all extragalactic studies. Since spectroscopic redshifts require additional telescope and human resources, millions of galaxies are known without spectroscopic redshifts. Therefore, it is crucial to have methods for estimating the redshift of a galaxy based on its photometric properties, the so-called photo-z. We developed NetZ, a new method using a Convolutional Neural Network (CNN) to predict the photo-z based on galaxy images, in contrast to previous methods which often used only the integrated photometries of galaxies without their images. We use data from the Hyper Suprime-Cam Subaru Strategic Program (HSC SSP) in five different filters as training data. The network over the whole redshift range between 0 and 4 performs well overall and especially in the high-z range better than other methods on the same data. We obtain an accuracy |zpred-zref| of sigma=0.12 (68% confidence interval) with a CNN working for all galaxy types averaged over all galaxies in the redshift range of 0 to ~4. By limiting to smaller redshift ranges or to Luminous Red Galaxies (LRGs), we find a further notable improvement. We publish more than 34 million new photo-z values predicted with NetZ here. This shows that the new method is very simple and fast to apply, and, importantly, covers a wide redshift range limited only by the available training data. It is broadly applicable and beneficial to imaging surveys, particularly upcoming surveys like the Rubin Observatory Legacy Survey of Space and Time which will provide images of billions of galaxies with similar image quality as HSC.
- ID:
- ivo://CDS.VizieR/J/ApJ/880/49
- Title:
- Predictions of giant exoplanet host star's
- Short Name:
- J/ApJ/880/49
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- The presence of certain elements within a star, and by extension its planet, strongly impacts the formation and evolution of the planetary system. The positive correlation between a host star's iron content and the presence of an orbiting giant exoplanet has been confirmed; however, the importance of other elements in predicting giant planet occurrence is less certain despite their central role in shaping internal planetary structure. We designed and applied a machine-learning algorithm to the Hypatia Catalog to analyze the stellar abundance patterns of known host stars to determine those elements important in identifying potential giant exoplanet host stars. We analyzed a variety of different elements ensembles-namely, volatiles, lithophiles, siderophiles, and Fe. We show that the relative abundances of oxygen, carbon, and sodium, in addition to iron, are influential indicators of the presence of a giant planet. We demonstrate the predictive power of our algorithm by analyzing stars with known giant planets and found that they had median 75% prediction score. We present a list of ~350 stars with no currently discovered planets that have a >=90% prediction probability likelihood of hosting a giant exoplanet. We investigated archival HARPS data and found significant trends that HIP 62345, HIP 71803, and HIP 10278 host long-period giant planet companions with estimated minimum M_p_sin(i) values of 3.7, 6.8, and 8.5M_J_, respectively. We anticipate that our findings will revolutionize future target selection, the role that elements play in giant planet formation, and the determination of giant planet interior structure models.
- ID:
- ivo://CDS.VizieR/J/AJ/135/850
- Title:
- Properties of eclipsing binaries found in TrES
- Short Name:
- J/AJ/135/850
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- In recent years, we have witnessed an explosion of photometric time-series data, collected for the purpose of finding a small number of rare sources, such as transiting extrasolar planets and gravitational microlenses. Once combed, these data are often set aside, and are not further searched for the many other variable sources that they undoubtedly contain. To this end, we describe a pipeline that is designed to systematically analyze such data, while requiring minimal user interaction. We ran our pipeline on a subset of the Trans-Atlantic Exoplanet Survey dataset, and used it to identify and model 773 eclipsing binary systems. For each system we conducted a joint analysis of its light curve, colors, and theoretical isochrones. This analysis provided us with estimates of the binary's absolute physical properties, including the masses and ages of their stellar components, as well as their physical separations and distances. We identified three types of eclipsing binaries that are of particular interest and merit further observations. The first category includes 11 low-mass candidates, which may assist current efforts to explain the discrepancies between the observation and the models of stars at the bottom of the main sequence. The other two categories include 34 binaries with eccentric orbits, and 20 binaries with abnormal light curves. Finally, this uniform catalog enabled us to identify a number of relations that provide further constraints on binary population models and tidal circularization theory.
- ID:
- ivo://CDS.VizieR/J/AJ/156/149
- Title:
- Properties of massive giant planets & brown dwarfs
- Short Name:
- J/AJ/156/149
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- We present thermodynamic material and transport properties for the extreme conditions prevalent in the interiors of massive giant planets and brown dwarfs. They are obtained from extensive ab initio simulations of hydrogen-helium mixtures along the isentropes of three representative objects. In particular, we determine the heat capacities, the thermal expansion coefficient, the isothermal compressibility, and the sound velocity. Important transport properties such as the electrical and thermal conductivity, opacity, and shear viscosity are also calculated. Further results for associated quantities, including magnetic and thermal diffusivity, kinematic shear viscosity, as well as the static Love number k_2_ and the equidistance, are presented. In comparison to Jupiter-mass planets, the behavior inside massive giant planets and brown dwarfs is stronger dominated by degenerate matter. We discuss the implications on possible dynamics and magnetic fields of those massive objects. The consistent data set compiled here may serve as a starting point to obtain material and transport properties for other substellar H-He objects with masses above one Jovian mass and finally may be used as input for dynamo simulations.
- ID:
- ivo://CDS.VizieR/J/AJ/154/228
- Title:
- Properties of transiting planet's host stars
- Short Name:
- J/AJ/154/228
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- The properties of a transiting planet's host star are written in its transit light curve. The light curve can reveal the stellar density ({rho}_*_) and the limb-darkening profile in addition to the characteristics of the planet and its orbit. For planets with strong prior constraints on orbital eccentricity, we may measure these stellar properties directly from the light curve; this method promises to aid greatly in the characterization of transiting planet host stars targeted by the upcoming NASA Transiting Exoplanet Survey Satellite mission and any long-period, singly transiting planets discovered in the same systems. Using Bayesian inference, we fit a transit model, including a nonlinear limb-darkening law, to 66 Kepler transiting planet hosts to measure their stellar properties. We present posterior distributions of {rho}*, limb-darkening coefficients, and other system parameters for these stars. We measure densities to within 5% for the majority of our target stars, with the dominant precision-limiting factor being the signal-to-noise ratio of the transits. Of our measured stellar densities, 95% are in 3{sigma} or better agreement with previously published literature values. We make posterior distributions for all of our target Kepler objects of interest available online at 10.5281/zenodo.1028515.
- ID:
- ivo://CDS.VizieR/J/MNRAS/424/2832
- Title:
- Pulsars in {gamma}-ray sources
- Short Name:
- J/MNRAS/424/2832
- Date:
- 21 Oct 2021
- Publisher:
- CDS
- Description:
- Machine learning, algorithms designed to extract empirical knowledge from data, can be used to classify data, which is one of the most common tasks in observational astronomy. In this paper, we focus on Bayesian data classification algorithms using the Gaussian mixture model and show two applications in pulsar astronomy. After reviewing the Gaussian mixture model and the related expectation-maximization algorithm, we present a data classification method using the Neyman-Pearson test. To demonstrate the method, we apply the algorithm to two classification problems. First, it is applied to the well-known period-period derivative diagram. Our second example is to calculate the likelihood of unidentified Fermi point sources being pulsars.
