Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates. (arXiv:1908.08045v1 [astro-ph.IM])

<a href="http://arxiv.org/find/astro-ph/1/au:+Cranmer_M/0/1/0/all/0/1">Miles D. Cranmer</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Galvez_R/0/1/0/all/0/1">Richard Galvez</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Anderson_L/0/1/0/all/0/1">Lauren Anderson</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Spergel_D/0/1/0/all/0/1">David N. Spergel</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Ho_S/0/1/0/all/0/1">Shirley Ho</a>

We demonstrate an algorithm for learning a flexible color-magnitude diagram

from noisy parallax and photometry measurements using a normalizing flow, a

deep neural network capable of learning an arbitrary multi-dimensional

probability distribution. We present a catalog of 640M photometric distance

posteriors to nearby stars derived from this data-driven model using Gaia DR2

photometry and parallaxes. Dust estimation and dereddening is done iteratively

inside the model and without prior distance information, using the Bayestar

map. The signal-to-noise (precision) of distance measurements improves on

average by more than 48% over the raw Gaia data, and we also demonstrate how

the accuracy of distances have improved over other models, especially in the

noisy-parallax regime. Applications are discussed, including significantly

improved Milky Way disk separation and substructure detection. We conclude with

a discussion of future work, which exploits the normalizing flow architecture

to allow us to exactly marginalize over missing photometry, enabling the

inclusion of many surveys without losing coverage.

We demonstrate an algorithm for learning a flexible color-magnitude diagram

from noisy parallax and photometry measurements using a normalizing flow, a

deep neural network capable of learning an arbitrary multi-dimensional

probability distribution. We present a catalog of 640M photometric distance

posteriors to nearby stars derived from this data-driven model using Gaia DR2

photometry and parallaxes. Dust estimation and dereddening is done iteratively

inside the model and without prior distance information, using the Bayestar

map. The signal-to-noise (precision) of distance measurements improves on

average by more than 48% over the raw Gaia data, and we also demonstrate how

the accuracy of distances have improved over other models, especially in the

noisy-parallax regime. Applications are discussed, including significantly

improved Milky Way disk separation and substructure detection. We conclude with

a discussion of future work, which exploits the normalizing flow architecture

to allow us to exactly marginalize over missing photometry, enabling the

inclusion of many surveys without losing coverage.

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