Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Core-Collapse Supernova Gravitational-Wave Search and Deep Learning
  Classification   We describe a search and classification procedure for gravitational waves
emitted by core-collapse supernova (CCSN) explosions, using a convolutional
neural network (CNN) combined with an event trigger generator known as Wavelet
Detection Filter (WDF). We employ both a 1-D CNN search using time series
gravitational-wave data as input, and a 2-D CNN search with time-frequency
representation of the data as input. To test the accuracies of our 1-D and 2-D
CNN classification, we add CCSN waveforms from the most recent hydrodynamical
simulations of neutrino-driven core-collapse to simulated Gaussian colored
noise with the Virgo interferometer and the planned Einstein Telescope
sensitivity curve. We find classification accuracies, for a single detector, of
over 95% for both 1-D and 2-D CNN pipelines. For the first time in machine
learning CCSN studies, we add short duration detector noise transients to our
data to test the robustness of our method against false alarms created by
detector noise artifacts. Further to this, we show that the CNN can distinguish
between different types of CCSN waveform models.

22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin