Related papers: Predicting dust temperature from molecular line da…
With Planck and Herschel, we now have the spectral coverage and angular resolution required to observe dense and cold molecular clouds. As these clouds are optically thick at short wavelength but optically thin at long wavelength, it is…
Multi-wavelength observations in the sub-mm regime provide information on the distribution of both the dust column density and the effective dust temperature in molecular clouds. In this study, we created high-resolution and…
We have used a numerical simulation of a turbulent cloud to synthesize maps of the thermal emission from dust at a variety of far-IR and sub-mm wavelengths. The average column density and external radiation field in the simulation is well…
Dust determines the temperature structure of protoplanetary disks. However, dust temperature determinations almost invariably rely on a complex modeling of the Spectral Energy Distribution. We attempt a direct determination of the…
Context. Multitransition CO observations of galaxy centers have revealed that significant fractions of the dense circumnuclear gas have high kinetic temperatures, which are hard to explain by pure photon excitation, but may be caused by…
Climate models lack the necessary resolution for urban climate studies, requiring computationally intensive processes to estimate high resolution air temperatures. In contrast, Data-driven approaches offer faster and more accurate air…
The physical state of cold cloud clumps has a great impact on the process and efficiency of star formation and the masses of the forming stars inside these objects. The sub-millimetre survey of the Planck space observatory and the…
We present a high-sensitivity ($1\sigma<1.6~\mathrm{mJy~beam^{-1}}$) continuum observation in a 343 arcmin$^2$ area of the northeast region in the Small Magellanic Cloud at a wavelength of 1.1 mm, conducted using the AzTEC instrument on the…
Accurate short-term forecasting of air temperature and relative humidity is critical for urban management, especially in topographically complex cities such as Chongqing, China. This study compares seven machine learning models: eXtreme…
We apply photo-dissociation region (PDR) molecular line emission models, that have varying degrees of enhanced mechanical heating rates, to the gaseous component of simulations of star-forming galaxies taken from the literature. Snapshots…
The melting temperature is important for materials design because of its relationship with thermal stability, synthesis, and processing conditions. Current empirical and computational melting point estimation techniques are limited in…
The factor relating CO emission to molecular hydrogen column density, XCO, is still subject to uncertainty, in particular at low metallicity. Here, to quantify XCO at two different spatial resolutions, we exploit a dust-based method…
Achieving a comprehensive understanding of the star and planet formation process is one of the fundamental tasks of astrophysics, requiring detailed knowledge of the physical conditions during the different phases of this process. During…
Knowledge of the midplane temperature of protoplanetary disks is one of the key ingredients in theories of dust growth and planet formation. However, direct measurement of this quantity is complicated, and often depends on the fitting of…
The Orbital-Motion-Limited (OML) theory has been modified to predict the dust charge and the results were contrasted with the Whipple approximation [Tang and Delzanno, Phys. Plasmas 21, 123708 (2014)]. To further establish its regime of…
The thermal emission of dust is one of the most important tracers of the interstellar medium: multi-wavelength photometry in the far-infrared (FIR) and submillimeter (submm) can be fitted with a model, providing estimates of the dust mass.…
The growing adoption of machine learning (ML) in modelling atmospheric and oceanic processes offers a promising alternative to traditional numerical methods. It is essential to benchmark the performance of both ML and physics-informed ML…
We present a method for accurately and precisely inferring photometric dust extinction towards stars at mid-to-high Galactic latitudes using probabilistic machine learning to model the colour-magnitude distribution of zero-extinction stars…
In situ and remotely sensed observations have potential to facilitate data-driven predictive models for oceanography. A suite of machine learning models, including regression, decision tree and deep learning approaches were developed to…
Context: The study of dust emission at millimeter wavelengths is important to shed light on the dust properties and physical structure of pre-stellar cores, the initial conditions in the process of star and planet formation. Aims: Using two…