An interpretable machine learning framework for dark matter halo formation. (arXiv:1906.06339v1 [astro-ph.CO])
<a href="http://arxiv.org/find/astro-ph/1/au:+Lucie_Smith_L/0/1/0/all/0/1">Luisa Lucie-Smith</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Peiris_H/0/1/0/all/0/1">Hiranya V. Peiris</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Pontzen_A/0/1/0/all/0/1">Andrew Pontzen</a>
We present a generalization of our recently proposed machine learning
framework, aiming to provide new physical insights into dark matter halo
formation. We investigate the impact of the initial density and tidal shear
fields on the formation of haloes over the mass range $11.4 leq
log(M/M_{odot}) leq 13.4$. The algorithm is trained on an N-body simulation
to infer the final mass of the halo to which each dark matter particle will
later belong. We then quantify the difference in the predictive accuracy
between machine learning models using a metric based on the Kullback-Leibler
divergence. We first train the algorithm with information about the density
contrast in the particles’ local environment. The addition of tidal shear
information does not yield an improved halo collapse model over one based on
density information alone; the difference in their predictive performance is
consistent with the statistical uncertainty of the density-only based model.
This implies that our machine learning setup does not identify any significant
role for the tidal shear in determining halo masses. This result is confirmed
as we verify the ability of the initial conditions-to-halo mass mapping learnt
from one simulation to generalize to independent simulations. Our work
illustrates the broader potential of developing interpretable machine learning
frameworks to gain physical understanding of non-linear large-scale structure
formation.
We present a generalization of our recently proposed machine learning
framework, aiming to provide new physical insights into dark matter halo
formation. We investigate the impact of the initial density and tidal shear
fields on the formation of haloes over the mass range $11.4 leq
log(M/M_{odot}) leq 13.4$. The algorithm is trained on an N-body simulation
to infer the final mass of the halo to which each dark matter particle will
later belong. We then quantify the difference in the predictive accuracy
between machine learning models using a metric based on the Kullback-Leibler
divergence. We first train the algorithm with information about the density
contrast in the particles’ local environment. The addition of tidal shear
information does not yield an improved halo collapse model over one based on
density information alone; the difference in their predictive performance is
consistent with the statistical uncertainty of the density-only based model.
This implies that our machine learning setup does not identify any significant
role for the tidal shear in determining halo masses. This result is confirmed
as we verify the ability of the initial conditions-to-halo mass mapping learnt
from one simulation to generalize to independent simulations. Our work
illustrates the broader potential of developing interpretable machine learning
frameworks to gain physical understanding of non-linear large-scale structure
formation.
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