相关论文: Assessing the Performance of a Machine Learning Al…
Context: There is increasing need for good algorithms for modeling the aggregation and fragmentation of solid particles (dust grains, dust aggregates, boulders) in various astrophysical settings, including protoplanetary disks, planetary-…
Planet formation is a multi-scale process in which the coagulation of $\mathrm{\mu m}$-sized dust grains in protoplanetary disks is strongly influenced by the hydrodynamic processes on scales of astronomical units ($\approx 1.5\times 10^8…
Massive stars are mainly found in stellar associations. These massive star clusters occur in the heart of giant molecular clouds. The strong stellar wind activity in these objects generates large bubbles and induces collective effects that…
The photometric light curves of BRITE satellites were examined through a machine learning technique to investigate whether there are possible exoplanets moving around nearby bright stars. Focusing on different transit periods, several…
(Abridged) Numerical magnetohydrodynamic (MHD) simulations of a turbulent solar nebula are used to study the growth of dust mantles swept up by chondrules. A small neighborhood of the solar nebula is represented by an orbiting patch of gas…
Micro-bubbles and bubbly flows are widely observed and applied in chemical engineering, medicine, involves deformation, rupture, and collision of bubbles, phase mixture, etc. We study bubble dynamics by setting up two numerical simulation…
Our aim is to present a fast and general Bayesian inference framework based on the synergy between machine learning techniques and standard sampling methods and apply it to infer the physical properties of clumpy dusty torus using infrared…
In dense molecular clouds collisions between dust grains alter the ISM-dust size distribution. We study this process by inserting the results from detailed numerical simulations of two colliding dust aggregates into a coagulation model that…
Models describing dust-driven winds are important for understanding the physical mechanism and properties of mass loss on the asymptotic giant branch. These models are becoming increasingly realistic with more detailed physics included, but…
This paper presents BubbleID, a sophisticated deep learning architecture designed to comprehensively identify both static and dynamic attributes of bubbles within sequences of boiling images. By amalgamating segmentation powered by Mask…
We use a sample of 532 star-forming galaxies at redshifts $z\sim 1.4-2.6$ with deep rest-frame optical spectra from the MOSFIRE Deep Evolution Field (MOSDEF) survey to place the first constraints on the nebular attenuation curve at high…
It is counter-intuitive that multi-modality methods based on point cloud and images perform only marginally better or sometimes worse than approaches that solely use point cloud. This paper investigates the reason behind this phenomenon.…
Stellar bow shocks, bow waves, and dust waves all result from the action of a star's wind and radiation pressure on a stream of dusty plasma that flows past it. The dust in these bows emits prominently at mid-infrared wavelengths in the…
Key features of the mechanical response of amorphous particulate materials, such as foams, emulsions, and granular media, to applied stress are determined by the frequency and size of particle rearrangements that occur as the system…
The growing evidence pointing at core-collapse supernovae as large dust producers makes young massive stellar clusters ideal laboratories to study the evolution of dust immersed into a hot plasma. Here we address the stochastic injection of…
Planet formation models rely on knowledge of the physical conditions and evolutionary processes in protoplanetary disks, in particular the grain size distribution and dust growth timescales. In theoretical models, several barriers exist…
Interstellar dust is a major foreground contaminant for many observations and a key component in the chemistry of the interstellar medium, yet its properties remain highly uncertain. Using low-resolution spectra, we accurately measure the…
We present the results of a machine learning study to measure the dust content of galaxies observed with JWST at z > 6 through the use of trained neural networks based on high-resolution IllustrisTNG simulations. Dust is an important…
The growing interest in the high-z universe, where strongly obscured objects are present, has determined an effort to improve the simulations of dust formation and evolution in galaxies. Three main basic ingredients enter the problem…
Making mock simulated catalogs is an important component of astrophysical data analysis. Selection criteria for observed astronomical objects are often too complicated to be derived from first principles. However the existence of an…