  We present a Lagrangian model of galaxy clustering bias in which we train a
neural net using the local properties of the smoothed initial density field to
predict the late-time mass-weighted halo field. By fitting the mass-weighted
halo field in the AbacusSummit simulations at z=0.5, we find that including
three coarsely spaced smoothing scales gives the best recovery of the halo
power spectrum. Adding more smoothing scales may lead to 2-5% underestimation
of the large-scale power and can cause the neural net to overfit. We find that
the fitted halo-to-mass ratio can be well described by two directions in the
original high-dimension feature space. Projecting the original features into
these two principal components and re-training the neural net either reproduces
the original training result, or outperforms it with a better match of the halo
power spectrum. The elements of the principal components are unlikely to be
assigned physical meanings, partly owing to the features being highly
correlated between different smoothing scales. Our work illustrates a potential
need to include multiple smoothing scales when studying galaxy bias, and this
can be done easily with machine-learning methods that can take in high
dimensional input feature space.
