Dynamical mass inference of galaxy clusters with neural flows. (arXiv:2003.05951v1 [astro-ph.CO])
<a href="http://arxiv.org/find/astro-ph/1/au:+Ramanah_D/0/1/0/all/0/1">Doogesh Kodi Ramanah</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Wojtak_R/0/1/0/all/0/1">Radosław Wojtak</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Ansari_Z/0/1/0/all/0/1">Zoe Ansari</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Gall_C/0/1/0/all/0/1">Christa Gall</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Hjorth_J/0/1/0/all/0/1">Jens Hjorth</a>
We present an algorithm for inferring the dynamical mass of galaxy clusters
directly from their respective phase-space distributions, i.e. the observed
line-of-sight velocities and projected distances of galaxies from the cluster
centre. Our method employs neural flows, a deep neural network capable of
learning arbitrary high-dimensional probability distributions, and inherently
accounts, to an adequate extent, for the presence of interloper galaxies which
are not bounded to a given cluster, the primary contaminant of dynamical mass
measurements. We validate and showcase the performance of our neural flow
approach to robustly infer the dynamical mass of clusters from a realistic mock
cluster catalogue. A key aspect of our novel algorithm is that it yields the
probability density function of the mass of a particular cluster, thereby
providing a principled way of quantifying uncertainties, in contrast to
conventional machine learning approaches. The neural network mass predictions,
when applied to a contaminated catalogue with interlopers, have a logarithmic
residual scatter which goes down to 0.043 dex for the most massive clusters.
This is nearly an order of magnitude improvement over the classical cluster
mass scaling relation with the velocity dispersion, and outperforms recently
proposed machine learning approaches. We also apply our neural flow mass
estimator to a compilation of galaxy observations of some well-studied clusters
with robust dynamical mass estimates, further substantiating the efficacy of
our algorithm. Such sophisticated approaches would undoubtedly be relevant for
robust and efficient dynamical mass inference from upcoming surveys covering
unprecedented volumes of the sky.
We present an algorithm for inferring the dynamical mass of galaxy clusters
directly from their respective phase-space distributions, i.e. the observed
line-of-sight velocities and projected distances of galaxies from the cluster
centre. Our method employs neural flows, a deep neural network capable of
learning arbitrary high-dimensional probability distributions, and inherently
accounts, to an adequate extent, for the presence of interloper galaxies which
are not bounded to a given cluster, the primary contaminant of dynamical mass
measurements. We validate and showcase the performance of our neural flow
approach to robustly infer the dynamical mass of clusters from a realistic mock
cluster catalogue. A key aspect of our novel algorithm is that it yields the
probability density function of the mass of a particular cluster, thereby
providing a principled way of quantifying uncertainties, in contrast to
conventional machine learning approaches. The neural network mass predictions,
when applied to a contaminated catalogue with interlopers, have a logarithmic
residual scatter which goes down to 0.043 dex for the most massive clusters.
This is nearly an order of magnitude improvement over the classical cluster
mass scaling relation with the velocity dispersion, and outperforms recently
proposed machine learning approaches. We also apply our neural flow mass
estimator to a compilation of galaxy observations of some well-studied clusters
with robust dynamical mass estimates, further substantiating the efficacy of
our algorithm. Such sophisticated approaches would undoubtedly be relevant for
robust and efficient dynamical mass inference from upcoming surveys covering
unprecedented volumes of the sky.
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