A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters
Abstract
We demonstrate the ability of convolutional neural networks (CNNs) to mitigate systematics in the virial scaling relation and produce dynamical mass estimates of galaxy clusters with remarkably low bias and scatter. We present two models, CNN_{1D} and CNN_{2D}, which leverage this deep learning tool to infer cluster masses from distributions of member galaxy dynamics. Our first model, CNN_{1D}, infers cluster mass directly from the distribution of member galaxy lineofsight velocities. Our second model, CNN_{2D}, extends the input space of CNN_{1D} to learn on the joint distribution of galaxy lineofsight velocities and projected radial distances. We train each model as a regression over cluster mass using a labeled catalog of realistic mock cluster observations generated from the MultiDark simulation and UniverseMachine catalog. We then evaluate the performance of each model on an independent set of mock observations selected from the same simulated catalog. The CNN models produce cluster mass predictions with lognormal residuals of scatter as low as 0.132 dex, greater than a factor of 2 improvement over the classical M─σ powerlaw estimator. Furthermore, the CNN model reduces prediction scatter relative to similar machinelearning approaches by up to 17% while executing in drastically shorter training and evaluation times (by a factor of 30) and producing considerably more robust mass predictions (improving prediction stability under variations in galaxy sampling rate by 30%).
 Publication:

The Astrophysical Journal
 Pub Date:
 December 2019
 DOI:
 10.3847/15384357/ab4f82
 arXiv:
 arXiv:1902.05950
 Bibcode:
 2019ApJ...887...25H
 Keywords:

 cosmology: theory;
 galaxies: clusters: general;
 galaxies: kinematics and dynamics;
 methods: statistical;
 Astrophysics  Cosmology and Nongalactic Astrophysics
 EPrint:
 21 pages, 10 figures, 3 tables, submitted to ApJ