Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Conditional Noise Deep Learning for Parameter Estimation of
  Gravitational Wave Events   We construct a Bayesian inference deep learning machine for parameter
estimation of gravitational wave events of binaries of black hole coalescence.
The structure of our deep Bayesian machine adopts the conditional variational
autoencoder scheme by conditioning on both the gravitational wave strains and
the variations of the amplitude spectral density (ASD) of the detector noise.
We show that our deep Bayesian machine is capable of yielding posteriors
compatible with the ones from the nested sampling method and better than the
one without conditioning on the ASD. Our result implies that the process of
parameter estimation can be accelerated significantly by deep learning even
with large ASD drifting/variation. We also apply our deep Bayesian machine to
the LIGO/Virgo O3 events, the result is compatible with the one by the
traditional Bayesian inference method for the gravitational wave events with
signal-to-noise ratios higher than typical threshold value. We also discuss
some possible ways for future improvement.

18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int