Related papers: Predicting dust temperature from molecular line da…
Thermal dust is the major polarized foreground hindering the detection of primordial cosmic microwave background (CMB) B-modes. Its signal exhibits complex behavior in frequency space, arising from the combined variation in our Galaxy of…
Molecular hydrogen being unobservable in cold molecular clouds, the column density measurements of molecular gas currently rely either on dust emission observation in the far-IR or on star counting. (Sub-)millimeter observations of numerous…
We have computed the dust temperature distribution to be expected in a pre-protostellar core in the phase prior to the onset of gravitational instability. We have done this in the approximation that the heating of the dust grains is solely…
In modern astrophysics, the machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We describe an application of the supervised…
Cirrus clouds are key modulators of Earth's climate. Their dependencies on meteorological and aerosol conditions are among the largest uncertainties in global climate models. This work uses three years of satellite and reanalysis data to…
We present a method to estimate the total gas column density, dust-to-gas and dust-to-metal ratios of distant galaxies from rest-frame optical spectra. The technique exploits the sensitivity of certain optical lines to changes in depletion…
Constraining the temperature and density structure of dense molecular cloud cores is fundamental for understanding the initial conditions of star formation. We use Herschel observations of the thermal FIR dust emission from nearby isolated…
The balloon-borne submillimeter instrument PRONAOS has observed one square degree areas towards the Orion and M17 molecular clouds. The 2' - 3.5' resolution maps obtained in four wide wavelength bands between 200 microns and 600 microns,…
We propose a novel method to constrain the spatial extent of dust around galaxies through the measurement of dust temperature. Our method combines the dust emission of galaxies from far-infrared (FIR) image stacking analysis and the quasar…
Aerosol scattering influences the retrieval of the column-averaged dry-air mole fraction of CO2 (XCO2) from the Orbiting Carbon Observatory-2 (OCO-2). This is especially true for surfaces with reflectance close to a critical value where…
Dust grains are important in various astrophysical processes and serve as indicators of interstellar medium structures, density, and mass. Understanding their physical properties and chemical composition is crucial in astrophysics. Dust…
Aims. The derivation of spectroscopic parameters for M dwarf stars is very important in the fields of stellar and exoplanet characterization. The goal of this work is the creation of an automatic computational tool, able to derive quickly…
We use UV measurements of interstellar CO towards nearby stars to calculate the density in the diffuse molecular clouds containing the molecules responsible for the observed absorption. Chemical models and recent calculations of the…
We report Submillimeter Array dust continuum and molecular spectral line observations toward the Orion Bar photon-dominated region (PDR). The 1.2 mm continuum map reveals, for the first time, a total of 9 compact (r < 0.01pc) dust…
Dust plays an important role in shaping a galaxy's spectral energy distribution (SED). It absorbs ultraviolet (UV) to near-infrared (NIR) radiation and re-emits this energy in the far-infrared (FIR). The FIR is essential to understand dust…
Accurate thermal analysis of composites and porous media requires detailed characterization of local thermal properties in small scale. For some important applications such as lithium-ion batteries, changes in the properties during the…
We investigated the effect of time-dependent ice growth on dust grains on the opacity and hence on the dust temperature in a collapsing molecular cloud core, with the aim of quantifying the effect of the dust temperature variations on ice…
We apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time series data. We preprocessed over 94 GB of Kepler light curves from MAST to classify according to ten distinct physical…
We aim to characterize the relationship between dust properties. We also aim to provide equations to estimate accurate dust properties from limited observational datasets. We assemble a sample of 1,630 nearby (z<0.1) galaxies-over a large…
Solar irradiance is fundamental data crucial for analyses related to weather and climate. High-precision estimation models are necessary to create areal data for solar irradiance. In this study, we developed a novel estimation model by…