  Galactic-scale structure is of particular interest since it provides
important clues to dark matter properties and its observation is improving.
Weakly interacting massive particles (WIMPs) behave as cold dark matter on
galactic scales, while beyond-WIMP candidates suppress galactic-scale structure
formation. Suppression in the linear matter power spectrum has been
conventionally characterized by a single parameter, the thermal warm dark
matter mass. On the other hand, the shape of suppression depends on the
underlying mechanism. It is necessary to introduce multiple parameters to cover
a wide range of beyond-WIMP models. Once multiple parameters are introduced, it
becomes harder to share results from one side to the other. In this work, we
propose adopting neural network technique to facilitate the communication
between the two sides. To demonstrate how to work out in a concrete manner, we
consider a simplified model of light feebly interacting massive particles.
