Related papers: Predicting dust temperature from molecular line da…
The present work is aimed to examine the potential of advanced machine learning strategies to predict the monthly rainfall (precipitation) for the Indus Basin, using climatological variables such as air temperature, geo-potential height,…
We mapped the kinetic temperature structure of the Orion molecular cloud 1 with para-H2CO(303-202, 322-221, and 321-220) using the APEX 12m telescope. This is compared with the temperatures derived from the ratio of the NH3(2,2)/(1,1)…
Molecular clouds are regions of dense gas and dust in space where new stars and planets are born. There is a strong correlation between the distribution of dust and molecular gas in molecular clouds. The present work focuses on the…
Galactic dust emission is often accounted for in cosmic microwave background (CMB) analyses by fitting a simple two-parameter modified blackbody (MBB) model in each pixel, which nominally accounts for the temperature and opacity of the…
Machine learning has been successfully applied in varied field but whether it is a viable tool for determining the distance to molecular clouds in the Galaxy is an open question. In the Galaxy, the kinematic distance is commonly employed as…
We report the first uniform and systematic study of dust and molecular gas in nearby molecular clouds. We use surveys of dust extinction and emission to determine the opacity and map the distribution of the dust within a dozen local clouds…
Molecular abundances in protoplanetary disks are highly sensitive to the local physical conditions, including gas temperature, gas density, radiation field, and dust properties. Often multiple factors are intertwined, impacting the…
The far-infrared (FIR) emissivity of dust is an important parameter characterizing the physical properties of the grains. With the availability of stellar databases and far-infrared data from Infrared Space Observatory (ISO) it is possible…
Remote sensing experiments require high-accuracy, preferably sub-percent, line intensities and in response to this need we present computed room temperature line lists for six symmetric isotopologues of carbon dioxide: $^{13}$C$^{16}$O$_2$,…
This paper presents a Deep Learning (DL) framework for 48-hour forecasting of temperature, solar irradiance, and relative humidity to support Model Predictive Control (MPC) in smart HVAC systems. The approach employs a stacked Bidirectional…
Microclimate models are essential for linking climate to ecological processes, yet most physically based frameworks estimate temperature independently for each spatial unit and rely on simplified representations of lateral heat exchange. As…
We study the behavior of dust temperature and its infrared emission of FirstLight1 simulated galaxies at the redshift of 6 and 8, by using POLARIS2 as a Monte Carlo photon transport simulator. To calculate the dust temperature ($T_{dust}$)…
We study the emission of dust grains within the Orion Bar - a well-known, highly far-UV (FUV)-irradiated PDR. The Orion Bar because of its edge-on geometry provides an exceptional benchmark for characterizing dust evolution and the…
Context. Most of the modelling of interstellar dust infrared emission spectrum is done by assuming some variations around a single temperature greybody approximation. For example, the foreground modelling of Planck mission maps involves a…
The temperature is a central parameter affecting the chemical and physical properties of dense cores of interstellar clouds and their evolution to star formation. The chemistry and the dust properties are temperature dependent and the…
Aims: Our aim is to identify the dominant molecular cooling lines and characteristic emission features in the 1.3 mm window of distinct regions in the northern part of the Orion A molecular cloud. By defining and analysing template regions,…
We describe a phenomenological model for molecular hydrogen formation suited for applications in galaxy formation simulations, which includes on-equilibrium formation of molecular hydrogen on dust and approximate treatment of both its…
Single flux density measurements at observed-frame sub-millimeter and millimeter wavelengths are commonly used to probe dust and gas masses in galaxies. In this Letter, we explore the robustness of this method to infer dust mass, focusing…
We introduce machine learning (ML) models that predict the electronic structure of materials across a wide temperature range. Our models employ neural networks and are trained on density functional theory (DFT) data. Unlike most other ML…
We recently reported a population of protostellar candidates in the 20 km s$^{-1}$ cloud in the Central Molecular Zone of the Milky Way, traced by H$_2$O masers in gravitationally bound dense cores. In this paper, we report…