Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Swift sky localization of gravitational waves using deep learning seeded
  importance sampling   Fast, highly accurate, and reliable inference of the sky origin of
gravitational waves would enable real-time multi-messenger astronomy. Current
Bayesian inference methodologies, although highly accurate and reliable, are
slow. Deep learning models have shown themselves to be accurate and extremely
fast for inference tasks on gravitational waves, but their output is inherently
questionable due to the blackbox nature of neural networks. In this work, we
join Bayesian inference and deep learning by applying importance sampling on an
approximate posterior generated by a multi-headed convolutional neural network.
The neural network parametrizes Von Mises-Fisher and Gaussian distributions for
the sky coordinates and two masses for given simulated gravitational wave
injections in the LIGO and Virgo detectors. We generate skymaps for unseen
gravitational-wave events that highly resemble predictions generated using
Bayesian inference in a few minutes. Furthermore, we can detect poor
predictions from the neural network, and quickly flag them.

8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an