相关论文: Assessing the Performance of a Machine Learning Al…

We use experiments to study the evolution of bubble clusters in a swarm of freely rising, deformable bubbles. A new machine learning-aided algorithm allows us to identify and track bubbles in clusters and measure the cluster lifetimes. The…

The evolutionary classification of molecular clumps, crucial for understanding star formation, is commonly based on human-assigned categories derived from infrared (IR) emission and well-established morphological criteria. However, due to…

A new Monte-Carlo radiative-transfer code, Sunrise, is used in conjunction with hydrodynamic simulations of major galaxy mergers to calculate the effects of dust in such systems. The simulations are in good agreement with observations of…

The abundance evolution of interstellar dust species originating from stellar sources and from condensation in molecular clouds in the local interstellar medium of the Milky Way is studied and the input of dust material to the Solar System…

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…

We apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time series data. We preprocessed over 94 GB of Kepler light curves from MAST to classify according to ten distinct physical…

Clustered stellar feedback creates expanding voids in the magnetized interstellar medium known as superbubbles. Although theory suggests that superbubble expansion is influenced by interstellar magnetic fields, direct observational data on…

The intricate interplay of atmospheric shock waves and a complex, variable radiation field with non-equilibrium dust formation presents a considerable challenge to self-consistent modelling of atmospheres and winds of AGB stars.…

The structure and evolution of wind-blown bubbles (WBBs) around massive stars has primarily been investigated using an energy-conserving model of wind-blown bubbles. While this model is useful in explaining the general properties of the…

Interstellar dust grains do not have a single well-defined origin. Stars are demonstrably dust producers, but also efficient destroyers of cosmic dust. Dust destruction in the ISM is believed to be the result of SN shocks hitting the…

Context: The environments of young star clusters are shaped by the interactions of the powerful winds of massive stars, and their feedback on the cluster birth cloud. Several such clusters show diffuse gamma-ray emission on the degree…

Massive stars drive the evolution of the interstellar medium through their radiative and mechanical energy input. After their birth, they form bubbles of hot gas surrounded by a dense shell. Traditionally, the formation of bubbles is…

Planet formation in the discs around young stars involves the coagulation of sub-micron sized dust grains into much larger grains that may be mixed by turbulence and migrate through the disc. In this paper, we describe how we have combined…

We present the results of a proof-of-concept experiment which demonstrates that deep learning can successfully be used for production-scale classification of compact star clusters detected in HST UV-optical imaging of nearby spiral galaxies…

The secondary Bjerknes force plays a significant role in the evolution of bubble clusters. However, due to the complex dependence of the force on multiple parameters, it is highly non-trivial to include the effects of this force in the…

Observations of nearby molecular clouds detect "shells", which are likely caused by winds from young main sequence stars. However, the progenitors of these observed features are not well characterized and the mass-loss rates inferred from…

Most gas in giant molecular clouds is relatively low-density and forms star inefficiently, converting only a small fraction of its mass to stars per dynamical time. However, star formation models generally predict the existence of a…

We conducted experiments with machine learning techniques to construct dust temperature maps from the CO isotopologue molecular line data in the Orion A molecular cloud. In the classical astrophysical methodology, multi-band continuum data…

A new Monte-Carlo radiative-transfer code, Sunrise, is used to study the effects of dust in N-body/hydrodynamic simulations of interacting galaxies. Dust has a profound effect on the appearance of the simulated galaxies. At peak…

Aims. We present the implementation of the treatment of particle ejection and dust nucleation in the smoothed particle hydrodynamics (SPH) code phantom. These developments represent the first step toward a more complete modeling of…