Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using a nested anomaly detection machine learning algorithm to study the
  neutral triple gauge couplings at an \texorpdfstring{$e^+e^-$}{e+e-} collider   Anomaly detection algorithms have been proved to be useful in the search of
new physics beyond the Standard Model. However, a prerequisite for using an
anomaly detection algorithm is that the signal to be sought is indeed
anomalous. This does not always hold true, for example when interference
between new physics and the Standard Model becomes important. In this case, the
search of new physics is no longer an anomaly detection. To overcome this
difficulty, we propose a nested anomaly detection algorithm, which appears to
be useful in the study of neutral triple gauge couplings at the CEPC, the ILC
and the FCC-ee. Our approach inherits the advantages of the anomaly detection
algorithm been nested, while at the same time, it is no longer an anomaly
detection algorithm. As a complement to anomaly detection algorithms, it can
achieve better results on problems that are no longer anomaly detection.

16
The categories of this paper are: stat
1
The categories of this paper are: cs
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org