Related papers: Assessing the Performance of a Machine Learning Al…
We present Brut, an algorithm to identify bubbles in infrared images of the Galactic midplane. Brut is based on the Random Forest algorithm, and uses bubbles identified by >35,000 citizen scientists from the Milky Way Project to discover…
We propose a deep learning model that can detect Spitzer bubbles accurately using two-wavelength near-infrared data acquired by the Spitzer Space Telescope and JWST. The model is based on the Single Shot MultiBox Detector as an object…
To investigate the nature of starbursts' dust, we constructed a model of the stars and dust in starburst galaxies and applied it to 30 observed starburst spectral energy distributions (SEDs). The starburst model was constructed by combining…
Understanding the three-dimensional motion of bubbles is essential for interpreting transport and mixing in multiphase flows, especially when bubbles deform under shear or move rapidly through the flow field. In many laboratory setups, only…
We present 3-D hydrodynamical models of the evolution of superbubbles powered by stellar winds and supernovae from young coeval massive star clusters within low metallicity ($Z = 0.02$Z$_{\odot}$), clumpy molecular clouds. We explore the…
Molecular clouds are the principle stellar nurseries of our universe, keeping them in the focus of both observational and theoretical studies. From observations, some of the key properties of molecular clouds are well known but many…
Dust storms are associated with certain respiratory illnesses across different areas in the world. Researchers have devoted time and resources to study the elements surrounding dust storm phenomena. This paper reviews the efforts of those…
Dust growth is a crucial step in planet formation, and the efficiency of this process is controlled by the physical and chemical properties of the dust grains. Monte Carlo-based methods are commonly used to follow the collisional evolution…
The Boltzmann equation relates the equilibrium phase space distribution of stars in the Milky Way to the Galaxy's gravitational potential. However, observations of stellar populations are biased by extinction from foreground dust, which…
Mid-infrared arcs of dust emission are often seen near ionizing stars within HII regions. A possible explanations for these arcs is that they could show the outer edges of asymmetric stellar wind bubbles. We use two-dimensional,…
The pervasive interstellar dust grains provide significant insights to understand the formation and evolution of the stars, planetary systems, and the galaxies, and may harbor the building blocks of life. One of the most effective way to…
Aims: We introduce a new deep-learning approach for the reconstruction of 3D dust density and temperature distributions from multi-wavelength dust emission observations on the scale of individual star-forming cloud cores (<0.2pc). Methods:…
Emission from the interstellar medium can be a significant contaminant of measurements of the intensity and polarization of the cosmic microwave background (CMB). For planning CMB observations, and for optimizing foreground-cleaning…
Context. A leading paradigm in planet formation is currently the streaming instability and pebble accretion scenario. For this scenario, dust must grow into sizes in a specific regime of Stokes numbers in order to make these processes…
In Astrophysics, the identification of candidate Globular Clusters through deep, wide-field, single band HST images, is a typical data analytics problem, where methods based on Machine Learning have revealed a high efficiency and…
We adopt the deep learning method CASI (Convolutional Approach to Shell Identification) and extend it to 3D (CASI-3D) to identify signatures of stellar feedback in molecular line spectra, such as 13CO. We adopt magneto-hydrodynamics…
Context. Determining properties of dust formed in and around supernovae from observations remains challenging. This may be due to either incomplete coverage of data in wavelength or time but also due to often inconspicuous signatures of…
Clouds play a key role in regulating climate change but are difficult to simulate within Earth system models (ESMs). Improving the representation of clouds is one of the key tasks towards more robust climate change projections. This study…
Context. Machine-Learning (ML) solves problems by learning patterns from data, with limited or no human guidance. In Astronomy, it is mainly applied to large observational datasets, e.g. for morphological galaxy classification. Aims. We…
The characterization of the spectral energy distribution (SED) of dust emission has become a critical issue in the quest for primordial B-modes. The dust SED is often approximated by a modified black body (MBB) emission law but the extent…