Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders   We present an application of anomaly detection techniques based on deep
recurrent autoencoders to the problem of detecting gravitational wave signals
in laser interferometers. Trained on noise data, this class of algorithms could
detect signals using an unsupervised strategy, i.e., without targeting a
specific kind of source. We develop a custom architecture to analyze the data
from two interferometers. We compare the obtained performance to that obtained
with other autoencoder architectures and with a convolutional classifier. The
unsupervised nature of the proposed strategy comes with a cost in terms of
accuracy, when compared to more traditional supervised techniques. On the other
hand, there is a qualitative gain in generalizing the experimental sensitivity
beyond the ensemble of pre-computed signal templates. The recurrent autoencoder
outperforms other autoencoders based on different architectures. The class of
recurrent autoencoders presented in this paper could complement the search
strategy employed for gravitational wave detection and extend the reach of the
ongoing detection campaigns.

31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
16
The categories of this paper are: stat
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph