Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Rapid Identification of Strongly Lensed Gravitational-Wave Events with
  Machine Learning   A small fraction of the gravitational-wave (GW) signals that will be detected
by second and third generation detectors are expected to be strongly lensed by
galaxies and clusters, producing multiple observable copies. While optimal
Bayesian model selection methods are developed to identify lensed signals,
processing tens of thousands (billions) of possible pairs of events detected
with second (third) generation detectors is both computationally intensive and
time consuming. To mitigate this problem, we propose to use machine learning to
rapidly rule out a vast majority of candidate lensed pairs. As a proof of
principle, we simulate non-spinning binary black hole events added to Gaussian
noise, and train the machine on their time-frequency maps (Q-transforms) and
localisation skymaps (using Bayestar), both of which can be generated in
seconds. We show that the trained machine is able to accurately identify lensed
pairs with efficiencies comparable to existing Bayesian methods.

1
The categories of this paper are: cs
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph