Astrophysics Source Code Library

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Searching for codes credited to 'Offner, Stella'

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[ascl:1905.023] CASI-2D: Convolutional Approach to Shell Identification - 2D

CASI-2D (Convolutional Approach to Shell Identification) identifies stellar feedback signatures using data from magneto-hydrodynamic simulations of turbulent molecular clouds with embedded stellar sources and deep learning techniques. Specifically, a deep neural network is applied to dense regression and segmentation on simulated density and synthetic 12 CO observations to identify shells, sometimes referred to as "bubbles," and other structures of interest in molecular cloud data.

[ascl:1907.015] TurbuStat: Turbulence statistics in spectral-line data cubes

TurbuStat implements a variety of turbulence-based statistics described in the astronomical literature and defines distance metrics for each statistic to quantitatively compare spectral-line data cubes, as well as column density, integrated intensity, or other moment maps. The software can simulate observations of fractional Brownian Motion fields, including 2-D images and optically thin H I data cubes. TurbuStat also offers multicore fast-Fourier-transform support and provides a segmented linear model for fitting lines with a break point.

[ascl:2009.005] CASI-3D: Convolutional Approach to Structure Identification-3D

CASI-3D identifies signatures of stellar feedback in molecular line spectra, such as 12CO and 13CO, using deep learning. The code is developed from CASI-2D (ascl:1905.023) and exploits the full 3D spectral information.