Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Convolutional Autoencoder-Based Pipeline for Anomaly Detection and
  Classification of Periodic Variables   The periodic pulsations of stars teach us about their underlying physical
process. We present a convolutional autoencoder-based pipeline as an automatic
approach to search for out-of-distribution anomalous periodic variables within
The Zwicky Transient Facility Catalog of Periodic Variable Stars (ZTF CPVS). We
use an isolation forest to rank each periodic variable by its anomaly score.
Our overall most anomalous events have a unique physical origin: they are
mostly highly variable and irregular evolved stars. Multiwavelength data
suggest that they are most likely Red Giant or Asymptotic Giant Branch stars
concentrated in the Milky Way galactic disk. Furthermore, we show how the
learned latent features can be used for the classification of periodic
variables through a hierarchical random forest. This novel semi-supervised
approach allows astronomers to identify the most anomalous events within a
given physical class, significantly increasing the potential for scientific
discovery.

18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio