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Found 6 papers, 3 papers with code
Exoplanet Characterization using Conditional Invertible Neural Networks
Indeed, using cINNs allows for orders of magnitude faster inference of an exoplanet's composition than what is possible using an MCMC method, however, it still requires the computation of a large database of internal structures to train the cINN.
PHANGS-HST: Star Cluster Spectral Energy Distribution Fitting with CIGALE
We report on the use of the publicly available Code Investigating GALaxy Emission (CIGALE) SED fitting package to derive ages, stellar masses, and reddenings for star clusters identified in the Physics at High Angular resolution in Nearby GalaxieS-HST (PHANGS-HST) survey.
Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
Do fragmentation and accretion affect the stellar Initial Mass Function?
The fragmentation behavior depends mostly on the Jeans mass at the turning point in the equation of state where a transition occurs from an approximately isothermal to an adiabatic regime due to dust opacities.
Astrophysics of Galaxies Solar and Stellar Astrophysics
Shape and Spin of Minihaloes: From Large Scales to the Centres
On the scale of the virial radius, there is a good correlation between the shape and spin of the gas and that of the dark matter.
Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics
Models of rotating coronae
However, the two main categories of models that are used in the literature are poorly suited for this task: (i) simple barotropic models are analytic and can therefore be adjusted to match the observations, but are clearly unrealistic because the rotational velocity $v_\phi(R, z)$ does not depend on the distance $z$ from the galactic plane, while (ii) models obtained as a result of cosmological galaxy formation simulations are more realistic, but are impractical to fit to observations due to high computational cost.
Astrophysics of Galaxies
Analyzing Inverse Problems with Invertible Neural Networks
Often, the forward process from parameter- to measurement-space is a well-defined function, whereas the inverse problem is ambiguous: one measurement may map to multiple different sets of parameters.
