Related papers: Predicting dust temperature from molecular line da…
We design a convolutional neural network (CNN) incorporating channel attention and spatial attention mechanisms to predict atmospheric parameters of hot subdwarfs. The experimental dataset comprises spectra at nine distinct signal-to-noise…
Dust grains in the interstellar medium interact with photons across the electromagnetic spectrum. They are generally photon energy converters, absorbing short wavelength radiation and emitting long wavelength radiation. Sixty years ago in…
We present a new method based on information theory to find the optimal number of bands required to measure the physical properties of galaxies with a desired accuracy. As a proof of concept, using the recently updated COSMOS catalog…
One of the primary challenges facing upcoming CMB polarization experiments aiming to measure the inflationary B-mode signal is the removal of polarized foregrounds. The thermal dust foreground is often modeled as a single modified…
The spectral region around 10 micrometer, showing prominent dust emission bands, is commonly used to derive the chemical composition of protoplanetary dust. Different analysis methods have been proposed for this purpose, but so far, no…
We present observations of 25 transitions of 17 isotopologues of 9 molecules toward B335. With a goal of constraining chemical models of collapsing clouds, we compare our observations, along with data from the literature, to models of…
Temperature fluctuations significantly affect microorganism growth and pest activities in grain pile, precise monitoring and forecasting temperature of stored grain are essential for maintaining the quality and safety of grain storage. This…
The added value of machine learning for weather and climate applications is measurable through performance metrics, but explaining it remains challenging, particularly for large deep learning models. Inspired by climate model hierarchies,…
We show that a one-component variable-emissivity-spectral-index model (the free-{\alpha} model) provides more physically motivated estimates of dust temperature at the Galactic polar caps than one- or two-component…
Deep Herschel imaging and 12CO(2-1) line luminosities from the IRAM PdBI are combined for a sample of 17 galaxies at z>1 from the GOODS-N field. The sample includes galaxies both on and above the main sequence (MS) traced by star-forming…
Accurate temperature estimation of pouch cells with indirect liquid cooling is essential for optimizing battery thermal management systems for transportation electrification. However, it is challenging due to the computational expense of…
Air pollution poses a significant threat to public health and well-being, particularly in urban areas. This study introduces a series of machine-learning models that integrate data from the Sentinel-5P satellite, meteorological conditions,…
Near-surface air temperature is a key physical property of the Earth's surface. Although weather stations offer continuous monitoring and satellites provide broad spatial coverage, no single data source offers seamless data in a…
Grain alignment theory has reached the stage where quantitative predictions of the degree of alignment and its variations with optical depth are possible. With the goal of studying the effect of clumpiness on the sub-millimeter and far…
Recently, machine learning has emerged as an alternative, powerful approach for predicting quantum-mechanical properties of molecules and solids. Here, using kernel ridge regression and atomic fingerprints representing local environments of…
Numerical weather prediction requires initial estimates of the atmospheric state. Since the atmospheric density field is intricately woven into the atmosphere's governing equations, advancing atmospheric density estimation will improve…
The use of machine learning is becoming ubiquitous in astronomy, but remains rare in the study of the atmospheres of exoplanets. Given the spectrum of an exoplanetary atmosphere, a multi-parameter space is swept through in real time to find…
We propose a very simple physical mechanism responsible for the formation of the Low Ionization Line part of the Broad Line Region in Active Galactic Nuclei. It explains the scaling of the Broad Line Region size with the monochromatic…
We formulate a parametrized model of embedded protostellar disks and test its ability to estimate disk properties by fitting dust-continuum observations. The main physical assumptions of our model are motivated by a recent theoretical study…
We present a method that derives the dust temperatures and infrared (IR) luminosities of high-redshift galaxies assuming radiation equilibrium in a simple dust and stellar distribution geometry. Using public data from the Atacama Large…