Related papers: Segmenting Superbubbles in a Simulated Multiphase …
We review recent advances in the numerical modeling of turbulent flows and star formation. An overview of the most widely used simulation codes and their core capabilities is provided. We then examine methods for achieving the…
We propose a novel superpixel-based multi-view convolutional neural network for semantic image segmentation. The proposed network produces a high quality segmentation of a single image by leveraging information from additional views of the…
The identification and tracking of stellar feedback-driven galaxy bubbles is an important topic in star formation and galactic structure research. However, current observational analysis of bubbles is limited in scope; information on bubble…
MHD Turbulence is a critical component of the current paradigms of star formation, particle transport, magnetic reconnection and evolution of the ISM. Progress on this difficult subject is made via numerical simulations and observational…
Hyperspectral image analysis has become an important topic widely researched by the remote sensing community. Classification and segmentation of such imagery help understand the underlying materials within a scanned scene, since…
We investigate the role of supernova (SN)-driven galactic winds in the chemical enrichment of the intracluster medium (ICM). Such outflows on galactic scales have their origin in huge star forming regions and expel metal enriched material…
Modelling the propagation of supernova (SN) bubbles, in terms of energy, momentum and spatial extent, is critical for simulations of galaxy evolution which do not capture these scales. To date, small scale models of SN feedback predict that…
We present a model of a pulsar-driven supernova remnant, by using a hydrodynamics code, which simulates the evolution of a pulsar wind nebula when the pulsar is moving at a high velocity through its expanding supernova remnant. The…
Molecular clouds (MC) are structures of dense gas in the interstellar medium (ISM), that extend from ten to a few hundred parsecs and form the main gas reservoir available for star formation. Hydrodynamical simulations of varying complexity…
This methodological paper is part of a short series dedicated to the long-standing astronomical problem of de-projecting the bi-dimensional, apparent morphology of a three-dimensional distribution of gas. We focus on the quantification and…
The expansion and collision of two wind-blown superbubbles is investigated numerically. Our models go beyond previous simulations of molecular cloud formation from converging gas flows by exploring this process with realistic flow…
We use a data-driven approach to model a three-dimensional turbulent flow using cutting-edge Deep Learning techniques. The deep learning framework incorporates physical constraints on the flow, such as preserving incompressibility and…
The morphology, dynamics and thermal X-ray emission of the superbubble N70 is studied by means of 3D hydrodynamical simulations, carried out with the {\sc{yguaz\'u-a}} code. We have considered different scenarios: the superbubble being the…
In this exploratory submission we present the visualization of the largest interstellar turbulence simulations ever performed, unravelling key astrophysical processes concerning the formation of stars and the relative role of magnetic…
Computer vision and machine learning tools offer an exciting new way for automatically analyzing and categorizing information from complex computer simulations. Here we design an ensemble machine learning framework that can independently…
We carry out three-dimensional hydrodynamic simulations of the supernova remnants (SNRs) produced inside molecular clouds (MCs) near their surface using the HLL code (Harten et al. 1983). We explore the dynamical evolution and the X-ray…
Thanks to ultra fast and high resolution X-ray tomography, we managed to capture the evolution of the local structure of the bubble network of a 3D foam flowing around a sphere. As for the 2D foam flow around a circular obstacle, we…
We review recent advances in the analytical and numerical modeling of the star formation rate in molecular clouds and discuss the available observational constraints. We focus on molecular clouds as the fundamental star formation sites,…
Semantic segmentation is an important and popular research area in computer vision that focuses on classifying pixels in an image based on their semantics. However, supervised deep learning requires large amounts of data to train models and…
We present the first to date three-dimensional high-resolution hydrodynamical simulation tracing the non-equilibrium ionization evolution (using the Eborae Atomic and Molecular Plasma Emission Code - E(A+M)PEC) of the Local Bubble and Loop…