Zusammenfassung
We use a generic formalism designed to search for relations in
high-dimensional spaces to determine if the total mass of a subhalo can be
predicted from other internal properties such as velocity dispersion, radius,
or star-formation rate. We train neural networks using data from the Cosmology
and Astrophysics with MachinE Learning Simulations (CAMELS) project and show
that the model can predict the total mass of a subhalo with high accuracy: more
than 99% of the subhalos have a predicted mass within 0.2 dex of their true
value. The networks exhibit surprising extrapolation properties, being able to
accurately predict the total mass of any type of subhalo containing any kind of
galaxy at any redshift from simulations with different cosmologies,
astrophysics models, subgrid physics, volumes, and resolutions, indicating that
the network may have found a universal relation. We then use different methods
to find equations that approximate the relation found by the networks and
derive new analytic expressions that predict the total mass of a subhalo from
its radius, velocity dispersion, and maximum circular velocity. We show that in
some regimes, the analytic expressions are more accurate than the neural
networks. We interpret the relation found by the neural network and
approximated by the analytic equation as being connected to the virial theorem.
Nutzer