Star formation model choice in galaxy simulations controls giant molecular cloud lifetimes, yielding 20-30 Myr with sink particles versus over 200 Myr with the gravo-thermo-turbulent prescription.
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Astromorph adapts the BYOL self-supervised method into a usable pipeline that produces embeddings capturing morphological features in astronomical data, demonstrated on protoplanetary disks and infrared dark clouds.
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From short-lived to long-lived clouds: impact of star formation models on giant molecular cloud evolution in simulations of an NGC 300-like galaxy
Star formation model choice in galaxy simulations controls giant molecular cloud lifetimes, yielding 20-30 Myr with sink particles versus over 200 Myr with the gravo-thermo-turbulent prescription.
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astromorph: Self-supervised machine learning pipeline for astronomical morphology analysis
Astromorph adapts the BYOL self-supervised method into a usable pipeline that produces embeddings capturing morphological features in astronomical data, demonstrated on protoplanetary disks and infrared dark clouds.