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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