A CNN trained on MHD simulations estimates the sonic Mach number of interstellar turbulence from intensity, velocity centroid, and velocity channel maps, with median errors near 0.5 to 1.5 in ideal conditions.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
astro-ph.GA 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Estimate Sonic Mach Number in the Interstellar Medium with Convolutional Neural Network
A CNN trained on MHD simulations estimates the sonic Mach number of interstellar turbulence from intensity, velocity centroid, and velocity channel maps, with median errors near 0.5 to 1.5 in ideal conditions.